Product Development Cycle Research Articles

User-Centred Product Design with Photorealistic Virtual Prototypes: A Case Study on Process Optimisation for Aesthetic Quality Enhancement

In Industry 4.0, companies must focus on human-centred design for a competitive edge. The 4USER project aims to establish a user-centred design method using an interactive Photorealistic Virtual Prototype (PVP) based on Extended Reality (XR) technology, objectifying customer requirements into technical specifications. The PVP overcomes limitations associated with traditional physical prototypes, serving as a quality benchmark for final products. The research focuses on a case study involving the development of sports rifles, emphasising the importance of aesthetic quality. The proposed semi-automatic process in Blender enables the generation of low-poly PVPs, incorporating hyper-realistic textures and high-frequency details. In particular, the overall process is composed of the following steps: i) wooden texture generation via the Wasserstein Generative Adversarial Network (WGAN); ii) model creation based on a low poly “Shrinkwrap Cage”; iii) integration of generated textures into the UV-mapped model. This approach accelerates the product development cycle, reduces costs, and facilitates efficient quality control.

The socioeconomic impact of in-silico models for implantable medical devices: a conceptual framework

Abstract The use of in-silico technologies for implantable medical devices has gained importance in the last decade. From a medical perspective, the term ‘in-silico’ mainly refers to computer simulations of clinical trials based on virtual cohorts. So far, there is a lack of a concept how the impact of in-silico models on socioeconomic factors can be evaluated. As part of the EU-funded project SIMCOR, one objective was to conceptualize the impact of in-silico on implantable medical devices. We applied an iterative process described in Jabareen (2009) to build a conceptual framework based on concepts from a comprehensive literature review and expert interviews from academia, companies, and regulatory bodies. Repeated steps of literature work, exploratory interviews and discussion sessions among the research team led to the final framework that was validated by feedback from experts at the end. The conceptual framework describes the impact from in-silico technologies along the product development cycle of implantable medical devices through impact channels up to socio-economic endpoints referring to firm, market, health system and society. Since the whole conceptual framework has multiple complex impact channels that create a kind of network, each impact channel is explained with its ramifications. One channel reveals that underrepresented patient groups in clinical trials might benefit from in-silico models by enabling to model rarer anatomical configurations and leading to medical device development for a broader range of population. The conceptual framework provides the basis for the quantification of the impacts of in-silico models that is expected to increase the acceptance of these models among different stakeholders. The potential benefits as well as disadvantages can be better estimated and classified from different perspectives. The use of in-silico models is expected to enable access to implantable devices faster and to a larger population group. Key messages • The framework reveals opportunities of in-silico models for different stakeholders, e.g., entrepreneurs, regulators, that might lead to an increased development, use and acceptance of such models. • The framework indicates that in-silico models are expected to accelerate implantable medical device development, increase patient safety, and gain faster access for larger population groups.

Co-creation of the Global Patient Experience Data Navigator: a multi-stakeholder initiative to ensure the patient voice is represented in health decision-making

BackgroundPutting patients’ needs and priorities at the forefront of healthcare initiatives and medical product development is critical to achieve outcomes that matter most to patients. This relies on the integration of early, meaningful patient engagement (PE) to learn what is important to patients, and collection of representative patient experience data (PXD). The increased number of PE/PXD efforts across global regulatory, health technology assessment, and healthcare systems is an important step forward to deliver improved health outcomes for patients. However, these initiatives are fragmented and lack integration, which is necessary to maximize efforts and reduce burden on patients. To overcome these challenges, the Global Patient Experience Data Navigator has been co-created by Patient Focused Medicines Development to provide practical resources that can facilitate and optimize PXD generation, collection, analysis, and dissemination for patient benefit and aims to be applicable across all therapeutic areas for all stakeholders.MethodsCo-creation of the Navigator took place through an iterative process of validation and formalization driven by a diverse, multi-stakeholder working group with individuals who have varying knowledge/experience in PE/PXD.ResultsA series of workshops took place to conduct a gap analysis, develop a taxonomy model, and integrate existing frameworks. The collective insights led to the development of the Navigator consisting of four specific tools in the form of downloadable templates, which can be used to: (1) prioritize outcomes that matter most to patients and their caregivers; (2) select appropriate measurement methods for these outcomes; (3) identify when and why PXD is used throughout the product development cycle for each stakeholder; (4) identify when and why PXD is used throughout the healthcare process for each stakeholder. A public consultation was carried out to collect user feedback before the Navigator was made publicly available in December 2022.ConclusionTo our knowledge, the Global Patient Experience Data Navigator is the only publicly available toolkit developed with a multi-stakeholder and disease-agnostic approach providing taxonomically grouped resources to optimize the collection and collation of PXD for patient benefit. Future work will aim to further engage patients by adding a PE dimension to the Navigator.

