Design for sustainability

As global food demand rises and agricultural labor shortages intensify, robotic automation has become essential for sustainable fruit grasping. Among emerging technologies, ElectroAdhesion (EA) grippers offer a promising alternative to traditional mechanical end-effectors, enabling gentle, low-pressure handling through electrostatically induced adhesion. This paper presents a methodical review of EA grippers applied to fruit grasping, focusing on their advantages, limitations, and key design considerations. A targeted literature search identified ten EA-based and hybrid EA gripping systems tested on fruit manipulation, though none has yet been tested in real-world environments such as fields or greenhouses. Despite a significant variability in experimental setups, materials, and grasp types, qualitative insights are drawn from our analysis demonstrating the potentialities of EA technologies. The EA grippers found in the targeted review are effective on diverse fruits, shapes, and surface textures; they can hold load capacities ranging from 10 g (~0.1 N) to 600 g (~6 N) and provide minimal compressive stress at high electrostatic shear forces. Along with custom EA grippers designed accordingly to specific use cases, field and greenhouse testing will be crucial for advancing the technology readiness level of EA grippers and unlocking their full potential in automated crop harvesting.

Keywords: electroadhesion , electroadhesion gripper , end-effector , fruit , grasping , harvesting , manipulation , picking , robotic gripper

Soft pads are increasingly utilized in robotic grippers for applications requiring secure handling, such as fruit harvesting and delicate object manipulation. However, the interplay of material compliance, surface texture, and environmental conditions (e.g., moisture, wind) complicates selecting the optimal frictional performance. This paper investigates four soft pad textures—flat, cavity, protrusion, and hybrid—fabricated from two silicone rubbers with different Shore hardness (10A and 30A). Their frictional behavior was tested under varying loads (0–200 g), speeds (14–84 mm/s), and wet/dry conditions. A Friction Stability Index (FSI) was then introduced to capture both friction magnitude and slip oscillations. Results show that while flat pads provide the highest mean friction, they exhibit pronounced stick–slip, specially at higher speeds. Textured pads (cavity, protrusion, hybrid) yield more stable friction, particularly in wet, outdoor-like scenarios, albeit with somewhat lower peak friction. The findings underscore the importance of balancing high friction against stability for safe handling.

Keywords: Agricultural robotics , Friction , Gripping , Soft pads

The present work aims to ideate a gravity offloading mechanism for the NASA-ESA mission Mars Sample Return, which intends to bring samples of Martian soil to Earth to study the geologica history of Mars. This mechanism is designed for Ground Testing, in order to conduct tests on Earth at the NASA Jet Propulsion Laboratory to validate a model that could then be further designed and developed to be operated in orbit. Based on an in-depth study of the literature on gravity offloading mechanisms, on the mission, and on mechanisms designs, a few different mechanisms have been designed, and they imply both custom-designed components and components available on the market. Said mechanisms have been preliminarily analyzed and studied, in order to determine a most fitting one based on relevance with respect to the requirements, simplicity of design, and efficiency. The present work shall act as a preliminary study, that shall then be optimized and developed for one or more of the proposed mechanisms in the effort of a successful removal of the gravity load due to the End Effector, based on the constraints analyzed and the ideas proposed, and to promote successful ground testing for the Mars Sample Return mission. Note: this paper refers to efforts made in the 2022-2023 period, all mission aspects and status shall be considered at said time period. Hence, some of the mission's statements shall result outdated at the time this paper is written. The decision to implement Mars Sample Return will not be finalized until NASA's completion of the National Environmental Policy Act (NEPA) process. This document is being made available for information purposes only.

Keywords: Computer-Aided Conceptual Design (CACD) , Computer-Aided Engineering , Design Optimization , Design Process , Mechanism Design

In this paper, a Laser Powder Bed Fusion (LPBF) process is studied comparing the design of a Diesel piston with and without topological optimization. Starting from a commercial piston, an equivalent model and a topologically optimized one are designed, additively manufactured and post processed to set-up the starting framework to evaluate how Additive Manufacturing (AM) may impact the sustainability of an automotive product. Results highlighted a 27% mass reduction with a reduction of AM process duration of about 41% passing from a process lasted about 49 h for an AM piston with the original shape to about 29 h for 3D printing the topologically optimized one. Limits and further investigation are then discussed also in the perspective of a future assessment via LCA.

