This paper develops a novelty measure for patents. We devise a text-based novelty measure using natural language processing (NLP) techniques. The proposed method is applied on patents that belong to a common category, which represents a subset of patents under a specific patent class. We then extract the novelty-value profile of those patents and discuss a use case for product design and development (i.e., extracting patent novelty and predicting inventive value).

This paper explores the value of the visual features of assistive products for a positive psychological impact on users. The research focuses on upper limb prosthetic devices and their aesthetic impact on the user. Within the presented study, these products are identified not only as assistive products but also as fashion accessories. A case study is presented that applies an understanding of human behaviour, motivation, and perception of semantic cues within the cultural context of a given society to deliver a more socially acceptable child's upper limb prosthetic.

Within product development, manufacturing cost estimation provides a sound basis for design and management decisions. This secures companies profitability, but the effort is high and deep knowledge at the interface of design, manufacturing and costs is needed. These issues can be eased with automation enabled by semantic technologies. Therefore, the authors developed a method for reference-based manufacturing cost estimation and created a prototype. This research evaluates the method and the prototype. Observation, interview and questionnaire were conducted with ten experienced cost engineers at a large German manufacturing company.

Based on its results, the study shows the methods contribution to lower estimation effort, while the impact on transparency and the knowledge base was only partly verified. The method steps show different automation potential, so an incremental automation should be considered. Even though semantic technologies show high potential for identifying reference system elements in this study, the limiting factor for automation in manufacturing cost estimation remains the low availability of product and manufacturing information and missing knowledge of its connection within product development.

Precise sustainability assessment becomes increasingly important in decision-making, marketing, and regulations. Therefore, reliable and comparable LCA becomes mandatory. Currently, primary data is rarely available due to vastly complex value chains. Secondary data from eco-databases provide a remedy to estimate the sustainability impacts of up- and downstream processes. While giving insights and estimations, this data is seldom fitting exactly to the own processes and lacks comparability. Therefore, this paper proposes a method to close the gap between unreliable secondary data and unavailable primary data. This gap is to be closed by the integration of simulated process data. CAM is a tool during the work preparation to assess the design's manufacturability, decrease set-up times and optimize the NC code. However, integrating DES into LCA is still subject to research and will be discussed in this paper. This paper answers the question of the necessary steps to integrate the simulated production process in an LCA to increase the quality and reliability of sustainability indicators. A method is presented, and the implementation of the steps with the help of a developed assistance system on an example is performed.

Companies are increasingly struggling to manage their complex product portfolios. Since they do not fully understand the complexity, intelligent solutions are required. Emerging technologies and tools offer new ways to deal with existing problems. With the help of clustering, similarities between product variants can be identified automatically, and complexity can be systematically reduced. This article aims to develop a methodological approach to identify correlations between product variants in complex product portfolios automatically by using clustering algorithms. The approach includes the systematic cleaning and transformation of product portfolio data. In addition, a guide for algorithm selection and evaluation of clustering results is presented. As the last step, the results are systematically analysed and visualised. To validate the methodical approach, it is applied to a real-world data set of a commercial vehicle manufacturer and the usefulness of the results is confirmed in an expert workshop.

During product development, the customer or internal stakeholders initiate changes concerning the components or functions of a cyber-physical system (CPS). The complexity of such a CPS causes difficulties in evaluating the effects of a component change. Accordingly, product developers need an assistance system to quantify the impact of a component change on hardware, software, system functions, and production processes. Therefore, this paper focuses on concepts to evaluate the effects of component, functional, and process changes and contributes to its clarification and further understanding of the importance and requirements for such an assistance system. The literature review assesses the identified methods regarding their objectives, application objects, level of automation, and relations characteristics. However, the literature review pointed out that the change prediction method from Clarkson et al. (2004) is well-established in the literature and able to quantify the impact of a change.

The paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis.

