Early Stages Of Product Design Research Articles

A Fuzzy Decision-Making Method for Green Design for Remanufacturability

Designs for remanufacturing (DfRem) consider the remanufacturability of the product in the early stages of product design, which can greatly increase the reusability of the products. However, product design schemes lack reasonable evaluation indicators for remanufacturability, and the decision-makers of the design scheme have subjective preferences and vague hesitation. These result in inaccurate decision making on DfRem schemes that will affect the successful implementation of product remanufacturing. In order to improve the accuracy of the DfRem scheme decision, a fuzzy decision-making method for green design for remanufacturability is proposed. Firstly, an evaluation indicator system for green design schemes was established that takes into account remanufacturability, reliability, cost, and the environment, and the entropy weighting method is used to quantify and weigh the design scheme evaluation indicators. Then, the hesitation fuzzy set is applied to construct the set of evaluations and the optimal design scheme is selected by applying the comprehensive evaluation method. Finally, the feasibility of the above method is verified by using the green design of an injection mold as an example, and the results show that the above method is able to make accurate and effective design scheme decisions. This method has been implemented in a prototype system using Visual Studio 2022 and Microsoft SQL Server 2022. The results show that the fuzzy decision-making system is accurate and effective for rapidly generating a rational green design scheme for remanufacturability.

Insights into implementation planning for point-of-care testing to guide treatment of chronic obstructive pulmonary disease exacerbation: a mixed methods feasibility study.

The purpose of this mixed methods feasibility study was to gain insights into unmet clinical needs, stakeholder preferences and potential barriers and enablers to adoption for planning the implementation of point-of-care testing for earlier detection and guided treatment of chronic obstructive pulmonary disease (COPD) acute exacerbation in the NHS in England. Exacerbations of COPD cause considerable mortality and morbidity. Earlier identification of exacerbations and guided treatment would lead to reduced exacerbation duration, reduced hospitalizations and mortality, improve health-related quality of life, reduce unnecessary treatments (including inappropriate antibiotic prescribing) which could save the NHS over £400 per patient. During the early stages of product design, we took a multi-disciplinary approach to evidence generation, gaining insights from key stakeholders to test the product concept and inform evidence-based implementation planning. Primary data was collected from 11 health care and service professionals involved in the management of acute COPD exacerbations. Overall, participants agreed that by earlier differentiation of acute exacerbation from stable COPD, patients could be started on appropriate treatment. To implement point-of-care testing into clinical practice, evidence is required to demonstrate the accuracy of differentiating between exacerbation etiologies and to provide information on the beneficial impact to the system in terms of optimized management, reduced long-term side effects, admission avoidance, and cost-effectiveness. This research provides an evidence base for future implementation planning of point-of-care testing for earlier detection and guided treatment of COPD acute exacerbation. Moreover, the technology developers can decide whether to refine the product design and value proposition thereby de-risking product development.

Ecodesign approach for complex systems – Electric Vehicle case study

The reduction of environmental impacts from the early stages of product and system design is a major challenge. Thus, traditional ecodesign methods are not appropriate because they reason by fixing the functional definition of products/systems and address the complexity of these new systems in a simplistic way, which leads to a marginal reduction of impacts. Moreover, these methods do not consider the attractiveness for users, leading to an optimization oriented towards functional reduction. In this paper, a comprehensive ecodesign approach is presented suggesting a paradigm shift, which involves a hybrid ecodesign approach based on constraint modeling of systems based on the constraint satisfaction problem formalism (CSP) with more traditional environmental assessment methods to decide which design choices to make. This approach was applied to generate and characterize product and life cycle architectures of a serial hybrid vehicle powertrain focusing on the use and end-of-life phase.

The multi-objective optimization framework: A step towards minimizing life-cycle costs and energy consumption of carbon fibre automotive structures

Design of a sustainable and economical composite structure is challenging due to the often contradicting design drivers such as energy consumption and cost. Therefore, predicting the environmental and economic impact in the early stages of product design results in the significant reduction of energy consumption and cost. Consequently, the impact of contrasting objectives on an optimized carbon fibre reinforced polymer (CFRP) demonstrator is evaluated in this study. This evaluation is performed by integrating a predictive life-cycle assessment and costing framework in a multi-objective optimization methodology. The varying design configurations include designs with and without stiffeners. Furthermore, the impact of end-of-life allocation on the optimized design is also evaluated in this study. The results show a marked difference between the energy consumption of the various optimized designs with energy optimization producing the most efficient solution. However, the mass of the demonstrator increases by 7–9 kg across the different design configurations when the closed loop allocation model is implemented. Consequently, the results show the importance of selecting an appropriate EOL allocation methodology in evaluating the energy consumption of a product. The results also show that the parameters driving the energy consumption (mass and material configuration) and cost (complexity and manufacturability) are varied and often contrasting. The differences in mass, cost, and energy optimized designs further reinforces the importance of coupling cost and energy evaluation in design of sustainable products. Finally, the study demonstrates the need for a holistic life-cycle based assessment in early stage sustainable product design.

