Design for sustainability

3D-Printing, also known as Additive Manufacturing (AM), presents numerous opportunities for design engineers, such as fabricating complex geometries without tooling and manufacturing parts using material only where it is functionally required. Despite these advantages, one of the main challenges remains energy consumption. To assess whether AM offers superior sustainability performance compared to Traditional Manufacturing (TM), both Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) analyses are essential. While scientific literature includes various studies comparing AM with TM methods, these evaluations are often conducted using general-purpose tools. In response to this gap, the present study provides an LCA and LCC-based comparison between AM and TM for producing a motorbike brake caliper. The objective is to determine whether AM can deliver greater sustainability, from both environmental and economic perspectives, when assessed over the entire product life cycle. For AM, the analysis is conducted using a dedicated life cycle tool developed in prior research, whereas the evaluation of TM is carried out using established commercial software. The comparison deliberately excludes other considerations, such as product quality and performance. The findings indicate that AM does not emerge as the most suitable manufacturing option for this component, despite the implementation of lightweighting strategies.

Keywords: 3D-Printing , Brake , Caliper , LCA , LCC

This study presents a method for estimating product disassembly times using machine learning techniques. It demonstrates how to integrate the machine learning model with economic evaluations, circularity indices, and environmental impacts. The goal is to support sustainable design and optimize product end-of-life processes, enabling more informed decisions within the circular economy. The predictive model will be trained on experimental data to accurately estimate disassembly times, considering variables such as the condition of mechanical joints. A preliminary exploratory case study validated the characteristics and experimental dataset required for data collection through a screw rusting test. The results show a significant correlation between joint characteristics, their end-of-life conditions, disassembly configuration, and total disassembly time. Further developments are planned to expand the experimental database and refine the model, extending its application to different products and industrial contexts.

Keywords: Circularity , Disassembly , Environmental sustainability

The production of Engineered Wood Products (EWPs) — such as Oriented Strand Boards (OSB), particleboards, and fiberboards — plays a key role in the modern wood value chain, especially in the context of circular economy and climate change mitigation. By incorporating wood residues and low-quality timber into long-lasting products, these technologies contribute to carbon storage for up to 35 years, as recognized by EU regulations on carbon removals. However, the drying phase of wood particles is among the most energy-intensive steps in EWP manufacturing. This paper presents the development of a physical simulator aimed at optimizing the operational parameters of industrial dryers. The devised prototype will allow users to simulate and test drying processes under varying conditions, helping manufacturers reduce energy consumption without compromising final product quality. The simulator is intended to support the transition towards more sustainable and efficient production practices in the wood industry. The work is the result of a collaboration between academia and industry and represents a step forward in applying digital technologies to traditional manufacturing processes.

Keywords: Life Cycle , Mechanical Design , Simulation , Wood Products

The rapid growth of waste from electrical and electronic equipment (WEEE) poses significant environmental challenges. Efficient management of WEEE is critical for minimizing environmental harm and recovering valuable secondary raw materials. This study compares two alternatives end-of-life (EoL) modeling approaches in a Life Cycle Assessment (LCA) study to identify the applicability and consequences on results related to Circular Footprint Formula (CFF) implementation. The CFF explicitly incorporates circular economy principles by accounting for recycling, reuse, and energy recovery in a standardized way. Results demonstrate how the composition of the product, applying CFF approach, can lead to a substantial reduction in environmental impacts compared to the traditional EoL modeling methods in LCA. Sensitivity analysis highlights the importance of key CFF parameters, such as recycling rates and allocation factors in influencing results. The study underscores the relevance of adopting advanced modeling methods like CFF to enhance the accuracy and transparency of environmental assessments for WEEE, supporting sustainable product design and policy development.

Keywords: environmental impact , LCA , PEF , WEEE

In the early stages of product design, considering environmental impacts throughout a product's life cycle is essential to support sustainable industrial development. Traditional Life Cycle Assessment (LCA) methods, while comprehensive and systematic, are often limited in the conceptual design phase due to the lack of detailed information and the time required for modeling. Parametric LCA approaches attempt to bridge this gap but still demand extensive data and expertise. To address these challenges, this study proposes a methodology based on Machine Learning (ML) techniques, enabling the implementation of a Surrogate LCA for preliminary environmental evaluation during early design. ML models can learn from existing LCA datasets to predict environmental impacts using limited design parameters, providing rapid and informed feedback to designers. This approach transforms LCA into a proactive design aid, capable of handling data uncertainty and dynamically adapting to design variations. The study demonstrates the methodology through a case study, showing how product geometry influences prediction accuracy and, consequently, the uncertainty of environmental impact estimations. Results highlight the potential of ML-driven LCA tools to enhance early-stage design decisions, supporting the transition toward circular economy principles and environmentally responsible product development.

