Crashworthiness optimisation and environmental impact assessment of a redesigned passenger coach integrating lean design-for-X framework

The transportation industry focuses on reducing vehicle weight for fuel economy and emissions. This emphasis promotes the use of coaches, which raises concerns about passenger safety in frontal collisions. The proposal is to correlate the crashworthiness of coaches with the replacement of fibreglass composite materials by a state-of-the-art polymer (DCPD). Based on the ECE R29 standard, FEM models solved by Pamcrash® assess the vehicle's crashworthiness. Cross-referencing these results with the Eco-Design X technique, two models are evaluated in terms of environmental impact. The LeanDfX methodology involves multiple analyses for design domains, including model optimisation, manufacturing processes, and eco-design. On a performance scale of 0% to 100%, different ‘X’ domains are evaluated. The Eco-Design study allowed the assessing the environmental impacts of the proposed solution compared to the original models, is conducted using Simapro v9.2.0.2 and the ReCiPe 2016 methodology. The novel design proposed modifications to the models resulted in significant structural behaviour improvements for driver's physical integrity. The cross results of Design-for-Crashworthiness and Design-for-Eco-Design using the innovative LeanDfX framework provide a new perspective to be integrated into the automotive industry. The use of DCPD is expected to lead to a more crashworthy and environmentally friendly design, while ensuring passenger safety.


Introduction
The transportation industry is a sector in constant technological evolution, only manufacturers keeping pace with these advancements can sustain themselves in the market (Lopes et al. 2021).Presently, the focus is on reducing vehicle weight as a crucial parameter for fuel economy and the mitigation of harmful emissions.Pollution levels show an increasing trend, exacerbated by the uncontrolled rise in the number of automobiles.Consequently, this industry is under pressure to optimise pollution levels by reducing them and raising awareness among transportation users, particularly in favour of public transport, notably buses/coaches.This scenario introduces a new imperative: ensuring the physical integrity of passengers in the event of a frontal collision.Vehicles must be equipped to prevent harm to passengers and drivers in such circumstances.Passive safety measures in buses/coaches still remain somewhat inadequate, demanding a concentrated effort to address this issue.This article establishes a correlation between the crashworthiness of tour buses and the optimisation of materials integrated into the vehicle.The goal is to incorporate innovative materials that do not compromise the crashworthiness of the vehicle.Besides that, it was developed the denominated 'Automotive Eco-Safe by LeanDesignX' technique based on the LeanDfX (Lean Design-for-eXcellence) framework, the FEM (Finite element method) simulations and LCA detailed study for the use-case tested.Two models are evaluated: the original and the optimised, regarding environmental impact and crashworthiness.
The coach industry faces a shortage of significant incentives to improve its passive safety features, especially in the context of frontal impacts.Existing literature, including recent contributions by Lopes et al. (2023), underscores the inadequacy of passive safety measures.Consequently, there is a need to optimise vehicles for safe operation.However, these optimisations must be executed without compromising performance in terms of environmental impact.This work stands out for its innovation, addressing two distinct but critical topics not yet covered in the existing literature.
The Lean Design-for-eXcellence framework (LeanDfX) was developed at INEGI (Institute of Science and Innovation in Mechanical and Industrial Engineering) as a comprehensive and multidimensional approach for assessing the performance of product or system designs (Atilano et al. 2019;Baptista, Lourenço, et al. 2018;Baptista, Peixoto, et al. 2018).The LeanDfX framework encompasses multiple design domains or 'X dimensions' integrated performance assessment, strongly based on life cycle engineering foundations, modular design principles for functions orientation in product complexity management, and Lean Product Development basis to identify product inefficiencies (wastes or over-engineering scenarios) besides the DfX effectiveness assessment for design goals.Along the years, different projects have been done to explore and complement the X-domain knowledge basis of LeanDfX Scorecards and KPIs examples (Atilano et al. 2019;Baptista, Lourenço, et al. 2018;Baptista, Peixoto, et al. 2018).For instance, domains such as design for manufacturing, maintenance, modularity, environment and structural optimisation, were studied and support LeanDfX validation in real-world project cases.LeanDfX framework is based on a systematic approach with Lean Principles and includes a tool for design engineers to analyse the effectiveness of product technical objectives listed on requirements and specifications, as well as the efficiency of the product by identifying potential over-engineering hotspots (Carneiro et al. 2022).As a result, the LeanDfX framework provides a holistic approach for assessing product performance in terms of both effectiveness and efficiency.Due to the novelty of LeanDfX, but also as a literature gap, the cross-link of Design-for-Crashworthiness and Design-for-Environment was not properly addressed, as far as the authors acknowledge, in a integrated way, where both design effectiveness and design efficiency were explicitly evaluated.This paper outlines the steps taken for the design of a coach body module, specifically the front part of the coach which includes the complete front chassis and cab, using innovative composite materials, taking the here presented Automotive Eco-Safe by LeanDesignX methodology as a guiding tool.
Traditional construction technologies for coach exterior components often involve stamped steel or natural fibreglass-reinforced resin lamination (Pravilonis and Sokolovskij 2021).However, the Reaction Injection Moulding (RIM) method, which has long been associated with polyurethane injection, has emerged as an unconventional option.New polymeric materials, such as dicyclopentadiene (DCPD), have been introduced in recent years (Camboa et al. 2013;Yang, Lafontaine, and Mortaigne 1997).DCPD is a self-releasing polymer that does not require a release agent, resulting in a high-quality Class A surface finish, which is a requirement in the automotive industry (Camboa 2016).Steel and aluminium are widely used in the automotive industry's most demanding constructions (Pravilonis and Sokolovskij 2020;Saini et al. 2022).The main objective of this study is to develop innovative modular passive safety solutions for coaches, with the aim of ensuring the safety of both the coach driver and passengers in the event of a frontal collision.With the emergence of new urban mobility paradigms, there are new technological challenges that require innovative solutions to improve weight reduction and passive and active safety, particularly through the use of novel materials with favourable structural properties (Blanco 2010;Chen, Cheng, and Fu 2020;Wan 2011).Therefore, careful selection of materials and manufacturing techniques is essential to enhance structural performance while minimising environmental impact.In this project, two structures, an original one and an optimised one, were numerically tested according to the ECE R29 standard to assess the feasibility of modifying the outer panels and strengthening certain areas of the vehicle's frontal region.The numerical study allowed for the evaluation of the structural performance of the coach in frontal impacts, considering its current manufacturing approach and daily use.The objective was to determine if the structural improvements, in addition to the environmental benefits, justified the replacement of the panels.To validate the structural behaviour of the buses, the deformations, displacements, and absorbed energy were measured and compared between the two configurations.The Numerical analyses were conducted using PamCrash R , a specialised software based on the Finite Element Method.The evaluation of the structural safety aimed to determine if the structural improvements would provide sufficient residual space for the driver to survive during and after the frontal crash test on the complete vehicle.
Together with the novel coach front structure redesign to attend more strict crashworthiness requirements, the study compares also the environmental impact of the creation of a new exterior frontal panel that integrates RIM-DCPD production technology with structural reinforcement components (optimised model) to exterior panels made of fibreglass reinforced polyester (original model).In addition to this change in comparison to the original model, this new solution also endured alterations in the thickness of several components, resulting in a change in its mass.The environmental study in this manuscript adheres to the LCA concepts, as specified by ISO 14040:1997(International Organization for Standardization 1997) and ISO 14044:2006(International Organization for Standardization 2006).The LCA methodology is carried out by identifying and quantifying the material inputs and outputs within the pre-defined system boundary, allowing quantification of the potential environmental impacts resulting from this system, from raw material extraction to processing, use, and end of life.