Keywords: Design for additive manufacturing , LCA , Lightweight design , LPBF , Topological optimization

Sustainable product design (SPD) is a critical approach for mitigating environmental impacts and promoting socioeconomic welfare. This paper explores stakeholder behavior patterns throughout the life cycle of sustainable design, emphasizing their role in shaping product sustainability. Combining natural and cognitive resources within design methodologies elevates SPD beyond mere functionality, ensuring environmental stewardship and user satisfaction. Leveraging the TASKS framework illuminates stakeholder behavior complexities, optimizing cognitive resources for sustainable outcomes. Addressing implementation barriers and leveraging facilitators unlock the driving force behind stakeholder engagement, fostering active participation in sustainable design practices. The study envisions a future where informed stakeholder behaviors inherently promote sustainability, emphasizing the pivotal role of stakeholder behavior in achieving sustainable product design excellence. Through comprehensive analysis and strategic interventions, this research seeks to bridge the gap between sustainability objectives and practical implementation in product design, ultimately fostering a more sustainable future.

Keywords: Behavior pattern , Cognition , Stakeholder , Sustainability , Sustainable product design

Cement production is responsible for approximately 8% of global carbon dioxide emissions, creating an urgent need for sustainable material alternatives. This study investigates the incorporation of rice husk ash as a partial replacement for cement and sand in concrete to enhance durability and reduce environmental impact. A database-driven approach was applied, analyzing over 1000 data points from the literature published between 1996 and 2020. The analysis focused on correlations among key durability indicators, including chloride resistance, acid resistance, electrical resistivity, and porosity. Based on these relationships, optimal rice husk ash replacement levels were identified as 30% for cement and 20% for sand. Findings show that rice husk ash incorporation significantly improves durability and can reduce carbon dioxide emissions by up to 40%, particularly when combined with pozzolans such as fly ash and metakaolin. The study highlights that analyzing correlated durability factors is crucial for refining optimal replacement levels. This research provides a data-driven pathway for incorporating rice husk ash as an agricultural waste product into concrete and supports circular economy and resource conservation efforts. These contributions ultimately help attain the UN Sustainable Development Goals and wider sustainability targets.

Keywords: concrete durability , data-driven approach , low carbon cementitious materials , resource efficiency , rice husk ash

The automotive development of Electric Vehicles (EVs) is accelerating due to environmental concerns and technological advances. The complexity and performance demand of EVs require the use of Silicon-Carbide (SiC) technologies for the related efficiency, high-temperature resistance, and robust dynamic switching. To ensure the high performance of electric vehicles, the rigorous screening of the delivered SiC devices plays a pivotal role. Defects in SiC production can cause failures that directly affect the performance of electric vehicle engines, particularly in the traction inverter subsystem. To identify these defects early, advanced visual screening through optical microscopy and X-ray approaches has been proposed by leading car makers. These screening methodologies require advanced technical expertise and are still prone to significant errors, even when performed manually by experienced operators. To address these inefficiencies, the authors propose an ensemble deep learning pipeline for performing a robust automated visual inspection of SiC devices. By combining Multi-Head Attention blocks with adaptive input data distortion compensation through Jacobian regularization within convolutional architectures, the proposed model leverages global context and local feature maps, enhancing accuracy and robustness in multimodal defects detection. The proposed combined approach, tested on ACEPACK/TPACK DRIVE SiC power modules provided by STMicroelectronics, achieved an average accuracy of approximately 93% in both methodologies.

Keywords: Deep Learning , Defects analysis , Silicon-Carbide

The miniaturization and integration of micro-electromechanical systems (MEMS) have progressively expanded the capabilities of advanced prosthetic hands, enabling not only the replication of human sensory and motor functions but also the implementation of sophisticated mechatronic control, precise manipulation, and adaptive responses to environmental interactions. The aim of this scoping review is to systematically map and evaluate current research on MEMS-integrated prosthetic hands, highlighting how MEMS sensors and mechanical modelling approaches contribute to dynamic control, biomechanical performance and user-centered functionality. Comparative analyses of different modelling techniques and MEMS applications indicate that MEMS-based sensing systems substantially improve the mechanical performance of prosthetic hands by enabling accurate force modulation, enhancing motion stability during dynamic tasks and supporting efficient signal acquisition for real-time control. These features lead to more precise control, smoother movements and enhanced dexterity during activities of daily living (ADL), broadening the functional capabilities of the devices. Microsurgical and neural interface aspects were also examined, including physiological considerations relevant to neural integration and common challenges related to prosthetic implantation, such as potential immunological responses to materials. The increasing role of MEMS in the development of smart, biomimetic prosthetic hands underscores new opportunities for creating highly adaptive devices, optimizing dexterity and environmental interaction and ultimately improving users' quality of life.