Structure formation in pin-like joining using vibration welding technology

Joining technologies have a decisive role in the product development cycle. Welding processes are widely used industrially, which, in addition to a number of advantages, also have limitations on possible material combinations. Therefore, novel joining processes are necessary, like the joining using pin-like structures using the vibration welding technology. Beside known material and process correlations, the structure formation effect the resulting bond properties. For this reason, the structuring step was analyzed within this study. The investigations allowed a model-based description of the formation of the pin-like structures to be derived. In addition, the investigations show that an additional energy input during pin development prevents a pin defect during tool pull removement due to a reduced weld line. This can have particular advantages for material combinations with a bonding-partner of high stiffness.

Artificial Intelligence in Product Development

A great deal of practitioners in the product design and manufacturing area considers using artificial intelligence (AI) tools with an aim to provide unusual ideas in a shorter period of time. The present paper is using a series of case studies in order to provide a considerable experience in using a text-based AI platform and highlight the advantages and difficulties that is needed to be overcome in the future. The new role for the AI tools within the product development cycle is stressed, while their use together with the design methodologies and the technological tools became clear. The output of these tools is mainly images that cannot be parametrically controlled and further substantial work is needed for creating the 3D models of these proposals.

Mechanical properties of 3D printed specimen using FDM (Fused deposition modelling) and SLA (Stereolithography) technologies

In this competitive world, grabbing the market and achieving the deadlines is one of the deciding factors for the industry. So, develop a scope of new manufacturing methods for high quality and cost-efficient products to maintain in the competitive world. Thus, the utilization of Rapid Prototyping Techniques (RPT) has become essential in the rapid product development cycle for complex products. Mechanical properties are the one focusing area of rapid prototyping (RP) end product, which may help for proper strength in the structure. Furthermore, fused deposition modelling (FDM) and Stereolithography (SLA) are the two major 3D printing technologies for building parts. This study investigates the mechanical properties of 3D-printed end products of these methods (FDM and SLA). Initially, numerical analysis (Structural) of acrylonitrile butadiene styrene (ABS) 3D model has been created using ANSYS 3D. Furthermore, printed specimens using said 3D printing method to identify the tensile, compressive, flexural and hardness tests. Lastly, the data are used to compare and identify the optimum result of ABS printed specimens.

A reliability assessment methodology of system-in-package based virtual qualification

System-in-Package (SiP) is a cutting-edge packaging technique that is flourishing in the semiconductor sector. Physical testing, which can be time-consuming and lead to design modification iterations delaying product release or delivery, has traditionally been used to assess the reliability of SiPs. Simulation methods that mimic physical tests can be used to shorten product development cycles, increase reliability, and lower development costs. This paper introduces a SiP reliability assessment methodology based on virtual qualification. A life prediction methodology of SiP based on physics-of-failure (PoF) is studied in conjunction with multi-source fusion life information of typical structures. Based on this, a comprehensive reliability assessment methodology of SiP is formed. The information input includes material types, structure size, and environmental load conditions. The finite element analysis (FEA) is used to analyze the typical structures. The life distribution of SiP components is obtained through the theory of the cumulative damage and competitive failure model. Through multi-source information fusion, the SiP comprehensive reliability assessment methodology is determined. Finally, a case study is done to determine the accuracy of the approach.

G-code converter using interface system for a STEP file (ISO 10303)

The STEP file is the “standard for the exchange of product model data,” which is usually used to exchange geometric data in boundary representation (B-rep) between different computer-aided design (CAD) platforms. These data can be fully utilized and integrated into a larger manufacturing organization, such as a computer-aided manufacturing (CAM) environment for computer numerical control (CNC) machining applications based on ISO 6983. The 3D models of 2D machining profiles were created in CAD software and saved as STEP files. The data structure was analyzed by comparing the geometric entities of the CAD model and the STEP file. The algorithm was created using the hypertext preprocessor (PHP) programming language and produced a computer interface system to convert STEP files into G-code format. The machining blocks with profile machining features were simulated using a CNC simulator and PC-based open architecture control (OAC) software. The G-code was validated on a three-axis CNC milling machine, and the result was compared to the CAD model to confirm the machining profile. The integrated interface system I2S demonstrates its ability to interpret all 2D profiles and generate machining tool paths and G-code, allowing data flow between CAD and CAM environments and shortening product development cycles.