The development of modern vehicles is complex, especially regarding compliance with security and safety. ISO/SAE 21434 considers security and safety along the entire product life cycle. According to the standard, a system architecture, a risk analysis, and the application of countermeasures are carried out in the early system design. Design patterns are solutions to known design problems. Security Design Patterns (SDP) describe countermeasures and are used to reduce risk. After our literature review, we did not find a suitable approach that presents SDPs that would be applicable in early system design. In this paper, we present 10 SDPs for early system design, which we evaluated during an 11-week student project with 28 teams. We present the results of the quantitative analysis and the evaluation of the feedback.

Government regulation shapes many aspects of the design of a product. This paper addresses the effect of the complexity of a regulation on product architecture through the structure of the regulation itself. The structure of a regulation derives from dependencies among requirements and parameters in the regulation that are ipso facto design elements. Since design elements such as requirements and parameters have no formal definition in regulation, it is difficult to identify them accurately and consistently. We apply two approaches to defining and coding requirements and parameters in the context of washing machine regulation. The two coding approaches generate networks of design elements that are analyzed to measure the complexity of the regulation and by extension the product. We find significant differences in the complexity of the regulation when coded in different ways and note deficiencies and strengths of each approach. These findings will support future research to measure the impact of regulatory complexity on product architecture.

The task of developing “concepts” is common in all fields of engineering, especially in the early phases of product development. However, an in-depth literature analysis showed that authors - often depending on different contexts in design research, education, and industry - define the term “concept” in differing ways. The aspect of reference-based development is rarely addressed in existing definitions. This indicates that there is a need for an updated and concise concept definition. In this paper, the authors propose a new definition of the term “system concept” within the context of SGE - System Generation Engineering that incorporates the findings from the literature analysis. The definition was reflected on in two case-studies. The first one contained the system concept for automotive display and operating systems, the second one the system concept for a kinesthetic-haptic VR interface. The proposed definition contains the relevant characteristics identified from the literature review and supports both current activity-based process models and reference-based development, as practical application has shown.

Design is widely understood as a domain-independent notion, comprising any activity concerned with creating artefacts. This paper shows that models can be viewed as artefacts, and that the design of models resembles the design of artefacts in other domains. The function-behaviour-structure (FBS) ontology of design is applied to models, mapping generic characteristics of models derived from literature on modelling onto basic, design-ontological categories. An example of model design, namely the CRISP-DM model for designing data mining models, is analysed and compared with models of designing in other domains (systems engineering, mechanical engineering, software engineering, and service design). The results show that there are fundamental commonalities but also differences, revealing the need for further research in developing a theory of model design.

The operation of large production assets requires many decisions from the acquisition and design of new assets to the choice of lubricant for a specific piece of equipment. The decisions made in maintenance have a direct effect on the management of the production process, making it important to ensure correct maintenance decision making. However, studies on maintenance decision making tend to focus on smaller areas of decisions being made in a process, but rarely the entire process. To introduce more studies that consider the entire maintenance process, this paper proposes using a modular Maintenance Decision Architecture. The paper introduces a framework for structuring information sources into standardized information modules and mapping them to maintenance decisions made across the entire organization. The application of approaches from product, system, and service engineering are used to support the management of the complexities of maintenance of large production facilities.

Sustainability is an issue concerned with social, economic and environmental problems. The primary aim of sustainability is to fulfil the needs of the present society without compromising potential needs of future generations. Product design has a significant impact on sustainability, and a sensible decision-making process that considers trade-offs at early design stage is critical to the success of product design that addresses environmental sustainability issues. This study aims to identify and review the decision-making process for environmentally sustainable design. A comprehensive literature review has been performed to establish the trends over the past two decades. The decision-making process for sustainable design has been summarised, and the frequently-used decision-making methods, such as ANP/AHP, TOPSIS, and BWM, have been identified and discussed. A framework for the selection of Multi-criteria Decision-making (MCDM) methods has been developed to aid researchers to select appropriate MCDM methods in sustainable design. In addition, future research opportunities have also been identified.