Keywords: biomechanical modelling , dynamic control , mechatronic systems , MEMS sensors , prosthetic hand , smart hand prosthesis , smart MEMS

Eco-design for metal additive manufacturing (AM) can be pursued in different manners, including lightweight design and the reuse and recycling of powders. However, these options are contradictory due to the decline in characteristics in recycled and reused powders. This study proposes a parametric model of the environmental impact of AM process, including raw material extraction, metallurgical process, scrap recycling, melting, atomization for the production of the powders and printing. The model consists of an objective function that has been derived from the Life Cycle Assessment (LCA) methodology applied to the AM process. Each contribution of the function has been parameterized, including decision variables about mass reduction and the percentage of use of recycled and reused powder. The eco-design for AM is pursued through the minimization of the objective function (i.e., the environmental impact of the process) by obtaining the most suitable values of the decision variables. The proposed model has been applied to an illustrative example related to the AM of a component made by Ti6Al4V.

Keywords: Additive manufacturing , Eco-design , Parametric LCA , Parametric model , Recycled powders

Electrification of the vehicle fleet is a key strategy for decarbonizing transportation. However, electric vehicles (EVs) typically have higher weight than traditional vehicles, resulting in increased energy consumption, diminished driving range, and raised non-exhaust emissions. This study presents a modelling framework to assess the benefits of EV fleet lightweighting on driving range, greenhouse gas emissions, and air quality. The framework integrates a longitudinal vehicle dynamics simulation tool (TEST) and an integrated assessment model (MAQ). TEST determines the energy consumption, the traditional hydraulic braking system energy, and the maximum driving range as a function of the vehicle's weight. MAQ estimates the reduction of greenhouse gases (including carbon dioxide) and particulate matter emissions, and the impact on PM2.5 and NO2 concentrations of different scenarios defined by assuming a fleet with 10 % and 25 % of electric vehicles. The study focuses on the Po Valley, a vast area in northern Italy characterized by some of the highest levels of air pollution in Europe. Results show that increasing the fleet electrification from 10 % to 25 % without altering the average vehicle's mass reduces the NO2 levels in urban areas by up to 9.3 %, but increases non-exhaust emissions, with possible local negative impact on PM2.5 levels if the energy production is supplied by natural gas. When combined with vehicle lightweighting, electrification yields consistent benefits, including improved driving range, PM2.5 concentration reduction up to 3 % in major and most populated cities, and CO2 emissions decrease by up to 3 %. The framework provides a methodology for evaluating the environmental trade-offs of EV diffusion at the regional scale and the role of EV weight for maximizing the co-benefits on the environment.

Keywords: Air pollution , Emission abatement , Energy consumption , Integrated assessment modelling , Vehicle lightweighting

This study introduces a comprehensive life cycle model and tool to support the ecodesign of 3D printed components. The model, developed for Fused Deposition Modeling (FDM) technology, integrates both product and process-related parameters and encompasses all life cycle stages: (i) raw material production, (ii) part manufacturing, (iii) use, including application context and maintenance, (iv) end-of-life, and (v) transport. The model enables the predictive evaluation of environmental and economic performance through life cycle assessment (LCA) and life cycle costing (LCC), respectively, and using engineering design parameters as input. The FDM life cycle tool aims to guide decision-making during the early design phases by integrating sustainability metrics alongside conventional design requirements. It constitutes the kernel of a broader approach (called eDAM—ecodesign for Additive Manufacturing), which incorporates life cycle models, extensive data inventories, and technology-specific ecodesign guidelines. However, the development and implementation of the framework fall beyond the scope of the present study. The FDM model's applicability is demonstrated through a case study involving the production of a lightweight aerospace component (aircraft interior panel). The bio-inspired optimized 3D printed part exhibits notable reductions in greenhouse gas emissions and life cycle costs, confirming AM's potential for sustainability improvements in dynamic applications, including aeronautical systems. This tool outperforms other commercial tools used for environmental and cost assessment in additive manufacturing, in terms of built-in functionalities, product life cycle phases, and database breadth. The research highlights the scalability of the eDAM approach and its capacity to support engineers in achieving a balance between technical, economic, and environmental performance. Future work will extend the model to additional AM and conventional manufacturing technologies, refine specific life cycle phases (e.g., use-phase and end-of-life scenarios), and evaluate the usability and implementation efficiency of the model within the broader ecodesign framework.