A Cyclic Calibration Method of Milling Force Coefficients Considering Elastic Tool Deformation

In metal-cutting technology, milling plays an important role in the product development cycle. The accurate modeling and prediction of milling forces have always been research hotspots in this field. The mechanical model based on unit-cutting force coefficients has a high prediction accuracy of cutting forces, and it is therefore widely used in the modeling of milling forces. The calibration of the cutting force coefficients can be realized by linear regression analysis of the measured average milling forces, but it needs to carry out multiple groups of variable feed slot milling experiments with full radial depth of cut, and it cannot well represent the interaction condition in peripheral milling with a non-full radial depth of cut. In peripheral milling, the tool will inevitably deform under the influence of cutting force in the direction perpendicular to the machining surface. If the force-induced deformation is ignored, the calibration of the cutting force coefficients will be out of alignment. For this non-slot milling condition where one side of the tool is mainly stressed, a cyclic calibration method for milling force coefficients considering elastic deformation along the axial contact range is proposed. Firstly, the cutting force coefficients are preliminarily calibrated by the experimental data, and, secondly, tool deformation is calculated through a preliminarily calibrated cutting force model cycle until convergence, before cutting boundaries are updated. The cutting force coefficient is then calibrated again, and it is brought back to the cantilever beam model in order to calculate the tool deformation again. The above process is repeated until the cutting force coefficient is convergent. Finally, the cutting force coefficients are obtained in order to predict and model the milling forces.

Swift Assessment of Flexible Elements of High Speed Flexible Couplings for Aircraft Transmission Systems

The High Speed Flexible Coupling (HSFC) known as Power Take-Off shaft (PTO) is used in combat aircraft for connecting engine gearbox with accessory gearbox to transmit power and to accommodate larger misalignments. The HSFC utilizes series of metallic contoured plates as flexible elements. The development of a HSFC shaft assembly involves high cost and time process. Any failure of the HSFC during flight operation will deprive the secondary power system mainly used for controlling the single engine fighter aircraft. To develop the reliable HSFC with minimum developmental cost and time, an innovative approach termed as Swift Assessment of Flexible Elements (SAFE) for the development of HSFC is evolved. A case study of a typical high speed flexible coupling developed using this approach is presented With the SAFE approach, sufficient level of flexibility in design modification, trim down product development cycle time and cost were realized.

Degradation Science from Nanometers to Kilometers: A Pathway to Rapid Detection for Reliable Photovoltaics

Photovoltaics reliability for terrestrial applications has more than four decades of history and experience. International standards development has prevented many early life failures and must be continued in the future. Yet, as the module product development cycle is measured in mere months, additional investment in the fundamental degradation science and rapid detection should be developed. In this article, a brief strategy is provided addressing indoor accelerated, outdoor testing, and modeling efforts required to retain high reliability in the following decade.

Exploring new-product selling challenges in the FMCG sector: a qualitative method approach

PurposeThe study aims to identify salespeople’s challenges while selling newly launched products in the fast-moving consumer goods (FMCG) sector by examining the holistic environment in which they perform their selling tasks. Furthermore, it develops a hierarchical model mapping the interrelationships between identified challenges to explore their dependence and driving power through qualitative research techniques.Design/methodology/approachThe current study is exploratory and inductive in its research design. It used focus-group discussion (FGD), semistructured interviews and thematic content analysis (TCA) to identify new-product selling challenges in the FMCG sector. The identified factors were then worked into a hierarchical model using total interpretive structural modeling (TISM) to analyze their relationship. The factors were further classified into clusters based on their driving and dependence power, with the help of the Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) techniqueFindingsThe TISM and MICMAC results identified salespeople’s most critical new-product selling challenges in the FMCG sector: product innovation, product differentiation, customer perception and market turbulence. An enhanced organizational focus on these factors will ensure that salespeople get adequate input to tackle the challenges they face while selling newly launched FMCG products.Research limitations/implicationsThe study was confined to identifying challenges in the FMCG sector alone but offered scope for application in other sectors.Practical implicationsThis study will help organizations to identify and close gaps in the new-product selling process, thereby improving the performance of salespeople and contributing to a new product’s success. The study findings have a bearing on various stages of product development, management and life cycle. They also highlight the need for greater synergy between an organization’s sales force and other departments.Originality/valueTo the best of the authors’ knowledge, this research is unique in identifying new-product selling challenges in the FMCG sector. It also delineates the complex Web of interrelationships between them and classifies the identified factors based on their driving and dependence on power. The research results can help in organizational decision-making and sales practices, empowering salespeople in their new-product selling tasks.