Evaluation of open-ended, clustering, and discrete choice methods for user requirements development in a low-income country context

High quality user requirements are positively correlated with successful design outcomes, but engaging stakeholders within low-income contexts can present financial and time-related challenges to product developers from non-local industrial and academic institutions with limited knowledge of the context. Existing literature provides guidance for engaging stakeholders during the early stages of product design in high-income country contexts, but few studies have examined the effectiveness of these methods in low-income country contexts. This study evaluated three user requirements elicitation and prioritization methods including open-ended, clustering, and discrete choice. Ghanaian healthcare delivery stakeholders with varying types of expertise, years of experience, and from various types of healthcare facilities were recruited to allow for diversity of responses. Participants included physicians (n = 10), nurses/midwives (n = 16), biomedical technicians (n = 14), and public health officers (n = 7). A hypothetical mechanical device for managing and treating postpartum hemorrhage was chosen to characterize each method's ability to elicit and prioritize user requirements. The open-ended method captured general requirements of a design concept, yet resulted in predominantly generic requirements. The results from the open-ended method were used to inform the clustering and discrete choice methods. The clustering and discrete choice methods were useful for inferring in-depth user requirements and eliciting stakeholder priorities. The clustering method revealed that usability and affordability were high-priority requirements among all four stakeholder groups. An individual difference scaling analysis was performed using the clustering method outcomes, which indirectly identified ease-of-use, availability, and effectiveness as the priority user requirements categories. Stakeholders ranked ease-of-use as the highest-priority user requirement, followed by performance, cost, and place-of-origin requirements, using the discrete choice method. Given the significance of the ease-of-use requirement, an analytical framework based on sub-requirements was developed for quantifying stakeholder needs. Lastly, the relative merits of the three elicitation approaches and their implications for use with different stakeholder groups were examined.

Determining Reliability Requirements and Testing Costs in the Early Stages of Single Use Medical Product Design

ABSTRACTThe production of single use medical devices, particularly for home use by patients, continues to grow, and the reliability of these devices is a primary concern for manufacturers and end‐users. The systems engineer tasked with the device development needs methods and tools to establish reliability requirements and provide cost estimates for the testing necessary to show compliance with those requirements. This paper examines methods for determining reliability requirements, the cost of reliability testing for single use medical devices in the design input phase of product development, and how the costs of testing and potential errors can be used to perform trade‐off analysis between reliability tolerance and confidence level.

A smart conflict resolution model using multi-layer knowledge graph for conceptual design

Reducing the impact of conflicts on requirement-function-structure mapping in the early stage of product design is an important measure to achieve conceptual innovation, which relies on accurate reasoning of multi-domain knowledge. As product requirements become more personalized and diverse, traditional discrete knowledge organization and reasoning methods are difficult to adapt to the challenges of continuity and precision in conceptual solution. Knowledge graphs with complex networks have obvious advantages in association detection, knowledge visualization, and explainable reasoning of implicit knowledge, which offer innovative opportunities for conflict resolution in conceptual design. Therefore, a smart conflict resolution model using a multi-layer Knowledge Graph for Conceptual Design(mKGCD) is proposed in this study. A knowledge expression form of FBS-oriented design patent vocabulary is proposed, which is used for knowledge entity recognition and relation extraction based on natural language processing. A label mapping method based on inventive principles is used for patent classification and a four-layer semantic network for conflict resolution is constructed. Through semantic distance calculation, the designer's requirements for function/behavior/structure are smart deployed to obtain appropriate knowledge. A case study of the conceptual design of a collapsible installation and handling equipment demonstrates the feasibility of the proposed approach. The proposed method can not only meet the functional solution and innovation in the context of different design requirements, but also effectively improve the design efficiency in the iterative design process by means of multiple meanings of one graph.

Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews

Abstract Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization have created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference model is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design.

Challenges for data-driven design in early physical product design: A scientific and industrial perspective

With the rapid development of digital technologies, complex products are becoming more connected. Alongside, the usage of data in the product development process keeps on increasing. Data is an essential means of monitoring the behaviour of the products and their users for optimisation purposes. It is in the digital sector that data is already commonly used to identify new opportunities, to support decision-making and to reduce development time. To replicate this process with physical products, novel design approaches based on data are emerging. Designers need to anticipate from the early stages of product design the right data to capture and analyse. However, research is still in its infancy and faces numerous challenges. Thus, the question addressed in this article is: “what are the challenges of data-driven design research in the early phases of the physical product development process?” A workshop involving 10 researchers was held to answer this question. In addition, a campaign of 12 interviews with connected products manufacturers completed this research. Through a literature review, the workshop and the campaign of interviews, this article synthesises both a scientific and an industrial outlook on the challenges of data driven design. It offers a first glimpse of future research leads.