Keywords: Conceptual design , Environmental sustainability , Life Cycle Assessment , Machine learning , Manufacturing

The growing focus on environmental sustainability has driven industries to adopt Life Cycle Assessment (LCA) to quantify environmental impacts. However, in the furniture sector, challenges arise due to the emphasis on craftsmanship, quality materials, and customization, making complex assessments difficult for small and medium enterprises. This research presents an integrated eco-design framework tailored to the furniture sector, which utilizes machine learning algorithms to support sustainable decision-making from early design stages. The framework introduces three key tools: a Qualitative Feedback Tool for gathering stakeholder input, an Eco-Design Tool for developing guidelines through machine learning, and a Simplified LCA Tool for conducting rapid environmental assessments. A case study with a leading furniture company validates the approach, focusing on the development of a sustainable seating archetype. Results show that the framework enhances decision-making efficiency, embeds sustainability early in the design process, and reduces costly late-stage modifications. By merging machine learning with eco-design, the study provides a structured and accessible path to sustainable product development, emphasizing the importance of integrating sustainability as a core design principle rather than an external constraint. This methodology offers valuable support for companies aligning with environmental regulations and meeting the growing demand for sustainable products.

Keywords: Eco-design , Furniture , Life Cycle Assessment

In the field of boating, the design of eco-sustainable solutions has been recently focused on hydrofoils. Such technology is employed in small and large vessels. A great challenge may be represented by the reconstruction of geometric details in objects with large sizes through traditional approaches based on simple and manual instrumentations and measuring systems. With regard to reverse engineering techniques, photogrammetry may be suitable for reconstructing devices with large sizes. The aim of the work was to report and provide an analysis on the last progresses related to the feasibility of the photogrammetric approach in the hydrofoil research, summarising a specific methodology reported in the literature to acquire and reconstruct the outer sector of the front wing of a Kolkhida-class hydrofoil. The potential of computational fluid dynamics was also stressed in the evaluation of the characteristic curves, focusing on drag and lift coefficients as well as on the error due to the reconstruction through photogrammetry. Data analysis and research generally require the possibility of sifting through a great amount of information, thus providing a very overwhelming and time-consuming approach. In this scenario, the focused summarising approach may play an important role in providing a simplification of the complex content and technical features into digestible insights. This means that the proposed summarising approach should be able to boost retention providing a help in remembering key features more effectively through their condensation to a reduced number of information and steps.

Keywords: computational fluid dynamics , Design methods , hydrofoil , measurements , reverse engineering

Large language models (LLMs) have rapidly transformed how information is accessed and generated across domains by leveraging deep learning to produce human-like responses. Their applications have become powerful in supporting coding, law, medicine, and design tools. However, despite their capabilities, LLMs often suffer from critical limitations such as hallucinations, lack of domain specificity, and reliance on generalized internet-based knowledge. These limitations could pose risks for industrial research and development (R&D), where precision and innovation are essential. This study investigates the potential of a patent-based Retrieval-Augmented Generation (RAG) tool (Omnia) to support R&D activities more effectively than general-purpose LLMs (Google AI Studio). Omnia accesses patent databases in real-time to provide structured and validated data, offering reliable and domain-specific responses. Multiple research questions were generated to evaluate the responses from both Omnia and Google AI Studio, which are addressed through targeted case studies. Findings demonstrate that patent-based RAG systems can offer significant advantages in R&D scenarios, including semantic search accuracy, TRIZ-based problem-solving, technical failure analysis, prospective life cycle assessment, and identification of circular economy opportunities.

Keywords: Artificial Intelligence , Large Language Model , Patent Retrieval , Retrieval-Augmented Generation , TRIZ

Home Cultivators (HCs) are emerging devices for food cultivation within residential environments, with increasing potential for self-sufficiency. Therefore, the current state of the art remains limited, with few established references. In this study, environmental simulations were conducted in Rhinoceros 3D and Grasshopper using Ladybug and Honeybee plugins (Radiance module) to assess the lighting and energy performance of a home cultivator oriented towards the south and north. Analyzed parameters include energetic variables (e.g., direct sunlight, cumulative radiation) and vegetative growth indicators (e.g., Photosynthetically Active Radiation, illuminance). This research project aimed to investigate several energy-related aspects concerning an HC and its cells in a NeoVernacular Kitchen context through computational simulations. To better understand the relationship between in-house lighting conditions and plant growth, a plugin named AiPlantcare was utilized to support the simulation process, enabling a more accurate assessment of light quality and energy efficiency. Results indicate that the south-facing side receives up to 44.9% more natural light and energy than the north, despite requiring shading strategies. In contrast, the north side offers greater stability but lacks sufficient resources for autonomous cultivation. CAD-based simulations provide a valuable tool for integrating cultivation systems into residential environments in the early design phase.

