Design for sustainability

This work investigates the application of Augmented Reality (AR) in assisting industrial companies in facilitating the adoption of vertical-axis wind turbines for domestic use. Despite its growth, high upfront costs limit broader wind turbine adoption, especially in urban areas. AR is an emerging technology that can enhance the spread of renewable energy. In the wind energy sector, it is frequently integrated with data monitoring to support the decision-making process. In the literature, many studies address this topic, focusing primarily on improving performance in industry rather than evaluating application usability and user experience. Therefore, our study intends to explore an AR application's usability and user experience integrated with data monitoring to create customer service in the wind energy sector. We developed an application targeted to potential customers planning to adopt wind power to produce electricity within their homes. The aim is to encourage the spread of this renewable energy in an urban setting by facilitating the choice of the most suitable wind turbine based on the local weather conditions and the needs of the individual. To achieve this goal, specifically, we paid significant attention to data visualization, including the estimated power output of the chosen turbine, to better engage the user through customizable graphs rather than traditional textual information. We evaluated the usability and user experience of the implemented application through a user study involving 10 potential buyers of a wind turbine to be installed in their property. The results reveal that the usability is 'excellent' with a positive user experience evaluation, especially in attractiveness and novelty. Thus, our proposal turns out to be promising in creating customer service in the wind energy sector and other branches of renewable energy, such as solar-powered systems.

Keywords: augmented reality , data monitoring , data visualization , user acceptance , wind turbine

Environmental sustainability-oriented design is becoming increasingly important in the industrial field partly because of the effects of climate change. Sustainable development-oriented choices are most effective at the early design stage. The design team must be able to assess approximately and quickly the environmental impact early in the design phase. From these motivations comes the need for a method that quickly and with few parameters can estimate the product environmental impact during the conceptual design phase. Machine learning techniques appear to be well suited to meet this challenge. Machine learning is an established research topic in Industry 4.0 and its adoption is increasing. The integration of machine learning within conceptual design quickly facilitates the approximate assessment of environmental impact through high-level data. In this paper, a method is proposed to obtain a parametric model for the environmental impact assessment of manufacturing components at the early design stage. It allows consistent considerations concerning environmental matters, albeit little information available during design phase.

Keywords: Data Science , Design , Life Cycle Assessment , Machine Learning , Sustainability

Despite the availability of multiple CBMs that merge economic sustainability with environmental considerations, each enterprise faces its own challenges in the transition from a linear to a circular economy. This work proposes an approach for organizations to determine which CBM aligns best with their practices, starting from available design tools, and being supported by the correlation matrix serves as allowing design tools to be integrated among core resources. With the support of the proposed approach, designers can extend their overview over multiple stages of a product's lifecycle. The paper focuses on three design tools and identifies the lifecycle extension strategies they enable. The application provides an example of the implementation of the approach and involves three tools: the first quantitatively evaluates the environmental and economic sustainability of durable products; the second interrogates virtual 3D models and identifies recycling criticalities; the latter supports the research of new supply chain partners to avoid landfilling and incineration. The correlation matrix correlates the functionalities of the tools and ecofriendly models to commercialize goods and offer services, scheduling and prioritizing the introduction of innovative aspects and business models. Future work could introduce quantitative milestones for a more robust evaluation.

Keywords: Business Model innovation , Circular Business Models , Design for circular economy , Design tools

The recent trend in the textile setting highlights the need for more effective and affordable processes to make the garment market more sustainable. Secondhand garment reuse and fabric recycling are two virtuous pathways toward material saving and sustainable transition. Sorting processes are fundamental enablers of this transition. The main operations involved are identification, classification, and quality assessment. This work proposes a collaborative robotic solution for the first identification and classification of garments to support human experts, along with the following assessment operations for garment and fabric quality and the identification of textile fibers. The proposed approach is based on a collaborative system integrating robots, machine vision models, and dedicated gripper systems to perform the picking actions required to identify and classify garment categories. The first experiments demonstrate the feasibility of the proposed approach as well as the self-learning capability of the system using the detection model continuous training.

