This work investigates how the concept of affordance should be revised following the digital evolution. Starting from a review of the literature about affordance, the most acknowledged constructs are compared with the variegated definitions of digital artefacts. The paper proposes a definition of digital affordance, overcoming the inconsistencies identified in the literature. The study is enriched by a series of interviews to investigate the final users' perception of affordance. Finally, the paper shows the application of the proposed model with a case study related to food delivery services.

The development of engineered systems is a complex process, involving many activities and development artifacts. The challenges introduced by this complexity calls for a model-based design environment. The PROVE methodology is an artifact-centric, model-based development process design methodology. In this paper, we present the first, fully model-based design environment of the PROVE. Furthermore, our environment incorporates additional process design perspectives that are applied in real-time and in a completely consistent manner, based on a formally defined information model. We discuss the details of our environment's design as well as highlight its potential benefits.

Today we live a high increasing digitalization in industry 4.0. As part of the evolution of CAD solutions on the market, there is a particular interest in new generation software which are distributed as Software-as-a- Service (SaaS) such as Onshape, 3D Experience, Fusion 360, etc. In order to prepare engineering students for integrating such software within the Université de Technologie de Compiègne (UTC), a further practicing study is carried out in this paper. This study aims to identify, analyze, experiment, evaluate and compare the capacities of Cloud-based CAD solutions on the market and in scientific research work in order to define potential benefits for their implementation in mechanical engineering education. Therefore, we tested two use scenarios of the two Cloud-based software; Onshape and Fusion 360 on a case study. Then, we discussed the comparison results.

This research work presents a methodological support for the specification of complex products. This is achieved by developing a specification structure in a MBSE environment. The new method draws on success factors of complex product specification, principles of MBSE and the explanatory model of PGE – Product Generation Engineering. For evaluation, the method is applied within a student development project. A high applicability and the realization of novel synergies for coping with continuously increasing product complexity is demonstrated.

Engineering Digitalization enables development of new high intelligent products containing mechanical, electrical, software and communication components. As these complex products are result of multidisciplinary engineering processes, digitalization also enforces companies to raise, adapt and revise their engineering competencies and process capabilities to increase agility and maintain competitiveness. Also, the growing amount of data related to product and processes requires a well-structured management concept. In order to encounter all these changes and new requirements companies should identify their specific strengths and weaknesses and derive needs for action. This paper presents a novel maturity model for evaluation of capabilities of Engineering Digitalization in areas of processes, products, services, data, human and organization. The maturity model enables the detection of enhancement potentials and conception of individual digitalization plans for production companies. It has been composed based on a proven multidisciplinary engineering methodology along the product lifecycle process, which includes Model Based Systems Engineering Methods, and a multilevel IT architecture integration concept.

In objected-oriented design, "smells" are symptoms of code violating design principles. When a deadline is looming, decisions can affect the long-term quality of a code or CAD. Given this and the similarities between object-oriented code and CAD models, this paper introduces a set of CAD smells. These smells are derived from a top-down review of potential CAD smells mapped against the reported code smells that violate abstraction, modularity, encapsulation, and hierarchy principles. This list was further reviewed considering CAD systems and specific examples (some illustrated in the paper).

Our society is built on engineered systems. Engineers are becoming increasingly concerned with the sustainability of systems, particularly their ability to adapt to a changing world. Recently, there has been increased interest in exploring how design margins provide opportunities for a system change. There have been great developments in determining how design margins can absorb change at a system level, but it is still not clear how design margins might provide change opportunities at a decision variable level. In this paper, we show how system-level margins could be deconstructed to explore what change opportunities they may provide at a decision variable level. We also investigate how the coupling of functional requirements limits how system-level margins can be operationalized. Our analysis suggests that design margins can provide meaningful change opportunities at the decision variable level, but the mechanisms that produce these opportunities are complex. These insights lay the groundwork for future research on mapping and representing design margins in the context of system adaptability.

Understanding emergence is an important goal of system thinking, as it can express both desirable and negative properties of products and systems. Emergence has also a special importance as it has a direct link to the performance of products and systems, and thus has a direct relationship with the quality of life and thus sustainability in our societies. Emergence and system thinking are closely related to engineering design methodologies. In our paper, we develop a more precise definition of emergence through the core principles of systems complementarity that are similarity, irreducibility and sophisticated relationships expressed through the interfaces between systems, subsystems or product components.

We demonstrate the utility of the approach based on an aircraft pylon case study by presenting a detailed definition of an interface design matrix and analyse how pylon subsystems influence emergence. The results have shown that the product can be perfectly represented by a model-based approach supporting interface management and the assessment of system complementarity. In turn, this approach allows to go beyond a qualitative definition of emergence, as it proposes a quantitative approach through the assessment of complementarity.

Digital Twins are virtual representations of a product-service-instance and, as a technology, represent an important part of the realization of Industry 4.0. They manage data of the associat-ed product instance and can also have functions for simulation to achieve cost and resource savings while simultaneously increasing product quality. In this paper, a need for action for the implementation of a systematic approach for the returning of data of Digital Twins into the product design is identified and a methodology is developed as an answer. This methodology realizes an information management, which supports holistic data and information flows. It de-fines necessary steps for the implementation of data and information transport, starting from a data management up to information provision in product design. Based on a performed potential analysis for the identification of intended uses in the context of product design, the overall ap-plication focus is narrowed down to the development of new product generations to support the requirements development. The concept structure consists of Digital Twins, a data mining sys-tem for the transformation of data into information and a presentation system for managing the information provided.