Keywords: Additive manufacturing , Design for sustainability , Ecodesign , FDM , LCA , LCC , LCE , Life cycle assessment , Life cycle cost , Life cycle engineering

Classification is a crucial task for reintroducing end-of-life fabrics as raw materials in a circular process, thus reducing reliance on dyeing processes. In this context, this review explores the evolution of automated and semi-automated colour classification methods, emphasizing the transition from deterministic techniques to advanced methods, with a focus on machine learning, deep learning, and particularly Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). These technologies show potential for improving accuracy and efficiency. The results highlight the need for enriched datasets, deeper AI integration into industrial processes, and alignment with circular economy objectives to enhance sustainability without compromising industrial performance. Tested against a case study, the different architectures confirmed the state-of-the-art statements demonstrating that they are effective in classification, with better performance reached by CNN-based methods, which outperforms other methods in most colour families, with an average accuracy of 86.1%, indicating its adaptability for this task. The adoption of the proposed AI-based colour-classification roadmap could be effective in reducing dyeing operations, lower costs, and improve sorting efficiency for textile SMEs.

Keywords: circular economy , CNNs , colour assessment , textiles

The total quantity of food waste in Europe has been estimated at around 88 million tons per year. About 42 % is thrown out by households, of which 60 % would be avoidable by increasing users' awareness. The present paper aims to develop an integrated system to reduce household food waste and improve the end-users' lifestyle in terms of health and well-being. A smart fridge able to track the stocks, a web application and a set of related services have been designed and prototyped to guide the user in the proper storage of food and support him/her in purchase planning and food preparation. The system was positively evaluated in terms of usability, it is use contributes to both environmental and economic benefits, leading respectively to a reduction of environmental impacts of about 21 % and a yearly savings amounting up to 285 €, in comparison with a traditional system.

Keywords: environmental sustainability , household food waste , smart fridge

The current industrial environment requires companies to develop innovative solutions to adapt production processes and procurement strategies to the challenges of global supply chains. The Italian ceramic industry, characterized by high resource consumption and a complex supply chain, demonstrates vulnerabilities in ensuring the continuity of its operations. To address these challenges and mitigate supply chain risks, the circular economy paradigm has been adopted, tailored to the specific context, and implemented with an innovative perspective. In this context, this paper proposes a transdisciplinary methodological approach, integrating knowledge from supply chain management, digitalization, and sustainability science, and leveraging digital technologies in a real-world setting. The research focuses on defining a methodology finally aimed at developing a Digital Supply Chain Twin (DSCT): A dynamic virtual representation of a real supply chain that predicts its behaviour. Achieving this goal requires building a robust benchmark of data and objective functions to represent supply chain components, performance metrics, and flows. Indeed, only a validated and reliable DSCT can enable accelerated and automated 'what-if' analyses related to sustainability performance. Procurement decision-making will thus benefit from an intuitive, fast, and automated tool to evaluate supply chain performance and potential modifications, integrating economic, environmental, and social sustainability perspectives.

Keywords: Circular Economy , Digital Supply Chain Twin , Supply Chain Management , Sustainability , Transdisciplinary Engineering

Plastic waste management presents a growing global challenge, and recycling is an important strategy for mitigating plastic's environmental impact and promoting circularity. Although centralized recycling chains are well established, the potential of small- and medium-scale distributed recycling facilities to improve waste collection in urbanized areas and foster recycling in remote regions remains underexplored. This study uses life cycle assessment (LCA) to compare the environmental performance of three recycling chain scenarios for rigid high-density polyethylene plastic packaging waste: centralized, semi-distributed, and fully distributed. Using the ReCiPe Midpoint (H) method and data from a recycling facility in Northern Italy, this study applies the product perspective calculation method in which recycled plastic replaces virgin plastic, and environmental credits are assigned accordingly. The results show that the semi-distributed scenario offers the most effective compromise between logistical efficiency and operational scale and achieves the lowest environmental impact, with an average reduction of 48 % ± 13.9 % across 18 impact categories and savings of 1.42 kg CO2eq per kilogram of recycled granulate compared to the centralized system. The distributed scenario is second, while the centralized system demonstrates the highest environmental burden, primarily because of the impact of long-distance logistics.

Keywords: Circular Economy (CE) , High-Density Polyethylene (HDPE) , Life Cycle Assessment (LCA) , Plastic waste management , Plastic waste recycling

The article “Reduction of energy consumption and environmental impact in ceramic sanitary ware production through fluxing agents: a case study”, written by Emanuele Mingione, Marco Marconi, Gianluca Rubino, Daniel Salvi was originally published Online First without Open Access. After publication in volume 139, page 2671–2686 the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the