Keywords: Garment detection models , Garment sorting , Gripper for garment picking , Human-robot collaborative system

The design of modern production systems is requiring more and more integration of human-in-the-loop principles introduction of new working paradigms, where humans and machines cooperate and interact with each other. Such practices are reinforcing the need for human-centric smart manufacturing (HSM). Today, human-cyber-physical systems (HCPSs) allow to intrinsically meet the industrial complexity and bring insights in the development and implementation of HSM. In this context, the growing interest of academics and practitioners on the three key concepts of “sustainability, human-centric, and resiliency” related to the Industry 5.0 (I5.0) framework is justified. The aim of this study is to present a methodological approach for the design of adaptive human-cyber-physical systems to meet the I5.0 objectives. Starting from the analysis of the as-is user and process-related conditions, this innovative closed-loop framework guarantees a step-by-step tuning logic according to specified objectives. A use case concerning a quality control procedure for the Oil&Gas sector is proposed, where a preliminary HCPS prototype has been developed and tested. The solution provides a contextual support throughout all the value chain to the diverse activities carried out by design engineers up to end-users by offering a valuable operator's user experience (UX). The main results suggested that the conceived strategy overcomes traditional limitations of the technical drawings by integrating model-based engineering with human-centred design (HCD) principles. The systematic introduction of HCPS allows to redesign the human–machine interaction according to new paradigms of collaboration between the humans and the smart factory.

Keywords: Augmented reality , Human-cyber-physical system , Human-in-the-loop , Human-machine interaction , Industry 5.0

[No abstract available]

The manufacturing industry plays a crucial role in addressing sustainability challenges by integrating eco-friendly practices into product design. Employing a System Thinking methodology, this research comprehensively considers the interconnected components of a product, entities involved, and their interactions. The study focuses on developing a corporate sustainability tool for the entire supply chain, leveraging Digital Twin (DT), Virtual Reality (VR), and Augmented Reality (AR) to facilitate eco-design. Benchmarking existing monitoring systems and case studies in sustainable product design identifies gaps and opportunities, establishing guidelines for integrating digital technologies. Within the industrial context, the designer implements the system to ensure the development of eco-compatible products (Green Design), considering not only environmental impact, but also social change. The aim of this paper is to design a corporate sustainability tool based on a digital product model enriched with specific product and process information. This tool serves as an instrument for the company to evaluate environmental, social and governance data, make strategic sustainability decisions, and create company dashboards that facilitate the visualization and analysis of this data. The creation of a Digital Product Passport (DPP) will offer valuable information on the sustainability aspects of the product, benefiting end users who utilize the product. Conducted within a co-financed Ph.D. program with an Italian industrial machinery manufacturer, the research addresses challenges in academia-industry collaboration, enhancing the practical application of designed systems. The findings have significant implications for the manufacturing industry, contributing to sustainable product design practices and establishing a standardized framework for assessing and communicating product sustainability.

Keywords: Corporate sustainability digital tool , Green Design , Sustainable product design , System thinking , Transdisciplinary engineering

Purpose: Some prospective life cycle assessment (LCA) studies obtain information from patents, albeit without exploiting their full potential. The objective of this study is to show which data and information can be retrieved from patents to inform practitioners when conducting a prospective LCA of an emerging technology. Methods: This study suggests which patent analysis techniques can be used to support which prospective LCA challenges, by reviewing patent analysis techniques and classifying the information that can be extracted from them according to those required to meet prospective LCA challenges. To illustrate the usefulness of the suggested techniques, a case study on solid oxide fuel cells is presented. Results and discussion: The analyses of patent geographical jurisdiction, publication trend, maintenance costs, citations, and infringement can be used to define geographical and temporal scope and to select technology alternatives. Function(s), quantitative data, and information about scale-up and technological trends can be extracted from patents and used to predict function(s) of the new technology, fill the prospective life cycle inventory (pLCI), and choose existing LCI datasets. However, limitations of patents that could prevent their use in prospective LCA are as follows: (i) some information can be intentionally distorted to hinder competitors; (ii) patent bibliometric indicators to evaluate the future success of patented technology on the market can be overstated by patents of well-known owners that receive more citations and infringements albeit with no greater chance of future development; (iii) patenting to block competitors rather than to develop a new technology; (iv) the lack of significance of certain data due to the too low technology readiness level (TRL) of the prototype from which they were obtained; (v) a less than rigorous data examination process; and (vi) patents are not very helpful to quantify emissions. Conclusions: We show how patents can be used to support prospective LCA when the assessment cannot count on the support of technology experts. We highlight how it is necessary to pay more attention, compared to the current practice in prospective LCA, to the peculiarities of patent prose and the legal and strategic use of patents by companies.