Keywords: Energy Efficiency , Energy Sustainability , Grasshopper , Home Cultivator , Rhinoceros 3D , Sustainable Design

Lithium–sulfur batteries (LSBs) are a promising emerging technology due to their high energy density, low-cost materials, and safety. However, their environmental sustainability is not yet well understood. This study conducted a prospective life cycle assessment (LCA) on two patented LSB models, using data from patents as the inventory: one with a standard sulfur cathode and another with a graphene–sulfur composite (GSC). The assessment is conducted for a functional unit of 1 Wh of produced electricity, adopting a cradle-to-gate system boundary and a prospective time horizon set to 2035. The LSB GSC model battery showed significantly better performance in terms of climate change and fossil depletion, with a 42% lower impact, mainly due to a reduction in the lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) content from 1205 mg Wh−1 to 250 mg Wh−1. However, the GSC model also had significant drawbacks, showing a 93% higher metal depletion and 49% higher water depletion than the standard sulfur battery. Building on an established patent-based prospective LCA approach, this work applies patent-derived quantitative inventories and patent-informed eco-design analysis to support environmentally informed design decisions for emerging LSB technologies prior to large-scale commercialization.

Keywords: eco-design , Li-S battery , patents , prospective life cycle assessment

Rotational moulding (RM) is a versatile technique for producing plastic products, but its environmental sustainability needs deeper analysis. Existing life cycle assessment (LCA) studies are often limited, focusing on single materials and ignoring variations in process parameters. This study introduces a parametric LCA model designed to minimize the environmental impact of RM composite products by optimizing material composition, specifically blends of polyethylene and natural fibre (e.g., abaca). The model allows users to set control parameters, including product dimensions, mould material and thickness, energy consumption, from gas furnace and motor, number of moulds, geographical location, and the impact category to assess. Applied to case studies with abaca fibre composites, the model demonstrated that optimizing the fibre-to-polymer ratio can significantly reduce environmental impacts. These reductions vary depending on the input parameters, confirming that a one-size-fits-all approach is inadequate. The model's ability to process multiple heterogeneous factors makes it a robust tool for eco-assessment. It supports systematic sensitivity analysis and offers practical, flexible guidance for eco-design in RM, particularly for multi-material products. Its strengths, flexibility, low cost, and ease of integration make it especially valuable for small and medium-sized enterprises with diverse product lines, enabling more sustainable decision-making without requiring extensive resources.

Keywords: Abaca , Bio composite , Eco-design , Natural fibre , Parametric LCA , Rotational moulding

With the increasing attention paid to sustainability and sustainable consumption, many products are redesigned to diminish their environmental footprint. This is also triggered by changes in legislation and the growing consumers' interest in what is supposed to be more environmentally friendly. As a consequence, more and more products are getting certified by governmental and other bodies. However, the proliferation of eco-labels leads to consumers' difficulty in distinguishing them in terms of their relevance, actual contribution to sustainability, and genuineness. In this context, the aim of the paper is to collect from the literature and catalog the design factors influencing the effectiveness of eco-labels and other forms of sustainability- and environment-related communication. The main outcome is the presentation of recommendations as a first core of design guidelines for products including eco-labels. These guidelines, seen as the final goal of this research, should support designers in making appropriate choices when designing products including eco-labels while willing to convey sustainability-related information most successfully and effectively. However, this study also highlights that much research is still needed to get knowledge about eco-labels' design and support designers appropriately. Hence, the paper stresses the large number of areas requiring further investigation and most pressing open issues.