Concise literature
The continuous growth in the number of vehicles on the roads demands ongoing development in the automotive sector (Lopes et al. 2021).Consequently, statistical data analysis unequivocally shows an increase in accident rates (Porcu et al. 2020).Despite industrial advancements, particularly in structural optimisation, that have contributed to mitigating this issue (European Commission 2020a), numerous studies on road safety are available in the literature.Vehicle safety is considered a critical component in vehicle design by most manufacturers, irrespective of vehicle category or efficiency (UNECE 2015).
The present study aims to assess the safety of heavy vehicles, as this subject has received limited attention due to their relatively lower incidence of accidents (Cafiso, Di Graziano, and Pappalardo 2013).However, when accidents involving heavy vehicles do occur, they can result in severe consequences such as fatalities or injuries (European Commission 2020b).In the European Union, accidents involving heavy vehicles accounted for 2.5% of total road-related deaths in 2018, a percentage that has remained stable since 2010.In the United States, an estimated 55,000 accidents involving heavy vehicles occur every year (European Commission 2020a).
The UNECE (Commission for the Union of European Economies) has prioritised this issue with the goal of reducing road-related mortality by 50% by 2020 through bodywork improvements and technological solutions, to minimise fatalities (UNECE 2015).Matolcsy (2016) categorised bus accidents as frontal collisions or combined impacts, with frontal collisions being the most common cause (Jongpradist, Senawat, and Muangto 2015).The high number of individuals involved in bus accidents, with potential to injure up to 50 people per incident, is a significant concern (Matolcsy 2016).Collisions involving buses often result in substantial penetration of components into the passenger compartments, endangering the occupants' physical integrity (Kumar 2012).Furthermore, the deformation of vehicle structures and materials, as well as the driver's deceleration during impact, are key factors that determine the severity of injuries to the neck, femur, and thorax (Hermann and Olivares 2005;Lopes et al. 2022;Olivares 2008;Rivero-Urzúa, Torres-San-Miguel, and Martínez-Sáenz 2019).The status of vehicle deformation directly affects driver safety, with structural incursion into the passenger compartment and decelerations being crucial criteria (Matsumoto, Drimeier, and Alves 2012).Therefore, it is imperative to incorporate in bus design, innovative technologies that can absorb the crash energy in a controlled manner (Abramowicz 2003;Jongpradist, Senawat, and Muangto 2015).Currently, buses lack the structural capacity to fully absorb the impact energy, resulting in significant intrusion into the driver's cabin (Jongpradist, Senawat, and Muangto 2015).
Numerous studies in the literature have been conducted with the aim of mitigating the problem by identifying low resistance zones and optimising geometry (Morocho et al. 2022;Youming, Yongpan, and Hongwu 2013).For example, Jongpradist, Senawat, and Muangto (2015) presented optimised models that adhere to the ECE R-29 standard, highlighting the importance of incorporating crushing zones in vehicles to effectively manage energy absorption.Other researchers, such as Rooppakhun and Bua-Ngam (2013) and Cerit et al. (2010), have proposed the use of attenuators for controlling variable geometry.Additionally, Güler et al. (2020) conducted experimental investigations on coaches according to the standards, and concluded that strategies to prevent passenger injuries are necessary.These studies demonstrate the growing body of literature that focuses on the requirement for geometry optimisation and energy absorption strategies in addressing crashworthiness in coach design.Further research in this area is warranted to continue improving the safety of heavy vehicles and reducing the occurrence and severity of accidents, (Ali, Hussain, and Haque 2024).
Product strategy involves complex decision-making that significantly impacts the design and development of new products (McCarthy et al. 2006).Each product needs to comply with regulatory and legal standards, pricing policies, sustainability considerations, timeto-market requirements, customisation needs, and other factors, depending on the target market (Porter and van der Linde 2000).These variables contribute to the increased complexity and risks associated with new product creation, requiring organisations to employ strategies and procedures that align their resources and capabilities towards success.
Design-for-X (DfX), also known as Design-for-Excellence, is a holistic kind of methodology used in the initial design phase of new product development, which focuses on optimising resources, improving processes, and resulting in cost savings, enhanced product quality, and waste reduction, including raw materials, time, redesign, and design iterations (Ram and Kailas 2019;Reddy, Reddy, and Kumar 2023).DfX methodologies encompass all the constraints and objectives in the early stages of product development, with 'X' representing various domains closely related to the product, including technical disciplines such as Design for Manufacturing (DfM) studied in Edwards's work (2002), Design for Assembly (DfA) in Miles and Swift' work (1998), Design for Sustainability (DfS) in the work of Spangenberg, Fuad-Luke, and Blincoe (2010), Design for Cost (DfC) in Chen et al. (2004), or Design for Crashworthiness (DfCrash) employed in Hamza and Saitu' work (2004), among others.
In the DfX methodology, for each domain X, there are design guidelines that aim to address and resolve various challenges encountered during the product development phase.These guidelines are supported by methodologies and methods that facilitate the generation and application of technical knowledge, thereby contributing to the enhancement, control, and innovation of specific aspects of a product (Atilano et al. 2019).When designing new models of passenger transport vehicles, such as buses, several X domains should be taken into consideration.Design for Manufacturing (DfM), Design for Assembly (DfA), Design for Crashworthiness (DfCrash), Design for Sustainability (DfS), and Design for Cost (DfC) are different approaches used in product design and development.DfM aims to reduce costs and simplify manufacturing processes, while DfA focuses on simplifying component assembly.DfCrash enhances the impact behaviour of structures, (Hardman et al. 2023), to comply with safety regulations.DfS incorporates sustainability metrics into product design.DfC evaluates and lowers the product's life cycle costs.All of these approaches are part of the holistic DfX methodology, with the initial design phase having the most significant impact on product cost, making it crucial for decision making and exploring design improvements to minimise life cycle costs (Ram and Kailas 2019;Xiaochuan et al. 2004).
As indicated by the presented literature, it is evident that the findings align with the assumptions outlined in the preceding section.Concerning the safety of coach collisions, one can observe the validity of the notion that there is still a considerable way to cover in structurally optimising coaches for better resistance to frontal impacts.Numerous researchers have delved into the researching of frontal collisions involving coaches and buses, reaching the conclusion that the everyday structures are ineffective in withstanding and safeguarding occupants.The UNECE has addressed this matter, acknowledged the significance of this endeavour by setting objectives.
Moreover, the process of optimisation entails substituting and/or adding components to the structure and assess the inter-dependencies between the design variables, namely regarding of the DfCrash domain.In this project, this assessment is cross-evaluated by cooperatively integrated with the LCA methodology used to evaluate environmental impacts (Design-for-Environment), allows for the development of optimised structures with a specific focus, notably emphasising DfCrash and Design-for-Environment.These structures not only improve structural impacts capabilities but also ensure their efficacy without compromising environmental considerations.