Keywords: Life cycle assessment , Patent analysis , Prospective LCA , Prospective LCI , Technological forecasting

Electric and hybrid powertrains are steadily gaining popularity, showcasing their efficacy in reducing greenhouse gas emissions and pollution, particularly in urban environments. This also applies to medium and heavy-duty vocational trucks. Truck manufacturers have been expanding their electrified portfolio and some of them have already announced their plans to phase out fossil fuels. Vocational trucks are essential for the industry of commercial vehicles, represent an extremely heterogeneous class, and are often upfitted by third-party companies. In general, vocational trucks are designed for specific jobs. Typically, they are driven on short routes, but they may work for longer hours in comparison to freight transportation vehicles. Most importantly, among the broad category of vocational trucks, some vehicles greatly exploit power take-offs to drive auxiliary systems, like refuse trucks, utility trucks, cement trucks, and sweeper trucks. The benefits resulting from the kinetic energy regeneration of urban driving are undeniable and well-documented in several studies. Electrified short-haul, delivery, and box vocational trucks are indeed becoming widely accepted, but the auxiliaries of these trucks do not need a significant amount of energy. In this regard, few studies analyze the effect of auxiliary loads, even if they may require significant power and cause considerable drifts in energy consumption estimation. Indeed, velocity-based driving cycles are commonly used to assess the energy savings of electrified powertrains, but they only focus on analyzing the power needed for traction purposes. By focusing on vocational trucks that greatly rely on power take-offs and auxiliary power flows, this study shows an overview of the existing advancements in electric and hybrid vocational trucks, drawing on both academic and industrial instances, while explaining their different needs. Finally, this study analyzes efforts from both academia and industry to generate standardized duty cycles that can be used to assess the energy consumption of medium and heavy-duty vocational trucks, avoiding the need to replicate unique duty cycles that are measured on specific vehicles and environments.

Achieving sustainable mobility requires the implementation of alternative and carbon-free technologies, especially in the sector of heavy-duty vehicles where powertrain electrification is challenging due to the high loads and long distances involved. In this context, hydrogen proton exchange membrane (PEM) fuel cell technology is considered a promising power source for heavy-duty hybrid electric vehicles. At the fuel cell level, the membrane electrode assembly (MEA) degradation and the system thermal management remain two major areas of research, that can be addressed not only with the development of new materials but also with the implementation of optimal control strategies. Working under operating points that lead to MEA aging and performance degradation can reduce the lifetime of the fuel cell with repercussions on the vehicle's total cost of ownership. Typical fuel cell powertrains are hybridized in a parallel configuration with a battery, which requires solving an energy management control problem to define the optimal power split among the two power sources. For a given powertrain design and sizing of the components, different control strategies can greatly impact the performance of the system and its durability. In this work, a fuel-cell hybrid electric heavy-duty truck is modeled, and Dynamic Programming is used to find the optimal control strategy for the fuel cell operation that minimizes hydrogen consumption. At the same time, by limiting the current ramp rate and fuel cell shutdowns, the aim is to reduce fuel cell aging, enabling operation under minimal degradation conditions and higher efficiencies. Dynamic Programming off-line simulations provide an optimal benchmark control strategy for the fuel cell, that can be leveraged for the development of real-time implementable controllers. The simulation results show that introducing limitations on the current ramp rate and fuel cell shutdowns slightly affects energy consumption while greatly reducing the aging and degradation of the fuel cell.

In the present market, Computer Aided Design and Computer Aided Manufacturing represent considerable tools to achieve better design and optimize the manufacturing phases. To customize and tailor these tools to the company's needs, knowledge-based engineering solutions have been developed. The present paper proposes a method to support designers in the optimization of environmental sustainability aspects of their products, through a Knowledge Based Engineering approach. It allows the identification of design criticalities under the environmental issue, and on a life cycle perspective, supporting designers in their activity and favoring the knowledge use and re-use. The approach proposed was applied to develop a prototype version of a tool. It was then applied in a case study of an Italian manufacturer of professional espresso coffee machines to analyze and improve the product recyclability and disassembly level.

Keywords: Design for Environment , Design for X , Design rules , Knowledge-based engineering , Sustainability index

Preface and Acknowledgements (Editorial)

Keywords: Editorial

The paper presents a preliminary design activity and virtual prototyping of an innovative boat equipped with hydrofoils and hybrid propulsion, with the aim of extending the foil technology from the field of competition boats to recreational day-cruiser yachts and creating a craft with minimal environmental impact. Hydrofoils allow boats to rise from the water, greatly reducing resistance and increasing performance. The current work dealt with the preliminary design of a daysailer with foil technology and hybrid propulsion that allow to combine green and comfortable navigation both under sail and motor and that, when required, can sail in a more performing way by exploiting the foil technology and the thrust of the wind. After having deepened the theory and physics of sailing on foils, a MATLAB code was created to integrate the stability equations that characterize hydrofoil sailboats: connecting the acting forces and allowing to define the dimensions of the geometries, the code was fundamental in speeding up the iterative preliminary design process. The next step was to model the geometry of the hull and the appendages in the CAD environment and, subsequently, the wing movement mechanism so that it could both manage the incidence of the wings and retract the foils when the boat is moored. The hull, profiles, and wings were subsequently placed in a CFD and VPP virtual environment for testing their resistance. Future developments will include a detailed design and the physical prototyping of a first boat for water testing.