Keywords: Certification , Eco-Labels , Logos , Recommendations , Sustainable design

During the last three decades, higher education institutions (HEIs) have integrated sustainability into their curricula. Recent research has focused on sustainability competences and pedagogical approaches, and how the latter foster the development of the former. Previous studies have examined the connections between pedagogical approaches and sustainability competences from an educators\' perspective. However, there has been limited research addressing a students\' perspective, particularly in relation to gender. This paper aims to provide insights into developing students\' sustainability competences through pedagogical approaches from a gender perspective. A survey was applied to students at 17 universities from 13 countries. The responses were analyzed using: (1) Descriptive statistics and Friedman tests; (2) t-tests comparing perceptions of male and female students; and (3) correlations between competences and pedagogical approaches comparing both perspectives. The results showed differences in how male and female students\' competences ranked, with female students having a more concentrated range of competences. The pedagogical approaches results showed little to no differences and concurred with the published literature. The results also indicated differences between genders regarding how the pedagogical approaches develop the competences, where female students tend to develop more sustainability competences than males. HEIs must recognise gender-based perceptual differences and develop strategies that build upon the strength of both perspectives and, thus, better implement sustainability competence education.

Keywords: education for sustainable development , gender , pedagogical approaches , students' perception , sustainability competences

The traditional automotive design process follows a structured, linear workflow, progressing through fixed phases from research to validation. While effective in ensuring reliability, this approach often results in high costs, prolonged development times, and limited flexibility for late-stage modifications. This paper introduces a Circular Iterative Design (CID) methodology, integrating Virtual Reality (VR) and Generative Artificial Intelligence (AI) within the Industrial Design Structure (IDeS) to enhance efficiency, adaptability, and innovation. The proposed framework enables real-time design iteration, significantly reducing early-stage conceptualization time by allowing designers to rapidly transition between 3D gestural sketching and 2D refinement. Advanced AI tools further accelerate the process by automating rendering, generating design variations, and optimizing surfaces with minimal manual adjustments. Additionally, by leveraging a progressive 3D dataset (Core Model) that evolves from initial sketches to refined Class A surfaces, CID minimizes redundant efforts and enhances workflow continuity. Compared to traditional workflows, this approach can reduce reliance on physical prototypes, leading to a faster and more cost-effective design cycle, while also promoting sustainability by optimizing resource usage. The findings of this study demonstrate that integrating VR, AI, and structured methodologies into automotive design offers a scalable, collaborative, and highly efficient alternative to conventional processes.

Keywords: Artificial intelligence , Design methodologies , Industrial design structure , Transportation design , Virtual reality

The disassembly of end-of-life products offers significant benefits for both manufacturers and the environment by recovering reusable parts and materials. Disassembly planning is essential but complex, especially with increasing part numbers. Design for Disassembly methods aim to integrate disassembly considerations during product design. However, existing tools often lack simplicity and direct applicability for designers. This study proposes an objective Disassembly Complexity Index focusing on product structure and connections, aiming to provide quick and usable feedback for designers and engineers. The proposed index considers factors, such as component geometry, material, and liaisons, providing an objective assessment of disassembly complexity. By automating the calculation of factors, such as handling complexity and removal difficulty, the index streamlines the process and enhances reliability. A case study, through both the first analysis and the redesign, demonstrates the effectiveness of the index, offering insights into its main strengths: (i) computing time reduction, (ii) error reduction, (iii) increased resolution, (iv) results objectivity, (v) analysis repeatability. This approach contributes to product design optimization, compliance with regulations, and improved end-of-life treatment, aligning with sustainable manufacturing practices. The next steps will keep on working on objectivity and the automatic definition of the index; future works may (i) further investigate the rating resolution; (ii) study the automatic retrieval of geometric parts information; (iii) prove the validity and objectivity of parameters by comparing the tables contents and real effort evaluated through dedicated tools (i.e., electromyograph).

Keywords: Design for disassembly , Disassembly complexity , Disassembly index , Eco-design

This study proposes, validates, and optimizes an innovative recoverable composite energy absorber based on a foam-filled hexachiral auxetic architecture made of a visco-hyperelastic polymer. The absorber combined a 3D-printed hexachiral frame made of thermoplastic polyurethane reinforced by waste tire rubber (TPU-WTR) and a strain-rate-sensitive highly compressible open-cell polyurethane foam. While the combination of frame auxeticity with foam filling enhanced localized energy absorption, the specific materials adopted guaranteed full recoverability after impact and the variation of geometrical parameters in the hexachiral topology provided a significant design flexibility. A comprehensive mechanical characterization was performed, based on static and dynamic tests, which were used to calibrate accurately their numerical models. An absorber prototype was produced and experimentally tested, assessing the full recoverability at different impact velocities. Finite element models of the absorber were developed by using the identified material models and were validated from the quantitative and qualitative standpoint. Leveraging the design flexibility of the concept, fully parametric models were used to optimize a configuration for an impact of a 12 kg mass at 9 m/s, with a force constraint representative of the limit referred to a collision between a vehicle bumper and a pedestrian leg. The exploited Gaussian process regression (GPR) surrogate models, combined with a genetic algorithm, were able to predict the largely variable performance indices of the different configurations and enabled the identification of an optimal solution that significantly minimized the indentation of the impactor, while satisfying the force constraint and preserving full recoverability.