ECE-R29 regulation
Manufacturers adhere to the ECE-29 regulation (ECE-R29 2010) to assess and validate the safety of vehicles in the event of a frontal collision.The ECE-R29 standard specifically applies to heavy vehicles with a separate cab from the rest of the vehicle in case of a frontal collision (Cerit et al. 2010).However, for touring coaches, there are currently no standards that specifically address frontal impact tests (Cerit et al. 2010;ECE-R29 2010).The ECE-R29 regulation comprises of three different tests: Test A (front impact test), Test B (front and rear impact test), and Test C (roof strength test) (ECE-R29 2010;Raich and DaimlerChrysler 2003).For this project, the focus will be on Test A, which evaluates the performance of the vehicle in a frontal impact scenario.
The aim of this approach is to conduct a comprehensive analysis of the behaviour of the optimised coach in comparison to the original structure.By analysing the performance of the two structures, it will be possible to determine if upgrading the coach with a new structural solution will yield improved results in terms of the vehicle's structural integrity and environmental impact in the event of a frontal collision.This research endeavour will enable manufacturers to offer innovative products, giving them a competitive edge in the market.Additionally, it is noteworthy that the use of tourist models as a baseline for this study serves as a motivation due to the higher velocities typically achieved by such vehicles.
The frontal impact test involves the use of an impactor with a rectangular shape, measuring 2500 × 800 mm in projected dimensions.The weight of the impactor must be at least 1500 kg, and its edges should be rounded with a radius of 10mm ± 5mm.For assembly, the impactor must be supported by two bars that are 1000 mm apart and have a length greater than 3500 mm.As depicted in Figure 1(a), the impactor must be positioned upright, with its centre of gravity (c) located c = 50 + 5/-0 mm below the R point of the driver's seat.The pendulum is then released, and it strikes the front end of the vehicle.For vehicles categorised as N3 or N2, weighing over 7.5 tonnes, the striking kinetic energy must be 55 kJ.After the test, a dummy with dimensions specified by the standard should be able to fit within the survival area.The dummy should not make contact with non-resilient components that have a Shore hardness of 50 or higher when the seat is in the intermediate position (ECE-R29 2010).

Designing for crashworthiness
Design-for-Vehicle crashworthiness can be examined from three distinct perspectives (Belingardi and Chiandussi 2011): • Passive safety encompasses all automotive components designed to disperse energy and protect passengers during an incident without the need for driver intervention.This includes seat belts and airbags, which do not require activation by the driver (Barik and Nayak 2018).• Active safety includes systems that assist the driver in avoiding collisions, such as ABS.
They are termed active because they require driver input to respond before a collision occurs (Yiğit, Köylü, and Eken 2023).• Preventive safety encompasses technologies intended to assist the driver in avoiding risky situations and facilitating safe vehicle operation, such as obstacle recognition systems and safety warnings.
The Design-for-Crashworthiness related to the passive safety of passenger transport vehicles, specifically coaches, will be addressed within the scope of this document.This includes the design of superstructures capable of plastically deforming on impact to enhance energy dissipation without compromising the residual space of the vehicle with the intrusion/penetration of other vehicle parts, such as safety bars and uprights.To achieve this, the construction of the vehicle must include: • Progressive 'Crush zones' designed to absorb some of the kinetic energy released on impact and manage the failure mode (Alam et al. 2023).• Structures/supports/side safety bars to maintain structural integrity.
According to the authors (Ambrósio, Pereira, and da Silva 2012), analyses and inspections of bus structures following collisions reveal that the deformations of the bus structure are not equally distributed.There are entirely distorted and undeformed structural parts, as well as elements with severe plastic deformations, located in specific regions, which highlight certain Design-for-Crashworthiness factors in front and rear scenarios: • At low impact speeds, the bumper is designed to bend elastically, while at high impact speeds, the front bumper of the bus must undergo complete plastic deformation, attenuating rapid deceleration (dampening effect).• Seats can be positioned higher above the impact threshold to protect passengers in case of a rear crash, with a hard framework below this level providing protection.• The use of a robust safety platform at the base of the driver's seat, connecting the steering wheel, seat, and dashboard support component.The safety platform is attached to the bus frame by plastic joints that bend plastically after impact, allowing the driver's platform to recoil (see Figure 2).
Following that, various key performance indicators (KPI's) will be offered, allowing the performance of systems responsible for energy dissipation to be assessed throughout the development phase: Stroke Efficiency (SE) or Effective stroke ratio (ESR) -A dimensionless measure of the quantity of material used (deformed) during impact.As a result, it is an indicator of the efficient distribution of the material in energy absorption control (Baroutaji 2014;Yu et al. 2015).In the event of a pipe under compression due to a collision, it is characterised as follows: where L 0 is the initial length of the tube and S f , the effective stroke term, is defined as the displacement corresponding to the highest value of Equation ( 2) (deformation efficiency), as described by: where f represents deformation efficiency; s represents displacement; and F(s) represents the response of applied load as a function of the displacement.
• Energy Absorption Capacity (E) -The amount of energy absorbed/dissipated by the system as measured by the area under the Force-displacement curve (F − s), which is determined by the integral in Equation (3) (Baroutaji 2014): • Specific Energy Absorption (SEA) -Energy absorbed per unit of mass.It consists of the ratio between the total absorbed energy and the total mass of the absorber, defined by Equation ( 4) (Baroutaji 2014): This indicator represents a system's or component's ability to dissipate/absorb energy per unit of mass, which is particularly relevant in applications where the self-weight of the dissipating system is an essential quantity (automotive, aeronautical industry).
• Work Effectiveness (W eff ) -This indicator is the result of SEA and ESR, and it is especially beneficial for constructions with limited weight and space (Baroutaji 2014).
• Energy Absorption Effectiveness (EEA) -It is a dimensionless indicator that characterises a component's volumetric efficiency when compressed.This indicator is defined as the product of the average force and the product of the section area (A) and the material's yield stress (Y), multiplied by the ESR (Yu et al. 2015): • Energy efficiency (E e ) -The ratio of experimental dissipated energy and the theoretical dissipated energy (Baroutaji 2014), with Fmax being the highest load: • Energy absorbed per unit crush length (E cl ) -The proportion of dissipated energy to original length.This indication is helpful in cases where the energy-absorbing zone has a restricted length/space (Baroutaji 2014): • Dynamic amplification factor (DAF) -Used to assess the influence of dynamic stresses on the absorber system, which is defined as the ratio of absorbed energy over dynamic stress (E din ) and quasi-static stress (E est ), which is defined as:

LeanDfX framework description
The Lean Design for Excellence framework (abbreviated as LeanDfX) is a comprehensive approach that integrates modular design methodology and lean design principles.It is applicable throughout the product development cycle and product life cycle, combining Lean Thinking with Design-for-Excellence concepts to assess product performance ( Atilano et al. 2019).This framework enables management and technical teams to effectively evaluate various disciplines (referred to as 'X' domains) of a product in terms of compliance with requirements/targets and technical standards, as well as design efficiency, over-engineering, and optimisation possibilities (Atilano et al. 2019;Dombrowski, Schmidt, and Schmidtchen 2014).It can be applied to multiple design domains (DfX) for multivariate analysis of different product or systems development projects.The design and development of a new product typically involves collaboration among technical teams from various disciplines and the integration and management of numerous requirements and resources, resulting in extensive lists of technical specifications.To effectively manage these requirements and specifications, each specification must be related and characterised to its appropriate domain X and LeanDfX Scorecard (as illustrated in Figure 3), which is connected with each product life cycle (Carneiro et al. 2022).
The LeanDfX tool employs a systematic approach in which each important technical specification, typically derived from client or regulatory requirements, is allocated to a relevant domain X.This domain X is then associated with a Key Performance Indicator (KPI) for Design, that is assessed on a normalised scale of 0 to 100%.A distinctive feature of LeanDfX is the double assessment of design, in the sense of the combination of Effectiveness (requirements/specification goal check) and Efficiency (resource inefficiency evaluation), to provide an original integrated DfX holistic assessment along the modular design principles.Effectiveness refers to doing the right things to create the most value, (Jacobs and Chase 2013).Applying this to LeanDfX perspective, means if a particular requirement or specification target is met by the variable (also related to the design concept of minimum threshold fulfilment).In Equation ( 10) is the typical effectiveness normalisation ratio for a given Design_KPI (i,j), a Design_KPI(i) associated to a product Module (j).
Efficiency refers to, when all requirements are met, assess the degree of resources (any kind) allocated to the fulfilment of the design goals.This is in-line with the core meaning of 'efficiency is a ratio of the actual output of a process relative to some standard and doing something at the lowest possible cost' (Jacobs and Chase 2013).Moreover, by assessing resource allocated to a given design KPI, LeanDfX proves its Lean Thinking principles, to address over-engineering design measure (inefficiency evaluation), and also the life cycle engineering dimension by explicitly account the design impact, for instance, in the energy consumption within the product use phase.In LeanDfX, the efficiency assessment can require the use of an additional target besides the effectiveness one, since the 'minimum threshold' for a given design specification, can be conservative regarding the related resource efficiency ratio.Therefore, for efficiency, the notion of 'ideal target' appears, and must be defined along the vision of life cycle engineering (both to minimise product cost, production cost, use-phase costs and associated environmental impacts).In Equation ( 11) is the typical efficiency normalisation ratio for a given Design_KPI (i,j), a Design_KPI(i) associated to a product Module (j).
The KPIs are presented in a scorecard format within the product's module (as shown in Figure 3), allowing for a comparison of different product concepts or versions, with or without integration of multiple X domains.The LeanDfX framework is built upon four fundamental pillars (Baptista, Peixoto, et al. 2018): 1. Modular Product Breakdown -Using modular design principles to organise functional components into modules/physical units that interact with each other (Figure 4).This allows for effective management and visualisation of product complexity through decomposition into modules and sub-modules, enabling targeted evaluation of performance at each level of the product tree.2. Definition of Design Variables/Disciplines (KPIs) -Technical managers specify significant X domains for each product module, declaring and describing design variables (KPIs) based on product type, needs, and specifications.3. Indicator Visualisation System -Displaying KPIs (0-100%) using a colour map (red, orange, yellow, and green) based on efficiency.For effectiveness, just green and red is used, to emphasise the 'pass-or-fail' consistence for design goals meeting the product specification.This facilitates the identification of modules/sub-modules for potential improvement, aiding decision-making during the project lifecycle.4. Aggregation of Effectiveness and Efficiency Indices based on Modular Structure and X Domains in a Bottom-Up Logic -KPIs from sub-modules (low-level) are used to compute effectiveness and efficiency, and then aggregated to higher levels.Efficiency is evaluated only if the effectiveness criterion is met, and all modules/submodules have equal relative weight to minimise distortion effects.The aggregation method is typically a simple average for efficiency KPI(i,j), but the weighted average can be also considered.For the effectiveness the integration aggregation is only based on the Design KPI that passes the spec.targets.Thus, even if a given KPI of a module has effectiveness ratios close to 100%, the module aggregation result will not account this 'close to 100%', rather account as 'fail' to the accounting.
Even if LeanDfX base methodology is agnostic, its potential and value increase significantly with improved planning and knowledge of the processes from conception to product completion.
In this work, the LeanDfX methodology was adapted to create the 'Automotive Eco-Safe technic by LeanDfX'.The specific cross-combination of Design-for-Crashworthiness, and Design-for-Environment via Eco-design was set-up, around LeanDfX methodology, and a new workflow for the overall design assessment management, as detailed below: A. Define the complete automotive structure (or sub-structure) specification and key requirements, detailing the crashworthiness targets and Eco-Design goals.B. Parameterise LeanDfX DfCrash scorecards accordingly both product modular structure, Crash Design KPI and Eco-design KPI. C. Assess the Crash results either in specific graphs dashboards and making use of LeanDfX scorecard results evaluation.D. If the DfCrash specification criteria passes (effectiveness and efficiency), assess the Design KPI in DfEnvironment, if not, redesign the structure, and re-assess results back in 'C'.If no further re-design if possible, evaluate if the specification targets can be adjusted.E. If Crashworthiness results are fulfilled and Environment KPI passes, the crossassessment is complete, otherwise, re-design the structure and/or change material selection or processes of manufacturing, to keep the structural performance but lowering environment impacts.