Multidisciplinary design optimization (MDO) is a technique used in the design of systems involving the integration of many disciplines. The architecture and formulation of MDO has an impact on the solution time and optimality of final designs. The process of developing medical devices requires the combination of medical and technical knowledge and abilities. Developing a medical device is done by a complicated collection of Product Development Processes that entail tremendous oversight to ensure conformity to regulatory requirements. Regulatory standards often provide stern “Go / No-Go” policies which may discretize the design variables further increasing the complexity of the optimization problem. This work proposes a novel design approach which utilizes systems engineering practices to undertake complex multidisciplinary design optimization while implementing regulatory guidelines for medical devices. The formulated model is then applied and examined in a case study towards the development of a piezoelectric respiratory sensor. It is observed that the novel framework would extensively improve the design space definition and process driven product development practices.

In big companies, Circular Economy (CE) is being explored, nevertheless, Small and Medium-sized Enterprises (SMEs) struggle to follow because of particular barriers and a lack of practical guide. Here, we use Style & Design, a French industrial SME, as a case study to explore how SMEs can perform a successful CE integration. This article aims at proposing a multi-internal actor circularity diagnosis method. It focuses on building an inclusive and co-constructed therefore well-accepted and persistent integration of CE for SMEs, which is lacking today in the existing literature. This work relies on a mixed qualitative and quantitative data analysis applied on a corpus of 42 one-hour-long semi-structured interviews, with 37 different professions. We capture the view of the current and desired situation and identify CE barriers unique to each interviewed worker. We also connect with each worker and open the dialog for the rest of the CE deployment. The final goal is to assemble this worker-oriented diagnosis with quantitative diagnosis, like Life Cycle Analysis of the products and Material Flow Analysis of the factory, to diagnose a complete picture of an industrial SME circularity.

Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.

Requirements represent a central element in product development. The large number of requirements inevitably results in an increased susceptibility to errors, an expenditure of time and development costs. The associated problems motivate the application of Artificial Intelligence in the form of Natural Language Processing (NLP). In Requirements Engineering one main task is the classification of requirements which serves as the input in architectural models e.g. in SysML. In mechanical engineering there is still little overview regarding the interface between requirements classification and modelling. This paper provides an overview of the requirement classes and entities used in the literature and analyses their utilisation in modelling. Existing requirements classes usually do not offer the flexibility to be transferred to other domains. However, basic structures can be adopted from those classifications. This enables a clear assignment of existing classes to object classes in modelling. Resulting from the conducted literature study the observed predominant focus of research on the software industry requires an extension of the existing requirement classes and entities to enable further use and transfer to mechanical engineering.

The ambidextrous balance of combining quantitative and qualitative approach is crucial to achieve business innovation in our ever-changing society. While exploring the early stages of a problem space, profound and iterative observations of human behaviors allow designers to discover unusual insights and users’ needs. In this regard, the purpose of this study is to introduce the Needfinding Practice course at KAIST and emphasize the importance of observational research to frame new business opportunities. Main learning goal of this course is to expand the problem finding phase for enhancing students’ creativity and divergent thinking abilities. In this study, we have conducted a literature review on needfinding through observation and how the early stage of design thinking process is related to understanding users deeply. By presenting students’ field research projects, we highlighted the process of identifying unexpected latent needs to gain qualitative data for future business implications.

Finite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

The MedTech product development is experiencing a growing complexity of the design process. The design challenge is to keep the medical device simple and user-friendly while maintaining its interconnectivity with the other systems and products. The additional layer of complexity comes from the need to satisfy both - direct customers (pharma companies), and indirect ones (patients, health care practitioners, and pharmacists). Solving those design challenges must not compromise the safety of the end-user and must follow the regulatory requirements.

This research proposes the systematic design process for MedTech combination product development with the emphasis on product strategy and concept development operationalized by design thinking participative toolkit. The proposed approach serves the purpose of increasing the traceability between the early made business decisions on a product strategy level of MedTech company, and the engineering decisions made on product concept level. The ultimate goal of the research is to support the decision-makers with methods and tools which would allow them to make informed decisions on investment in a new MedTech combination product by Pharma and MedTech companies.