Keywords: Energy absorption , Finite element modelling , Hexachiral auxetic structure , Optimization technique , Recoverable absorbers , Surrogate model

Growing regulatory pressure and societal awareness have made sustainability a strategic necessity for industry. However, despite increasing academic research, significant gaps remain between theoretical approaches and their implementation in industrial decision making, where sustainability assessment often serves certification purposes rather than offering continuous improvement. This study proposes a replicable methodological framework that integrates sustainability into corporate design and decision-making processes. The framework includes three main phases: (i) assessment of the current system through Life Cycle Assessment (LCA), (ii) LCA identification of environmental hotspots to define improvement priorities, and (iii) interdisciplinary ideation of circular redesign scenarios using the eco-design approach—a multicriteria, multistage, and multistakeholder systematic process that considers environmental design and development aspects while aiming to reduce adverse environmental impacts throughout a product's lifecycle. The multifunctional team brings diverse expertise, enriches the holistic framework, and enables innovative and creative design proposals while evaluating competing priorities and facilitating trade-offs between life cycle phases. A game-changer has been challenging the status quo, from a conventional design methodology to an environmentally conscious design perspective with emission reduction as the primary core driver. When applied to an industrial case study at ABB S.p.A. on a medium-voltage circuit breaker, the framework proved effective in identifying design modifications that lower environmental impacts without compromising economic or technical feasibility. The results demonstrate the applicability of the Design for Sustainability initiative in promoting continuous product improvement. This methodology fosters an organizational culture that embeds sustainability into industrial innovation across internal ABB business areas and other manufacturing sectors.

Keywords: Design for Sustainability (DfS) , Eco-design methodology , Industrial product development , Life Cycle Assessment (LCA) , Sustainability-driven decision-making

Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-026-38291-3, published online 10 February 2026 The original version of this Article contained errors in the names of the authors Alberto Cavallito, Iacopo Bianchi, Tommaso Mancia, Davide Catania, Marta Rossi & Barbara Marchetti, which were incorrectly given as Cavallito Alberto, Bianchi Iacopo, Mancia Tommaso, Catania Davide, Rossi Marta & Marchetti Barbara. Furthermore, the Supplementary Material files 1 and 2 were duplicated from Supplementary Material 3, the duplicated files have been removed. The original Article and accompanying Supplementary Information files have been corrected.

This paper presents the evolution of the concept of the disassembly map into a standardized, software-readable Digital Disassembly Map (DDM) designed to support virtual product disassembly. The proposed DDM is an XML-based format derived directly from a 3D CAD model, delivering an optimized disassembly sequence for defined target components in product assemblies that minimizes time and cost while ensuring consistency and repeatability. It offers structured, step-by-step guidance to efficiently access target components, facilitating repair, remanufacturing, and recycling operations. The DDM is developed in accordance with the CEN-CENELEC 4555X series standards for product repair and disassembly, and it ensures interoperability with existing engineering and enterprise systems (e.g., PLM, ERP, and production planning tools). This paper describes the methodological framework for generating the DDM from CAD data, as well as the design of its XML schema and library structure. By enabling standardized, software-readable disassembly information, the DDM advances the state of the art in virtual disassembly analysis, represents a step forward in integrating manufacturing and de-manufacturing, enables seamless data integration across the product lifecycle, and supports Industry 5.0 objectives and technologies.

Keywords: demanufacturing , design for disassembly , disassembly map , Industry 5.0 , XML

Electrical and electronic equipment are widespread products that support the daily activities of people, but, due to their complexity, they are responsible for large quantities of improperly managed wastes. In this context, the present paper aims to demonstrate how the PiCo2RAEE digital platform can enhance lifecycle management, data sharing and stakeholders' collaboration in the Electrical and Electronic Equipment (EEE) sector. The PiCo2RAEE platform is composed by three main modules: (i) a design for disassembly tool, (ii) a digital product passport module, and (iii) a second-life tool. The platform architecture is able to implement different but interconnected use scenarios, supporting the creation of a collaborative environment, in which materials and information effectively circulate among the product value chain and whole EEE lifecycle, favoring the creation of circular business models.

Keywords: circular economy , digital product passport , lifecycle management , remanufacturing , repair , reuse