Crashworthiness numerical analysis
The numerical simulation using the finite element method (Hughes 1987; Zienkiewicz and Taylor 1989) is a significant part of the research, requiring extensive computational resources due to the complex modelling needed for accuracy.The PamCrash R software (ESI Group 2019) was used for the frontal test of the coach according to ECE R-29 regulation, with modelling initiated in Abaqus R (Smith 2009) and finalised in PamCrash R .Two-dimensional formulations were chosen for most of the modelling to reduce computing time, as they are suitable for structures with adequate discretisation and are less prone to negative Jacobian-associated errors (Bathe 1982;Reddy 2019).One-dimensional modelling was used for selected components, providing precise bending behaviour with a mesh equivalent to partitioning the components into multiple segments for smoother results (Berlioz and Trompette 2009;Wai, Rivai, and Bapokutty 2013).
The modelling approach in this research uses shell elements for panels, glass, and chassis pieces, while one-dimensional elements are employed for components with circular cross-sections (Wai, Rivai, and Bapokutty 2013).Three-dimensional elements are used for certain components such as the steering wheel, seat, and frontal reservoirs.A virtual pendulum with a mass of 2500 kg and compliant geometry with the regulation ECE-R29 was designed to simulate impact and deformation of the structure.The final model is illustrated in Figure 5.
To perform this simulation, an explicit formulation was used for the dynamic simulation.The explicit model involves several tasks after defining the finite element mesh (FEM).These tasks include: 1. General contacts: Assessing the contact type between the parts to precisely simulate the interaction between the impactor and the section; 2. Boundary conditions: Constraints and loads are defined to be applied at the node in order to replicate real test conditions, such as ground anchorage and/or impact velocities, in the model (Smith 2009).3. Mass scaling characterisation: It is a straightforward technique that involves artificially increasing the uniform density of a material to virtually enhance its mass.This enables the optimisation of computational efficiency without compromising accuracy in simulations (Cocchetti, Pagani, and Perego 2013); 4. TIE constraints: Surface-based binding constraints can be used to relate a pair of surfaces.One surface in the constraint is designated as the slave surface, while the other surface is the master surface, (Smith 2009); 5. Characterisation of connecting elements: Definition of the mechanical properties of the connecting fasteners.
The model employed a total of 593,481 nodes, which were distributed among 189 beam elements, 2473 triangular elements, 498,871 quadrangular elements, and 128,944 solid elements.Moreover, the structure was partitioned into 525 parts and encompassed 3 contacts and 2 boundary conditions.The selection of integration rules was contingent upon the precise requirements stipulated by the software for the particular type of content being simulated (ESI Group 2019).The following material laws were used:  Stress-strain curve computation is decoupled in major directions.Used in driver's seat modelling with reduced integration formulation and hourglass control based on stiffness modulus and shape vectors.7. Material 201 (ESI Group 2019) -Elastic beam and bar material.Timoshenko C 0 theory is used for the integration approach, requiring C 0 continuity.Shear strains are determined from displacement fields, rotation uses linear form functions, and variational formulation yields stresses and shear forces.Coordinate axis system must be described locally for each scenario to calculate moments of inertia accurately (Hutchinson 2000).8. Material 1 (ESI Group 2019) -Elastic-plastic model for solid and tetrahedral components, specifically for modelling metallic component deformation after vehicle crashes.Elastic behaviour is characterised using bulk modulus, shear modulus, young modulus, and Poisson's coefficient.
Furthermore, the contact employed in this model allows for the testing of three separate contacts: impactor-coach section, section-section (self-contact), and steering wheel-seat interaction.Contact 33, (ESI Group 2019), is a symmetrical node-segment master-slave contact that verifies and tests for penetrations into both the slave and master nodes/segments.This contact is used to represent impact contacts to increase precision, specifically used for the interaction between the section and the impactor.A parameter for locally altering contact thickness is specified to avoid early penetrations and increase result accuracy.Contact 36, (ESI Group 2019), is a node-segment contact with self-impact that allows for representing self-contact interactions, such as collisions between coach/components or contact induced by crushing of profiles.A surface is specified as the slave, and each slave node-segment is examined for penetrations.Nodal and/or segment thickness is lowered to eliminate early penetrations and improve accuracy, similar to the previous contact.Contact 10, (ESI Group 2019), eliminates numerical difficulties that may occur with solid elements (including tetrahedral elements) being squeezed and twisted.It aids in reducing excessive compression and avoiding distortion of large parts, which can result in poorly defined elements with negative volume.This contact is suitable for modelling contact between the steering wheel and the seat.
The boundary conditions applied in the model are described as follows.Instead of simulating the falling motion of the pendulum, a initial velocity is assigned at the pendulum lowest position, which also corresponds to the highest velocity instant.This boundary condition aims to simulate the pendulum movement while reducing computational costs.The assigned velocity is rotational around the Z axis, with an initial value of 2.57 rad/s (refer to Figure 6(a) for the rotational speed representation).On the other hand, the structure is anchored to the floor using 6 pins, as depicted in the numerical model (see Figure 6(b)).In conclusion, it can be stated that the gravitational effect is considered in the model.The connections in this model primarily consist of MTOCO (Multiple Nodes to One Node Constraint) which impose restrictions on the connection of multiple nodes to a reference node, allowing for translational or rotational degrees of freedom.This results in a set of nodes corresponding to the independent node, depending on the degrees of freedom, (ESI Group 2019).These constraints are applied to a predefined local coordinate axis linked to the independent node, with the objective of accurately representing parts such as front bumpers and connections to the front glass.Additionally, the majority of chassis components are connected using TIE constraints, a common approach for modelling nut-bolt connections and welds.The model includes a total of 868 Tie constraints, each with a Master-Slave pair.Table 1 provides detailed mechanical characteristics of materials in the coach section, with classification based on mechanical properties from MatWeb (MatWeb 2022).Notably, the mechanical behaviour of Composite (Polyester-Glass fibre) and DCPD polymer is determined through tensile tests performed at INEGI's facilities, following ASTM D3039 (ASTM International 2000) and ASTM D638 (ASTM International 2010), respectively.
Figure 7 depicts the components that will be changed.In Figure 7(a), the red components will be replaced by DCPD polymer, which was previously manufactured in a polyester reinforced with glass fibre composite.In Figure 7(b), the red components will have a 1 mm thickness increase while keeping the cross section.It is meant to analyse the structural behaviour of the vehicle upon frontal collision in light of these modifications.The final modification is seen in Figure 7(c) where red ribs were added to increase bending resistance in a head-on collision.

Analysis and results
The outcomes were retrieved to precisely characterise the changes resulting from the enforced adjustments.In accordance with regulation R29, the key criterion for test success is the dummy and its residual survival space, which determines whether there is interference between the dummy and the deformed components.Additionally, strain values were recorded and compared for selected points to track the evolution of both geometries.The visual progression of deformation during impact is depicted in Figure 8.It is evident that the presence of the door reduces the stiffness of the structure in this lateral, resulting in increased intrusion due to pendulum torsion.However, the structural improvements observed may be beneficial, as the increased energy absorption capabilities of DCPD can potentially enhance driver's safety.This aspect will be further explored in the subsequent sections.
Figures 9 and 10 offer a comprehensive comparison of the displacements along the xx and yy axes of the driver's side, respectively, providing valuable insights into the impactor's intrusion.A closer look at the xx axis, which is particularly crucial as it directly relates to intrusion, reveals that the original model exhibits more intrusion in the pendulum crushing area, with the energy concentrated in the driver's residual volume.However, the difference is not as prominent on the vertical axis.This analysis of the figure's sheds light on the impactor's intrusion and the distribution of energy in the respective models.
Through the analysis of Figure 11, it becomes evident that the optimised structure exhibits an increased absorption capacity when evaluating the impactor velocity and the internal energy of the structure.This is clearly observed in Figure 11(a), where the optimised structure shows a higher peak in energy absorption.Furthermore, Figure 11(b) reveals that in the interval [0.04, 0.07]ms, the impactor's velocity decreases more rapidly in the Plastic Behaviour -DCPD optimised configuration, indicating the enhanced absorptive capability of the structure.
The absorbed energy plot also exhibits a steeper slope in this interval, which can be attributed to the increased energy absorption capability of the optimised structure.This suggests that there may be less material encroaching on the surviving area, which has significant implications for improved crashworthiness performance.
In order to investigate the direct impact of the changes resulting from the modifications, a detailed analysis was conducted on two specific chassis nodes located in close proximity to the impact zone.These nodes, as shown in Figure 12, were chosen due to their direct influence from the modifications performed.The displacement evolution of each node over time was analysed and compared between the original and optimised sections, as shown in Figure 13.It was observed that both nodes exhibited similar behaviour, with reduced displacement following the adjustments, resulting in less intrusion into the driver's cabin.This favourable behaviour indicates the positive impact of the modifications on mitigating intrusion and enhancing occupant safety, as demonstrated by the reduced displacement of these critical nodes.
Figure 14 presents a top view, in section, of the geometric deformation of both geometries when the impactor's velocity reaches zero.This examination considers the dashboard and its components anchoring.Upon visual examination, it is observed that the residual region for the driver is similar in both tests at this stage.This is particularly noticeable between the centre column of the dashboard and the side of the driver's seat.The consistency in the residual region suggests that the modifications made to the geometry did not significantly impact the deformation in this specific area.
To conduct an analysis of the accelerations and deformations a specific set of points of interest was identified, as illustrated in Figure 15, based on their proximity to the modified region and the immediate impact zone of the striker.The curves depicting the obtained accelerations are presented in Figure 16, while the evolution of strain curves may be seen in Figure 17.It should be noted that the accelerations were filtered using a CFC180 filter, to remove high-frequency noise while preserving lower-frequency information that is relevant to this analysis (ESI Group 2019).
In terms of accelerations, it is observed that there is a slight disparity in the recorded acceleration values between the left and right sides, which could be attributed to the presence of the side door, resulting in lower stiffness and higher intrusions in that area.Lower values of accelerations on the driver's side are beneficial for the driver's physical safety.However, despite the relatively higher accelerations observed in the optimised structure, the overall pattern is remarkably similar.The higher accelerations can be attributed to the fact that the test is conducted with perfect fixation to the ground, indicating that the structure absorbs all the energy.
Upon closer examination of the strains, it is evident that the modifications result in significant variation in strain values.Among the selected points, those near the bottom exhibit minimal variation.On the other hand, the optimised structure shows substantially lower strain in the top half.This is advantageous as maintaining this area in an elastic regime prevents the creation of plastic joints, thereby limiting intrusion into the driver's cabin.