How do firms build dynamic capabilities to develop sustainable products? A multiple case study in the manufacturing industry

In response to mounting pressure for organizations to meet sustainability requirements and improve their environmental footprint, companies are increasingly tasked with adapting their product portfolios to include environmentally sustainable solutions. However, little research has explored how firms develop the capabilities necessary to achieve this goal. In particular, there is a gap in understanding the microfoundations of dynamic capabilities–the specific skills, processes, and organizational activities–that enable firms to simultaneously pursue business objectives and environmental requirements during product development cycles. To address this gap, we conducted a multiple case study of 19 established manufacturing firms to examine the dynamic capabilities involved in developing environmentally sustainable products. Our findings reveal a dynamic process of profiting from environmentally sustainable product development and identify several microfoundations that enable these dynamic capabilities. For scholars, this study provides a detailed analysis of why and how microfoundations of dynamic capabilities support environmentally sustainable product development. We show that implementing these becomes a continuous process of strategic renewal for an organization. For manufacturing practitioners, our study provides a blueprint for initiating and enabling strategic activities for environmentally sustainable product development.

A STUDY ON THE POTENTIAL OF GAME BASED LEARNING FOR SUSTAINABILITY EDUCATION

AbstractAcademic institutions are increasingly required to prepare future practitioners to face complex sustainability challenges. The need to foster the development of different skills, attitudes, and multidisciplinary collaboration raised the interest in alternative learning approaches. Game-based learning can be a tool to achieve a variety of desirable learning outcomes, including sustainability and collaborative attitudes change. We present a pilot study investigating the potential of a board game on sustainability risks and opportunities in product development and life cycle, performing a test with different student audiences. The paper discusses our results of the experiment, including a survey following the game, qualitative analysis of students' feedback, and observations during the game sessions. Additionally, we relate insights from students' reflections to CDIO learning objectives. We then illustrate lessons learned and the potential advantages of using the game compared to other teaching approaches and as a complementary tool. Finally, we propose future directions and recommendations for the use of the board game and game-based learning in sustainability education with different student audiences.

A Hybrid Artificial Intelligence-Based System for Supporting Eco-Industrialization of Complex Manufacturing Processes

Since the industrialization phase is one of the main phases of the product development cycle, an original hybrid flexible automated system is developed in this paper in order to support production eco-processes designers’ decision making. It is based on three different artificial intelligence tools, namely the fuzzy Ontologies, the cases based reasoning and the rules based reasoning, which have been integrated in one system. Actually, the proposed system is composed of different modules that are well described in details thereafter. In the end of this paper, a case of study is presented in order to illustrate the efficacy of the developed intelligent system.

Agile Design and AI Integration: Revolutionizing MVP Development for Superior Product Design

This paper delves into the integration of Artificial Intelligence (AI) into the design process of Minimum Viable Products (MVPs) to enhance efficiency, quality, and innovation. It particularly highlights the application and potential advantages of ChatGPT, a widely used natural language processing model, in MVP design. Firstly, the fundamental concepts and development of AI and ChatGPT are discussed. Subsequently, the potential benefits of ChatGPT are elucidated, including improved design efficiency, enhanced design quality and innovation, and optimized user experience. Furthermore, a ChatGPT-based framework for MVP design is proposed, encompassing key steps such as identifying target users and core functionalities, outlining product requirements, prototyping, user testing, and product launch. Lastly, the challenges and considerations associated with the application of ChatGPT are addressed, and future trends in AI technology for agile design are explored. Through the research and discussions presented in this paper, it is concluded that integrating AI into MVP design holds tremendous potential for enhancing design efficiency, optimizing design quality and innovation, and improving user experience. ChatGPT, as an advanced AI technology, provides designers with intelligent assistance and decision support, enabling them to better meet market demands, expedite product development cycles, and enhance product competitiveness. However, it is essential for designers to acknowledge the challenges associated with ChatGPT and adopt appropriate strategies to address them. Looking ahead, further advancements in AI technology and continuous learning and adaptation to emerging technologies by designers are anticipated, thus driving innovation and development in the field of agile design.