Keywords: Green , Hybrid , Hydrofoil , Virtual prototyping

Nowadays, energy efficiency of industrial plants is an issue of primary concern. This research aims at minimizing the Energy Consumption (EC) of Industrial Manipulators (IMs) leveraging on Eco-programming strategies. In particular, building upon well-known methods for robot trajectory planning, a novel time-scaling approach is proposed, which employs a non-linear scaling function to better optimize EC. In this scenario, a Graphic User Interface (GUI) has been realized to ease the implementation of the presented algorithm, offering a fast and user-friendly tool to be used by robotic cell designers. Different manipulators models can be uploaded in the GUI to then automatically run the non-linear optimization process and retrieve a minimum-energy trajectory. As an instance, a 6 Degrees of Freedom IM has been considered; the obtained results have been compared with other methods known from the literature and validated through a commercial tool. The proposed method is verified to be more effective for energy-intensive trajectories, possibly reaching EC savings of more than 10 %.

Keywords: Eco-programming , Energy efficiency , Graphic User Interface , Industrial robotics , Smart manufacturing , Trajectory scaling

The constant growth in global energy demand, and corresponding prices rise, is soaring new engineering methods for reducing energy consumption in manufacturing processes. For decades, industrial robotics have been enabling quality enhancement of end-products by using flexible manufacturing processes, without much concern to energy cost, but now a makeover is happening. Calls for sustainable and green manufacturing processes are being promoted across the globe with the aim to produce more goods and with less consumption. In this paper, a new method is presented focusing on the optimization of energy intake by industrial robots, without the need to change their hardware set and just modifying the trajectory planning of the end-effector. A test case scenario consisting of a robotic cell with 4 pick-and-place manipulators has been set to validate the method. Starting from a pre-scheduled trajectory, robots are moved at the highest speed and acceleration and, by performing the sequenced operations, the optimal trajectories are defined. The goal is to find a trajectory that minimizes the time cycle and the total energy consumption, while avoiding collisions between the robots' links: comparing the results thereof to those of the pre-scheduled trajectory, noticeable energy saving has been obtained along with possible decrease of the cycle time.

Keywords: Computer-aided engineering tools , Energy efficiency , Robot scheduling , Trajectory planning , Virtual prototyping

‘Repair' in the design process of products can prolong the life cycle of parts: this is substantiated by a few examples that put this ideology into practice. Among many other products, home printers could be a good example, with huge numbers of printers ending up in landfill after a relatively short life; often due to blocked print heads that are either impossible or too expensive to replace. The act of fixing things can both prolong the life of an artifact, and create new values through the process of engagement for its users. However, the prohibitive cost of repair makes it inaccessible or unfavorable this practice for many. In this paper a preliminary approach to Design for Repair is proposed, in order to virtually test an industrial case study and to show a comparison between a product, that was intended to disposal after a failure of some components, and the same product re-designed, by taking into account how to repair parts in easy and effective way. Redesign of parts in some cases can be very effective and the virtual test can be easily reproposed in practice, for industrial products. Advantages in the repair of parts is evident in terms of sustainability and circular economy pursuit. This paper suggests a sequenced method to approach the Design for Repair and provide the virtual model of a re-designed solution that could replace the previous one in order to make the repair of components easy and effective. The economic analysis on the effective convenience of repair faced to the disposal of a product was not developed within this context.

Keywords: Circular economy , Design for Disassembly , Design for Repair , Gearmotor , Sustainability

Nowadays, relevant design challenges include the need to use sustainable materials that allow designing products with a lower environmental impact. The construction sector is currently undergoing a slow but continuous change towards the use of sustainable materials. One of the most generalized methods for assessing sustainability is the Life Cycle Assessment (LCA), which aims to analyze and compare product alternatives to minimize the environmental impact of a product or a process. In this work, the LCA method has been applied to a mobile tiny house prototype built with sustainable materials, such as hemp bricks or wood. The ISO 14040 and the EN 15804 standards were followed. The life stages calculated are hemp cultivation and processing, production of the hemp brick, construction of the tiny house and transportation. The results show that the most significant impact comes from the production of titanium sheet metal, wood, bricks, and the transport of raw materials. The results suggest that hemp bricks are a sustainable alternative, but they need to be combined with the right manufacturing and transportation processes. This research offers insights into how to introduce sustainability in the building sector through early design decisions, such as the selection of materials