Environmental performance analysis
After the crashworthiness analysis and the specification performance were achieved, the Design-for-Environment is assessed.The Life Cycle Assessment (LCA) technique is employed for this purpose.Life Cycle Assessment (LCA) is the most commonly used methodology for assessing the environmental impacts of a product or system because it allows for the analysis of the potential environmental impacts of the various stages associated with the life cycle, that is, from raw material production to end of life (Sala et al. 2021).

Case study
This work follows the standardised LCA methodology (refs).The aim of this research was to compare the environmental performance of two modules with identical functionalities, focusing on the composition of these models in relation to the materials that make them up and the repercussions they will have on a frontal impact.This research aims to analyse the stages from the extraction and transformation of the raw material to the use of the bus over 15 years.The processing of the modules, the manufacture of the equipment used in production, the installations and other components used in the construction of the bus are excluded from the system under study, as we can consider these stages to be identical in both models under study.Activities related to transporting, receiving and storing raw materials from the manufacturing site to the assembly facilities were also eliminated, as well as waste created throughout the production and use phases, such as tyre repairs, battery replacement, etc.Although the stages excluded from the study vary depending on the materials defined, these stages had to be excluded because there was not enough information to carry out this analysis.
The functional unit (FU) is a bus module (front chassis and cab), where it was assumed that the lifetime (15 years) and kilometres travelled by each module were the same.Figure 18 depicts the system boundaries under consideration.

Life cycle inventory
The materials and transformational processes inventory for each phase of the coach modules' life cycle is shown below (namely the extraction of raw materials, production of the materials that make up the module and their use).The inventory was built using information given by the partners in the form of tables including the components and their constituent materials.Whenever possible, information from the literature was used to determine the composition of particular items.Finally, a connection was formed between the materials and the inventoried processes in the Ecoinvent life cycle database version 3.7.1 (Wernet et al. 2016), in order to gather information on the mass and energy fluxes for raw material extraction and manufacture.The relationship between the materials and the database procedures is shown in Tables 2 and 3 displays the mass characterisations for each module under consideration.
Table 4 presents the transformational processes associated with the processing and handling of components in the inventory.For instance, processes such as Wisa-Wire, Pecolite, and reinforced polyester with fibreglass are included as examples of materials that have their transformational processes accounted for within the respective material processes.
Assuming that both models run on diesel, the production and consumption of this fuel was considered throughout the usage phase.The average density of diesel estimated  was 850kg/m 3 (Khan 2011).Because fuel usage is proportional to vehicle weight, the link between these two quantities must be established.The LCA research focuses on only one module of the bodywork, which represents only a small proportion of the overall mass of the bus and is responsible for only a small portion of the environmental consequences created by the bus during its entire life cycle (15 years).The 'Incremental Method' is commonly used to allocate the hypothetical fuel consumption of each component of a car and will be used in this study to determine the consumption associated with each module (Subic and Schiavone 2006), and this method only considers the effects of vehicle mass.Equation ( 12) represents the fuel consumption calculation for the original module's Defined Material Family Transformative Process, and Equation ( 13) represents the fuel consumption calculation for the optimised module.

Original module fuel consumption
Optimised module fuel consumption : where • C orgcomp , the original component's fuel consumption in kg/100km; • C oticomp , the optimised component's fuel consumption in kg/100km;  The assumed value for the coefficient c in this investigation was 0.6, as it is in other LCA studies (Ribeiro, Ferreira, and Partidário 2006).As a result, Equation (11) allows for a consumption of 2.80 l/100 km for the original module and 2.76 l/100 km for the optimised module.The computed consumptions are solely related with the modules, not with the entire body.Table 5 shows the fuel consumption of the modules under consideration over 1, 5, and 15 years of life, as a function of the travelled distance.
The gaseous emissions associated with the combustion of diesel were calculated using the emission factors provided by the EMEP/EEA (2009).These factors represent the amount of each pollutant emitted into the air for each kilogram of fuel used.In Table 6, the values   for the calculation of total quantities can be consulted.On the other hand, Table 7 presents the inventory data for the usage phase corresponding to the original module and optimised module.

Life cycle impact assessment characterisation
The ReCiPe 2016, (Huijbregts et al. 2017), approach was chosen to transform each model's inventory into probable environmental consequences.This approach converts emissions and other material and energy fluxes into impact ratings using characterisation variables.These parameters can be determined at three levels: intermediate (midpoint), environmental impact categories, and aggregate harm.Each level contains factors based on three cultural perspectives: Individualist (short term and optimistic about potential future impacts), Hierarchist (widely accepted scientific principles, model used by default), and Egalitarian (long term and pessimistic about technological advances in future impacts).The ReCiPe 2016 v1.1 approach was used for this investigation, and the environmental effect was estimated using the midpoint and endpoint values.This method converts the inventory list into a limited number of environmental impact categories by using characterisation factors that express the contribution of each pollutant to each environmental impact category (midpoint) or weighting factors that allow the groups indicated in three final indicators (endpoint).
The inventory data was then processed in the SimaPro v9.2.0.1, (PRé Consultants 2019), software using the ReCiPe technique to assess the environmental consequences of the data entered and the information inherent in the Ecoinvent v3.7.1, (Wernet et al. 2016), database's extractive and transformational processes.Long-term emissions and secondary infrastructure were not considered in this computation.Table 8 shows the environmental effect categories that were investigated.

Environmental performance -materials analysis
One of the changes identified between the modules investigated in this study is the substitution of polyester with fibreglass by DCPD.As a result, the environmental performance of these two materials was investigated.Figure 19 depicts the environmental performance of 1kg of each of the materials chosen for the environmental effect categories, given in Pt.It demonstrates that glass fibre has a total of 1.40E − 01Pt and DCPD has a total of 8.94E − 02Pt, resulting in a 36% difference in total environmental consequences.As a result, the results show that the DCPD has a better environmental performance than glass fibre, making it a more ecologically friendly solution.