The use of passive exoskeletons in industrial settings has gained growing interest as a means to reduce muscle fatigue and prevent work-related musculoskeletal disorders. However, translating laboratory methods into realistic occupational environments remains a challenge. This study presents a modular and wearable-sensor-based experimental protocol designed to bridge this gap by enabling the evaluation of exoskeletons in both static (STC) and dynamic (DYN) tasks while preserving natural movement variability. A total of 52 participants, including both men and women, completed tasks with and without two different passive exoskeletons, while their motor activity was assessed using surface electromyography (sEMG) and inertial motion sensors. The protocol incorporates key EMG-based metrics - Root Mean Square (RMS) and Hilbert Median Frequency (MDF) - that effectively quantify muscle activation and fatigue, along with subjective Perceived Fatigue Scores (PFS) and a task performance metric (Screwing Velocity, SV). The results confirm that the exoskeletons significantly reduce muscle activation and perceived fatigue without impairing task performance. The proposed methodology, combining rigorous metrics with wearable and non-invasive instrumentation, offers a robust framework for evaluating fatigue in both STC and DYN tasks and usability in both laboratory and field settings. This protocol represents a valuable tool for both research and industrial evaluation, facilitating the evidence-based integration of exoskeletons into real-world industrial workflows.

Keywords: electromyography , exoskeleton , fatigue , sustainable manufacturing , wearable robotics

Awareness of the issue of environmental sustainability of industrial products and processes is becoming increasingly topical, especially in EU countries, and with it the need to assess and certify the environmental impact of production activities. Among the most popular practices are methods based on life-cycle assessment (LCA), both current and prospective, which require the research of a vast amount of data, accurate, and up-to-date. Whereas in the past the scientific community relied on data reported in the scientific literature and in European projects, there is now increasing recognition of the importance of including patent information, which provides a more detailed view of the scalability of processes, use and end of life of a product. One of the main challenges in making the best use of patents as a source of technical data is managing the complexity of the database and language structure of the documents themselves. A crucial step in facilitating access to this data is through the accurate identification and classification of patents dealing with sustainability to circumscribe the search to a more delimited and accurate scope. Although the criteria for classification are well defined by European regulations, the techniques for actually operating this classification are still highly inefficient. In this paper, we compared traditional patent search methods by keywords and by using Cooperative Patent Classification, in terms of precision and recall, then proposing as an alternative the use of AI tools, such as next-generation NER using a bidirectional transformer encoder and RAG, to overcome the limitations that exist with traditional approaches. An exemplary case is conducted on a pool of patents on metal powders for AM.

Keywords: AM powder , Artificial intelligence , EU environmental objectives , Patents

Compliant mechanisms are systems that, using a single body, are able to perform functions typical of a traditional assembly with hinges, bars, and connecting parts. Since many structures in nature are designed in a logic of compliant mechanisms, it is reasonable to assume the enormous advantages, also in terms of sustainability, of this way of designing in engineering. To date, one of the main barriers to their diffusion is the lack of tools that can suggest the right topologies to the designer (pointers to geometrical effects), but also easy tools for their appropriate dimensioning and simulation. In contrast, additive manufacturing, thanks to its high compatibility with complex shapes and the recent ability to print multi-material parts, is bringing them back into the spotlight. The aim of this paper is to show the potential of DfC—Design for Compliant for the conception of more sustainable products, by presenting an exemplary case in which a comparison was made between the environmental impact of a traditional multi-part door lock made using standard manufacturing processes and a compliant system capable of replacing the former. To achieve even better results, the DfC-inspired concept was printed by multi-material additive manufacturing, using a combination of two different polymeric materials with very different Young's modulus to simulate different local behavior, both rigid and elastic. This substitution resulted in a reduction of CO2 equivalent impact around 50%.

Keywords: Design for compliant , LCA , Multi-material additive manufacturing

In the recent years, the production of titanium powders has increased considerably due to the increase in uses, especially with additive manufacturing, in various application fields, including aeronautics and biomedical. Consequently, it is necessary to estimate the environmental sustainability of production processes while also considering the rapid technological evolution that characterizes them. This can be done with the prospective life cycle assessment (LCA) methodology, which estimates the future environmental impacts of an emerging technology or a new technology still in the R&D phase. This study generates prospective life cycle inventory (LCI) data of titanium powder production for additive manufacturing made with recent developments of Electrode Induction Gas Atomization (EIGA) reactors, which can be used in the prospective LCA. All data for the prospective LCI relating to argon consumption, water consumption, and electrical energy consumption of the EIGA reactor were extracted from the patents using a partly new approach. These data concern the experimental results (expressed through data ranges) obtained from laboratory-scale tests, qualitative and quantitative information to support the scale-up of the experimental data, and information to evaluate the data reliability.

Keywords: Additive manufacturing , Eco-design , Prospective LCA , Prospective LCI , Technological forecasting