Keywords: Biomaterials , Life Cycle Assessment , Sustainable design , Tiny house

The building design sector can benefit from the new opportunities offered by studies investigating people's perception of urban and architectural spaces. It emerges from the literature that certain elements of environment settings and buildings such as edges, landmarks and materials can affect perception. However, their impact on people's visual perception is still unclear, also because of the difficulty to report consciously what has been experienced. Technologies and tools such as mobile eye tracking (ET) give a chance to get insights into visual behaviors in real environments. In this work, the authors had the chance to conduct an experiment, where ET was used in a physical space of a tiny house prototype, which was the result of a research-industry cooperation project about real-world laboratories. An experimental activity involved 26 volunteers, who were asked to visit and freely observe the interior of the tiny house wearing ET glasses and fill in an evaluation questionnaire at the end of the visit. The first-view experience recordings of each visit of the tiny house were thereafter processed to acquire data to be put in relation with questionnaires' outcomes. Preliminary statistical analysis showed potential relationships between areas of interest (AOIs), namely distinguishable elements of the tiny house, data, and evaluations. The time spent on some AOIs positively or negatively affected the evaluation reported by the questionnaire. It is also worth noting that, surprisingly, some AOIs deemed to be secondary affected the questionnaire ratings more significantly than the core qualities of the tiny house.

Keywords: Buildings , Design , Eye tracking , Sustainability , User perception

To stop the dispersion of microplastics in the ecosystem, many technologies for collecting them were designed, tested and developed in the last period. However, a complete and exhaustive comparison of these technologies to guide in the choice and/or in the development of the most suitable appropriate one is missing in the literature. This study investigates the presence of some known technological trends, deriving from the TRIZ (Russian acronym for "Theory of Inventive Problem Solving") in the behaviour (i.e. the operating principle) of these technologies. To do this, a systematic methodology was followed, which has a general value and consists in analysing the patents relating to these technologies through various bibliometric indexes (i.e. Innovation index, Emergence Score index, Independent Claims index and Technology Cycle Time index). In general, the obtained results did not reveal a clearly identifiable ranking of the behaviour which was unanimously confirmed by all the considered bibliometric indexes. In addition, the average of the scores of the different indexes associated with the different behaviours equalized their differences. However, these results are mainly due to the markedly different evaluations obtained by the Emergence Score index compared to those of the other indexes. From the comparison of the results with the evolutionary trends, it emerged that the operative zone reduction trend was the most confirmed, while the technical system dematerialization was the least confirmed by the bibliometric analysis of all the indexes. In particular, the ranking of the behaviours provided by the Innovation index best confirmed all the evolutionary trends, while that of the Emergence Score index was the worst. In conclusion, this study confirmed the adherence of the development that technologies for collecting microplastics are following to the evolutionary trends through bibliometric analysis: this sequence places magnetic technologies in first place, followed by chemical, fluid dynamics, dynamic mechanics and static mechanics. The analysis of the performances declared in the patents substantially confirms this result.

Keywords: Bibliometric analysis , Evolutionary trends , Microplastics , Patents , TRIZ

Analyzing pathological movements can substantially help neurologists in the diagnosis and treatment improvement for patients with Parkinson's disease (PD). A linkage between the intensity and characteristics of moving and walking disorders and the stage and types of PD can be actually established. The main aim of this study is to develop an effective methodology that allows to evaluate, in real time and / or in deferred time, movements and posture of PD patients in their usual living environments. For this purpose, a wearable suit with Inertial Measurement Unit (IMU) sensors was designed; it has made it possible to acquire linear and angular signals of displacement, velocity and acceleration of the most relevant body points of the patients. The filtered and integrated signals were then used to animate a human parametric multibody model that virtually reproduces in real time and / or in deferred patient's movements and posture. Serving as the patient's “avatar”, the multibody model enables the neurologist to carry out an accurate assessment of the patient's movements and posture (freezing, festination, postural balance) as well as to measure disease progression and response to interventions. If compared to traditional 3D video-based motion analysis systems, the proposed method has the advantage of providing a more accurately measurable patients movements analysis and comparison performed in their usual living environments in real-world conditions.

Keywords: 3D posture analysis , Human parametric multibody model , Inertial Measurement Unit sensors , Motion recognition algorithms , Parkinson's disease movements