MATHEMATICAL MODEL AND NUMERICAL SIMULATION OF OXYGEN TRANSPORT IN MULTILAYER FOOD PACKAGING: A SEMI-ANALYTICAL APPROACH

The purpose of this work was to develop a numerical simulation methodology using a free software capable of predicting the behavior of oxygen in multilayer polymeric systems to optimize packaging configurations with a greater barrier to O2. The Laplace Transform method was used to solve partial differential equations, with numerical inversion by the Stehfest algorithm together with the Gauss-Sidel method. It was possible to simulate the oxygen concentration profile in packaging systems containing two or three materials in the configuration. For some systems, the increase in the simulation time caused a numerical fluctuation at the material interface, especially if oxygen diffused from a material with greater diffusivity to one with lower diffusivity. The numerically simulated oxygen concentration profile can be associated with results from the literature, experimentally validated. The results showed to be promising for application in diffusion problems of multilayer systems, and the methodology can be used to describe systems with more layers. Mathematical modeling and numerical simulation have been allies to predict the behavior of oxygen in different materials and layer configurations, and it is relevant to provide a better understanding of the oxygen transport fundamentals involved and shorten product development cycle time and cost, contributing to the economy and new product development.

Information Embedding for Secure Manufacturing: Challenges and Research Opportunities

Abstract The digitization of manufacturing has transformed the product realization process across many industries, from aerospace and automotive to medicine and healthcare. While this progress has accelerated product development cycles and enabled designers to create products with previously unachievable complexity and precision, it has also opened the door to a broad array of unique security concerns, from theft of intellectual property to supply chain attacks and counterfeiting. To address these concerns, information embedding (e.g., watermarks and fingerprints) has emerged as a promising solution that enhances product security and traceability. Information embedding techniques involve storing unique and secure information within parts, making these parts easier to track and to verify for authenticity. However, a successful information embedding scheme requires information to be transmitted in physical parts both securely and in a way that is accessible to end sers. Ensuring these qualities introduces unique computational and engineering challenges. For instance, these qualities require the designer of the embedding scheme to have an accurate model of the cyber-physical processes needed to embed information during manufacturing and read it later in the product life cycle, as well as models of the phenomena that may degrade that information through natural wear-and-tear, or through adversarial attacks. This article discusses challenges and research opportunities for the engineering design and manufacturing community in developing methods for efficient information embedding in manufactured products.

Research on parameter extraction of thin‐film transistors based on swarm intelligence

AbstractThe development of integrated circuits for displays and other applications requires semiconductor device models and appropriate parameter extraction techniques to predict and understand the circuit behavior. These techniques are paramount in reducing design errors and shortening the product development cycle. This paper presents an algorithm that employed swarm intelligence in exploring an automated and accurate parameter extraction technology. First, an automatic parameter extraction of Rensselaer Polytechnic Institute (RPI) Model for polysilicon thin‐film transistor (Poly‐Si TFT) is achieved by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Compared with the best solution of the GA algorithm for automatic parameter extraction, the PSO outperformed the GA. However, it still prematurely converges to the suboptimal solution henceforth cannot obtain the expected solution accuracy. Second, the mutual learning particle swarm optimization (MLPSO) algorithm is proposed that introduces the concept of “mutual learning.” The new algorithm aims to find the global optimum in getting suitable trade‐off between exploration and exploitation. In addition, the MLPSO algorithm implemented the novel random initialization and fitness function in simplifying the complex manual processes and the empirical calibration, and it led to achieving automatic and accurate parameters extraction.

Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?

Artificial intelligence (AI) in healthcare has now begun to make its contributions to real-world patient care with varying degrees of both public and clinical acceptability around it. The heavy investment from governments, industry and academia needed to reach this point has helped to surface different perspectives on AI. As clinical AI applications become a reality, however, there is an increasing need to harness and integrate patient perspectives, which address the distinct needs of different populations, healthcare systems and clinical problems more closely. Despite this need, patient perspectives on AI implementation have little presence in academic literature and within implementation science and are not sufficiently considered throughout the MedTech and eHealthtech product development cycle, which brings its own challenges and opportunities. This joint patient expert/clinician commentary aims to briefly summarise views on AI. It reflects upon recommendations on how stakeholders such as clinicians and Health & MedTech small and medium-sized enterprises (SMEs) can make practical usage of these views. The recommendations of the authors centre around how to work better with patients to enable both product centric and patient centric innovation and person-centred care.