Environmental performance -case study results
As previously stated, the research under consideration considers both the extraction and processing of the raw material, as well as the phase of use of the bus throughout its life cycle (15 years).Figure 20 depicts the characterisation step findings for each of the life cycle stages evaluated in the study, for both the original and optimised modules.Because there is no discernible variation between the modules, the findings for the models under consideration are comparable, with an insignificant variance in the categories of ozone layer depletion and human toxicity throughout the usage and raw-materials extraction and processing phase, respectively.The use phase contributes the most to the environmental impact of the nine selected categories: global warming (89%), stratospheric ozone depletion (85%), ozone layer depletion (85% -original model; 88% -optimised model), ionising radiation (89%), ozone formation (98%), fine particulate matter formation (91%), terrestrial acidification (94%), terrestrial ecotoxicity (54%), and fossil resources scarcity (92%).The extraction and processing of raw materials is the phase that contributes to the remaining categories, which are marine eutrophication (97%), freshwater ecotoxicity (75%), marine ecotoxicity (68%), land use (99%), mineral resource scarcity (97%), and water consumption (89%).
Table 9 depicts the possible consequences of each module using the ReCiPe Endpoint (H) technique.The ecopoint data provided in Figure 21 demonstrate that both the extraction and processing of raw materials, as well as the use phase, have equal environmental implications for both modules under consideration.
Analysing in Figure 21, it is also confirmed that the usage phase has the highest environmental impact for both modules under consideration.Throughout the life of the bus, this phase is defined by the generation of diesel and the gaseous pollutants related with its combustion.Although the difference is not clear, the optimised module (1630.97Pt) has lower environmental impacts when compared to the original module (1654.99Pt).This is    due to the optimised module having a lower mass relative to the original module, as diesel consumption is related to the total weight of the module.Following that, a comparison of the environmental performance of the original module and the optimised module across their full life cycle and for each of the life cycle stages studied is shown.The weighting step outcomes are reported in terms of a single indicator (single score), which is quantified in points (Pt).The potential environmental impacts of the modules under analysis using the ReCiPe Endpoint (H) methodology are shown in Table 10.
Figure 22 depicts a comparison of the original and optimised modules for the environmental effect categories in Pt.The data shown in this figure shows that the original model has a total of 1986 Pt and the optimised model has a total of 1958 Pt, indicating that the optimised model's environmental performance is 1.40% lower than the original model's environmental performance.This is due to the modification of existing material thicknesses in the original model, as well as the usage of DCPD material instead of glass fibre.The categories of Global Warming and Particle Formation are the most important, stemming mostly from fuel use throughout the bus use phase.Analysing the materials used in modules, it shows that the majority of the environmental consequences are caused by the quantity of steel employed in these parts.

LeanDfX cross-assessment results
The LeanDfX tool is a modular design framework that encompasses various stages of the product development and life cycle, integrating Lean Thinking concepts with Design-for-eXcellence principles.It enables the identification of areas in the product that require improvement in terms of effectiveness and efficiency.To effectively utilise the LeanDfX tool, it is important to define indicators associated with each area of investigation.In the context of the X-Dimension related to Design-for-Crashworthiness, the tool was applied to analyse the design of the use case structure (coach -regulation ECE-R29) from an innovative perspective.This involved integrating data on objectives, specifications, and requirements, and comparing the design results between the original version and the new version with improvements.The key design variables considered in the study were total absorbed energy [kJ] and critical displacements of the structure [mm].
Figure 23(a), depicted in the diagram below, illustrates the findings for Design-for-Crashworthiness of the coach front structure.The initial production version of the coach already met the minimum acceptable values for Internal Energy and the two maximum displacements determined at critical locations on the front of the bus.Even the efficiency levels, which encompassed reducing over-engineering and related inefficiencies such as excess material, were satisfactory.However, the project team was able to further enhance the efficiency values for the selected Key Performance Indicators, resulting in outcomes that were very close to the optimal targets set in the objectives.The comprehensive numerical study revealed a significant improvement in the structure's frontal impact performance due to the implemented changes.The energy absorbed by the structure increased from 95% to 98%, according to the obtained scorecard in Figure 23(a), effectively preventing energy transfer to the driver and thereby enhancing the probability of survival.Additionally, efforts were made to minimise intrusion in accordance with the ECE-R29 standard.The selected points for measuring intrusion showed notable improvements, with P1 improving from 90% to 97% and P2 improving from 90% to 100% relative to the original values.
For the X-Dimension related to Eco-Design, the key variables identified for both domains (modules being investigated) were mass (kg), diesel consumption (l), overall carbon footprint during the usage phase (kg CO 2 eq), and total effects of raw materials (Pt).Two targets were defined for each ratio (effectiveness and efficiency): a minimal value that represents the minimum threshold for achieving efficacy, and an ideal reference objective for efficiency, which should not exceed 100%.If the minimal value is met, the efficacy is considered as 100%.If the minimal value is met, an efficiency indicator is calculated with respect to the ideal reference value.It is important to note that the minimal value (permissible value) was set as the original module value, while an optimal value for the carbon footprint and material impact variables was considered to be a 20% reduction from the original module value.Based on the scorecard results, the findings for the modules under review indicate that the outcomes are very similar, albeit with an improvement compared to the previous version of the coach structure design.In terms of effectiveness, it can be demonstrated that the effectiveness targets set for the various design Key Performance Indicators were achieved for both versions of the modules being evaluated, with the thresholds defined by the validated figures from the original design.In terms of overall efficiency ratio, the original module achieved 88%, while the improved design version reached 90%.Upon analysing each design KPI, marginal improvements were observed in all four KPIs considered.For the Mass KPI, the efficiency improvement was based on a reduction from 1775.94 kg in the previous design version to 1750.41 kg in the new module, compared to the ideal value of 1600 kg.The carbon footprint during the usage phase and the material effects had the lowest efficiency, with variances of approximately 20% for both design versions.Although the difference is minor, the optimised module version slightly outperforms the original module design, with 36.9 tons of CO 2 emissions for the new version compared to 37.4 tons for the previous design, against an ideal target of 30 tons of CO 2 emissions.Finally, for the Environmental Impacts related to materials, the gains were based on 1631 Pt for the new design version compared to 1655 Pt for the previous design, with a reference ideal value of 1500 Pt.
The cross-analysis of the Crashworthiness and Eco-Design allowed the assessment of the improvement of the coach front main structure.It was improved the key crashworthiness design parameters without namely a weight increase and environmental impact penalisation.

Conclusions
Coach transports tend to be more regulated into very important areas of design knowledge and people and planet protection (passenger safety and environmental impacts reduction, respectively).This research contribution aimed a novel technique to support design assessment and decisions trade-offs.Due to increased product complexity and also new regional-sectorial directives, it is important to explore further the approach, for instance in the areas of product circularity (retrofitting, remanufacturing, etc.) or other emerging areas of security as cybersecurity, due to increased connectivity and advanced driving assistance systems in road vehicles.The main purpose of this study is to cross-assess the crashworthiness optimisation and Environmental Impact improvement of a redesigned passenger coach integrating LeanDfX Framework.Within the study an integrated technic designated 'Automotive Eco-Safe by LeanDfX' was developed and its main steps.
The environmental impact assessment of two solutions implemented in the automotive sector was carried out, especially in public heavy transportation, and to examine the impact that this replacement has on vehicle crashworthiness.The ECE R29 standard is adapted, which aims to certify the structural integrity of a heavy vehicle in the event of a frontal collision.Two structures are considered: an original one and an optimised version that undergoes structural reinforcement, with outer panels made of a latest-generation polymer (DCPD).
Through FEM simulations, the optimised structure demonstrates superior structural behaviour in the event of a frontal collision, thereby improving the probability of safeguarding the driver's physical well-being.This version of the coach absorbs more energy while allowing less material intrusion into the passenger area.Regarding accelerations and deformations, it is feasible to verify that the results for deformation are promising, but accelerations remain residually higher in the optimised solution, maintaining the same evolutionary trend.In summary, it is confirmed that the second solution is an enhanced version of the original, without a loss of structural properties.
Regarding the environmental evaluation, the goal of this research was to estimate the possible environmental implications of each module in order to identify which would have a better environmental performance.Based on the LCA methodology, this study evaluates the phases of raw material extraction, material processing, and coach usage during a 15-year period.The environmental consequences were quantified in Simapro v9.2.0.2 software using the ReCiPe 2016 method.The results demonstrate that when compared to the original module (1655 Pt), the optimised module (1631 Pt) has reduced potential environmental consequences.Analysing the various phases of the life cycle of the products under consideration, it was verified that the use phase contributes the most to the total environmental impacts, resulting from diesel production and gas emissions associated with vehicle combustion during the course of the vehicle's life cycle.The original LeanDfX Scorecards were evolved to a cross-integrated assessment technique of DfCrashwothiness and DfEnvironment.The design findings were compared between the initial model, which served as a baseline, and the optimised version.In the instance of the tourist coach, both constructions (original and optimised) met the particular requirements and permissible values, and both with extremely low levels of over-design (low inefficiency), indicating a good resource allocation at design stage.The cross-results of Automotive Eco-Safe technique by LeanDfX provided a new perspective in the integration of these two important domains of automotive industry.Knowledge and project systematisation were produced during this procedure for complex product design (passenger transport vehicles), with original selection and monitoring.
The novel technic can be also a useful tool for other kind of products, namely land transports such trains, where trade-offs assessment between crashworthiness and eco-design guidelines are also present.Thus, in this context, it can be an additional support design decision either for designers and technical, product and project managers, and explore the LeanDfX framework for another sector (rail transportation).
In future endeavours, it would be advantageous to integrate material characterisation based on strain rate into the numerical models.The behaviour of certain materials can exhibit variation under higher rates of deformation, characteristic of dynamic tests such as crash scenarios.Enhancing the numerical model could involve incorporating models that accurately depict these specific material responses.Regarding environmental performance, it would be interesting to consider all the life cycle stages, since the materials could affect the assembly phase as well as the end-of-life phase of the modules under study.