This paper presents a focused examination of critical performance and design issues for the introduction of highly automated tractors and their user interfaces in agriculture. An industry that as of today mainly uses direct-controlled machines that at least to some extent have partly automated functionalities. Issues include out-of-the-loop unfamiliarity, interface complexity, automation transparency, and changing information modalities in teleoperation scenarios for former cabin-based operated machines. Selected evidence and accompanying concepts and findings from literature are put in context to each issue, informing a systematic design process that utilizes the frameworks of knowledge engineering and ecological interface design. The resulting user interface prototype is built upon the identified requirements in analysis and collected design guidelines, stemming from various research areas. The documentation of the consideration of these in context with additional requirements, such as complexity reduction, information interactivity, and users' existing experiences is meant to provide insights into the often opaque and art-like design space.

In order to have a sustainable disassembly process, a successful decision-making based on reliable and up-to-date information should be made while taking into consideration sustainability indicators. In this context, The aim of this paper is to introduce a decision support system based on knowledge based and digital twin in order to help stakeholders to choose the most sustainable disassembly scenario .In this research, firstly, we presented the state of art of disassembly process, digital twin, knowledge based system and the merging of knowledge based system and digital twin for disassembly. Secondly, we presented the knowledge based digital twin (KBDTw) system framework for a sustainable disassembly process. Thirdly, a case study is presented about the use of KBDTw in the end-of-life of internet boxes. Finally, a conclusion and future work are conducted.

Although product family design methods are well established, little research has focused on Product-Service-System (PSS) family design. A PSS family design method is proposed in this paper that parallels methodology for designing product families. Separate platforms are proposed for products and for services. However, couplings between product and service platforms are identified and incorporated into the design method. Design problem formulations are proposed for PSS family platforms and for the PSS family itself, using a module-based approach, in contrast to a platform scaling approach. Alternative methods are investigated and compared for solving these problems. The application domain of Assistive Mobility (AM) is identified as a promising PSS family in this work. If smart technologies are integrated into AM devices, such as manual wheelchairs, powered wheelchairs, walkers, and rollators, then patient diagnosis and treatment, as well as device maintenance, services are enabled with these smart technologies, demonstrating that smart AM devices are a promising PSS family.

This study explores how large language models like ChatGPT comprehend language and assess information. Through two experiments, we compare ChatGPT's performance with humans', addressing two key questions: 1) How does ChatGPT compare with human raters in evaluating judgment-based tasks like speculative technology realization? 2) How well does ChatGPT extract technical knowledge from non-technical content, such as mining speculative technologies from text, compared to humans? Results suggest ChatGPT's promise in knowledge extraction but also reveal a disparity with humans in decision-making.

This exploratory work aims to understand which elements of a building mostly attract visitors’ attention. An experiment was conducted to allow participants to visit a prototype tiny house while wearing eye-tracking glasses. Identified gazed elements of the prototype were selected and the corresponding dwell times used as variables. The limited dwell times on structural elements show that they can be easily overshadowed by other features present in the building. This leads to a design problem when the novelty and the quality of a new product, markedly a building, reside in the materials used.

Neglecting challenges of distributed collaboration can lead to significant efficiency and effectiveness losses in agile, distributed development teams. The EDiT method provides support for improving distributed collaboration of development teams. To ensure acceptance, applicability, and contribution to success in industrial development practice, it is necessary to validate the EDiT method. The goal of this contribution is the development of a process model for early and incremental validation of the EDiT method in the field finally leading to a validation of the EDiT method itself.

In their early years of education, design students may experience difficulties in reframing design problems. Since reframing is linked to creativity, this may be problematic. While there are some models available to describe the reframing process, it is yet unclear how they are supporting design students. This study concerned the development and test of a framing model based on co-evolution transitions, through a two-part study: interviews with expert designers and workshops followed by group interviews with novices. The resulting model offers a way of thinking and a way of working, based on the fluidity of the design process. This study yielded two major insights. Firstly, students tend to perceive the problem space to be fixed once they defined it, even if they discovered disparate information along the way. Secondly, the developed model provides students with guidance and confidence in dealing with complex problems. Our results have a considerable impact on design education, as it is important to reinforce to design students that both the problem and solution understanding are fluid, and this model provides initial steps to help designers structuring their process.

This study explores the effects of time pressure in dexterous operations on two types of interface: the fixed interface and the moving interface. Results show that the accuracy of finger movement is decreased, the information processing on the sense of sequence, position and direction is worsened by the psychological disturbance. The findings indicate that a fixed interface is more robust to performance and participants can learn and perform tasks quicker than a moving interface. Finally, the researchers give some practices on both fixed and moving interface design.

Short-life cycle products are frequently replaced and discarded despite being resource-intensive. The short life span and the low utilization rate of the end-of-life products cause severe environmental problems and waste of resources. In the case of short-life cycle products, a new generation of products is released sooner than other products, therefore there are the opportunities to have various generations of products during the remanufacturing process. The commonality between generations increases the intergenerational component compatibility, which increases the efficiency of the manufacturing and remanufacturing processes, while at the same time weakening the performance difference between generations. This paper proposes a mathematical model to investigate the effect of commonality among generations on the overall production process. Based on various given new generation product designs with different commonality, we aim to propose optimal production planning and pricing strategies to maximize the total profitability and investigate how the results vary according to the commonality strategies between product generations.

To satisfy customer needs in the best way, companies offer them an almost infinite number of product variants. Although, an identical product was not built before, the values of its attributes must be determined during the product configuration process. This paper introduces a methodical approach to predict the values of product attributes based on customer feature configurations using machine learning. Machine learning reduces the effort compared to rule-based expert systems and is both, more accurate and faster. The approach was validated by predicting vehicle weights using industrial data.