Knowledge transferring and conversion in the early stage of collaborative product design by entrepreneurial teams

PurposeThe purpose of the study is to discover a model of knowledge conversion and knowledge transferring/sharing barriers in an entrepreneurial team (ET) working with innovative users at the early and fuzzy front end (FFE) stage of collaborative product design (Co-PD) process.Design/methodology/approachThe exploratory research framework included sampling, data collection and data analysis, through sequential levels of categorizations, undertaken jointly with constant comparative analysis. The sample frame is the pool of ETs/startups from university business accelerators that carry out Co-PD at the FFE stage. The research survey is based on observations of the collaborative ETs activities, questionnaires and in-depth interviews with them. The research was conducted on individual and team levels when Co-PD process and ET activities were in progress.FindingsThis study identified specific set of concepts of knowledge conversion and transferring/sharing and their barriers that reflect specificity of Co-PD processes at the FFE stage in collaborative ETs. The discovered conversion process is represented by the socialization, externalization and internalization, three-mode knowledge conversion model. The significance of barriers and frequency of their occurrence were measured in knowledge transferring/sharing in collaborative ETs on individual and team levels.Originality/valueThis study shows novel insights into how knowledge transfers/shares and converts in the context of ET working with innovative users in Co-PD process at the FFE stage.

The Development of an Egg Incubator Prototype with the Use of Heat on the Condenser Side of the Refrigerator

<p>The development for an egg incubator prototype with the use of heat on the condenser side of the refrigerator belongs to Research and Development (R&D) conductment with the aim of knowing the process of developing an egg incubator prototype by using heat on the condenser side of the refrigerator to find out the feasibility level. The method in this study uses Quality Function Deployment (QFD), which is a method used in the early stages of product design and development that makes quality design of a product based on the market quality demand and order of the customer. The prototype development process undergo several stages, including: preliminary survey to the farmer community, design, instrument validity testing, prototype making, media export testing and small group testing. Based on the calculation of the results of the validity test assessment by the instrument content expert, the content validity value of 1 which refers to the instrument validity criteria table is included in the very high validity criteria. Furthermore, based on the calculation of the validation results from the media expert test and the small group test, it gets a percentage of 100% for the media expert test and a percentage of 95.20% for the small group test and refers to the eligibility level qualification table with a scale of 5 in the very appropriate category without the need for revision, so that the developed prototype egg incubator are suitable for use and marketable for the industry.</p>

Sustainable design guidelines for additive manufacturing applications

PurposeAdditive manufacturing (AM) technologies have tremendous applications in industries owing to their unique advantages. Sustainable AM (SAM) is gaining significance because of lightweight structures, lattice geometries and customized parts for industrial applications. To facilitate design for SAM, design guidelines from AM and environment viewpoints are to be analyzed. In this context, this paper aims to present the analysis of SAM guidelines.Design/methodology/approachThis work divides 26 identified SAM guidelines into four categories. Grey axiomatic design was used to calculate the weights of guidelines categories. Further, the grey technique for order preference by similarity to ideal solution was used as a solution methodology to prioritize the SAM guidelines.FindingsThe top identified guidelines are “Design for reusability” and “Optimize part orientation for build time and roughness.” Implementing proper design guidelines leads to many sustainable benefits such as minimum material consumption, energy consumption and emissions.Originality/valueThis study would facilitate Am product designers to deploy prioritized guidelines for enhancing the effectiveness of the additively manufactured product with sustainability benefits. The prioritized guidelines would guide the AM product designers to take maximum advantage of the AM process by deploying design for AM and design for environment guidelines. This study contributed a structured approach for design engineers and practitioners to deploy guidelines during the early stages of product design to ensure AM feasibility with minimal environmental impact.

The Advancement of Virtual Reality in Automotive Market Research: Challenges and Opportunities

Virtual Reality (VR) can play a key role in automotive marketing research, lowering costs and shortening the time to launch a new product. However, few VR applications support automotive customers’ experiences during the early stages of product design. This study aims to identify and characterize into attributes the challenges and opportunities for the application of Virtual Reality in car clinics through a systematic review of the literature and patents. We searched PatentScout, ScienceDirect, Springer, and IEEEXplore for studies published between the databases’ inception and July 2020. Of the 77,383 patents and 336,785 articles identified, 72 and 13 were eligible, respectively. We discovered that patents are strongly concentrated by a few inventors, that the United States has the most records, and that the most prevalent applications relate to devices for automatically reading responders’ emotions in virtual environments. The articles revealed sixteen categories of challenges and opportunities: cost, location to customers, flexibility in interactions, model transportation, depth perception, haptic perception, motion, movement perception/physical collision, color and texture, sound feedback, product interaction/manipulation, visual–spatial, graphic quality, intuitiveness, cybersecurity, and cybersickness. Virtual Reality may be used for automotive marketing research but key factors such as hardware and software specification, stimulus quality, and survey objectives must be considered.