Disclosure statement
No potential conflict of interest was reported by the author(s).

Figure 3 .
Figure 3. LeanDfX Scorecard example, for a given Domain X and three product modules and four design variables.

Figure 5 .
Figure 5. 3D view of the FE mesh of the complete coach structure in PamCrash, highlighting the driver side.

1.
Material 103 (ESI Group 2019) -Thin shell material model with elastic-plastic isotropic behaviour, utilising an advanced plasticity algorithm with cross-sectional effects.It follows the Hill criterion(Hill 1998) and updates element thickness during plastic deformation iterations.Hybrid formulation of Belytschko-Tsay shell(Belytschko and Tsay 1983;Haufe, Schweizerhof, and DuBois 2013;

Figure 6 .
Figure 6.Definition of the boundary conditions of the FEM model -PamCrash: (a) applied to coach's section represented by the arrows (fixed rotation + fixed displacement) and (b) applied to impactor represented also by arrows (free rotation and angular initial velocity along zz).

Figure 7 .
Figure 7. Visualisation of the modified components in the optimised model: (a) out panels replaced by DCPD polymer, (b) modified chassi components in thickness and (c) The red hue components represent the ribs that form between the shapes with straight angles.

Figure 8 .
Figure 8. Visual comparison of the geometric deformation for different simulation steps, for the initial geometry and the optimised geometry.

Figure 9 .
Figure 9.Comparison of the contour of displacement xx for v = 0 m/s of the impactor, in mm for the: (a) original geometry and (b) optimised geometry.

Figure 10 .
Figure 10.Comparison of the contour of displacement yy for v = 0 m/s of the impactor, in mm for the: (a) original geometry and (b) optimised geometry.

Figure 11 .
Figure 11.(a) Evolution of the internal absorbed energy [kJ] of the system and (b) Evolution of the impactor velocity for both structures.

Figure 12 .
Figure 12.Location of intrusion analysis nodes.

Figure 13 .
Figure 13.Evolution of displacement in xx axis [mm], as a function of time [ms] for: (a) node 97 and (b) node 1937.

Figure 14 .
Figure 14.Comparison of residual driver space, in top section view for: (a) the original structure and (b) optimised structure.

Figure 15 .
Figure 15.Location of the strain (S Right−Lower , S Right−Upper , S Left−Lower and S Left−Upper ) and acceleration (acc Right and acc Left ) measurement points.

Figure 16 .
Figure 16.Acceleration magnitude data in accelerometers: (a) ACC Left and (b) ACC Right .

Figure 17 .
Figure 17.Comparison of the obtained strain at the pre-defined points for both structures, original and optimised structure: (a) S Right−Upper , (b) S Left−Upper , (c) S Right−Lower and (d)S Left−Lower .

Figure 18 .
Figure 18.System boundary conditions under study.

Figure 19 .
Figure 19.Comparison between glass fibre and DCPD, for the selected environmental impact categories, given in ecopoints, Pt.

Figure 20 .
Figure 20.Results of the characterisation step for the original module and for the optimised module for each impact category.

Figure 21 .
Figure 21.Environmental consequences of life cycle stages for the various modules under consideration, expressed in Pt.

Figure 22 .
Figure 22.Comparison of the original module and the optimised module for the specified environmental effect categories, expressed in Pt.
Figure 23(b) presents a comparison scorecard of the findings obtained for the original module and the optimised module, considering the Design-for-Environment research related to the Eco-Design of the coach's front module.

Figure 23 .
Figure 23.Comparative LeanDfX Scorecard for: (a) the domain -Design-for-Crashworthiness and (b) the case studies under consideration -eco-design.
(Abdoun et al. 2009;Abdoun, Azrar, and Daya 2010)uniform reduced integration using one integration point in the plane.Hourglass control is included for explicit analysis.Stiffnessbased technique is used to prevent hourglass, employing enhanced orthogonality to rigid body movement.2.Material 101 (ESI Group 2019) -Model for continuous shell components with linearelastic isotropic behaviour.Thickness variations are ignored for explicit analysis, and original thickness is always used for internal force calculations.Belytschko-Tsay reduced integration formulation is used with hourglass control based on the modulus of elasticity.3.Material 51 (ESI Group 2019) -Solid continuous elastic-linear material model for small deformations in explicit analysis.Total Lagrangian Formulation is employed to predict high rotations situations.Formulation of the average value for solid elements with 8 nodes is chosen, using the average value for derivatives of shape functions with one integration point.Hourglass control is required, and Bonet and Bhargava's approach(Bonet and Bhargava 1995)is used for hourglass control.4.Material 126 (ESI Group 2019) -Linear elastic material model with brittle failure criterion, intended for windshield glass with PVB reinforcement in automobile accidents.Three-layered model of glass-PVB-glass, with PVB as adhesive component(Lu et al. 2003).Belytschko-Tsay reduced integration formulation is used with hourglass control based on the modulus of elasticity. 5. Material 121 (ESI Group 2019) -Nonlinear, isotropic, Maxwell-type viscoelastic shell element, used to simulate PVB adhesive in a wide range of polymers(Abdoun et al. 2009;Abdoun, Azrar, and Daya 2010).6. Material 45 (ESI Group 2019) -Nonlinear elastic foam material with highly compressible behaviour dependent on deformation rate and energy absorption.

Table 1 .
Mechanical properties of the materials used in the coach section.

Table 3 .
Mass characterisation of the modules under consideration -both the original and optimised models.

Table 4 .
Transformative processes associated with the materials present in the models under study.

Table 5 .
Fuel consumption (l) of the original module and the module optimised for 1, 5 and 15 years of life, depending on the distance travelled in km.

Table 6 .
Emission factors for diesel combustion in heavy vehicles.

Table 7 .
Inventory of the original module and optimised module in the utilisation phase.

Table 9 .
Potential environmental implications associated with the various modules under consideration, based on the ReCiPe Endpoint (H) approach.

Table 10 .
Potential environmental impacts corresponding to the different modules under analysis, using the ReCiPe Endpoint (H) methodology.