Improving the electromagnetic compatibility of electronic products by using response surface methodology and artificial neural network

PurposeThe purpose of this study is to use the Taguchi Method for parametric design in the early stages of product development. electromagnetic compatibility (EMC) issues can be considered in the early stages of product design to reduce counter-measure components, product cost and labor consumption increases due to a number of design changes in the R&D cycle and to accelerate the R&D process.Design/methodology/approachThe three EMC characteristics, including radiated emission, conducted emission and fast transient impulse immunity of power, are considered response values; control factors are determined with respect to the relevant parameters for printed circuit board and mechanical design of the product and peripheral devices used in conjunction with the product are considered as noise factors. The optimal parameter set is determined by using the principal component gray relational analysis in conjunction with both response surface methodology and artificial neural network.FindingsMarket specifications and cost of components are considered to propose an optimal parameter design set with the number of grounded screw holes being 14, the size of the shell heat dissipation holes being 3 mm and the arrangement angle of shell heat dissipation holes being 45 degrees, to dispose of 390 O filters on the noise source.Originality/valueThe optimal parameter set can improve EMC effectively to accommodate the design specifications required by customers and pass test regulations.

The methodology of a locomotive simulation model development within the model-oriented approach

The use of mathematical modelling at the early stages of product design makes it possible to substantiate fundamental technical solutions, taking into account all possible (large number) requirements for the product by consumers and operation. At this (early) stage, it is important to use well-tested and convenient tools for designers to create virtual stands and digital twins (DC), confirming compliance, as well as forming the structure of systems and products as a whole. The article discusses a comprehensive methodology for the process of constructing a numerical model of a locomotive for analysing dynamics under various operating conditions. It is based on the top-level structural model (LBM), which is presented in the form of a block diagram and is used at all stages of product design. The required detailing of such a model depends on the actual tasks. The detailed models of the systems that have been developed within the framework of the work are presented.

Application of modal and harmonic analysis to predict the automotive transmission vibration at an early stage of design

Introduction (problem statement and relevance). The need to increase the operating speeds and input torque of automobile transmissions because of their vibration is becoming a more and more urgent problem.The purpose of the study was to substantiate the applicability of harmonic analysis, which makes it possible to determine the actual values of vibration values during a steady oscillatory process, for the initial assessment of the gearbox vibration activity.Methodology and research methods. The calculation of the gearbox vibration activity was carried out by modal and harmonic analyses, implemented by the finite element method, and followed by the experimental study of gearbox vibration on a test bench.Scientific novelty and results. The presented experimental and calculated results of the research show that the difference between the experimental and calculated values is no more than 4.2%. This proves the possibility of applying modal and harmonic analysis to predict the gearbox vibration state at an early stage of product design at the stand.Practical significance. The given calculation algorithm makes it possible to predict, with a sufficient degree of accuracy, the vibration of the gearbox before the manufacture of a prototype in experimental production.

DATA-DRIVEN DESIGN CHALLENGES IN THE EARLY STAGES OF THE PRODUCT DEVELOPMENT PROCESS

AbstractWith the rapid development of digital technologies, products connectivity is increasing as well as the data produced and collected. Forecasts on product development predict that this trend will keep on growing. In this context, new design solutions based on data are emerging. Those data-driven design approaches are common for identifying the customers' need when developing a new product. However, few studies cover data-driven design in the other early stages of product design. Thus, the research question addressed in this paper is: what are the challenges of data-driven design research in the early phases of the product development process? Through a literature review and a workshop proposed at the conference DESIGN 2020, this paper offers a first glimpse of future research leads. A list of 5 challenges for data-driven design in the early stages of product design is proposed and ranked from short term to long term.

THE PERSPECTIVES OF CLINICIANS ON ENRICHING PATIENT EXPERIENCES IN A CLINICAL CONTEXT: A QUALITATIVE STUDY

AbstractUnderstanding the viewpoint of clinicians in a healthcare setting is a vital task to comprehend the success of patients undergoing physical rehabilitation. Addressing user experience is an essential activity which designers undertake when formulating product specifications at the early stages of product design. The involvement of clinicians during the product use-phase influences the progress and achievements attained by the patients throughout their rehabilitation journey. Several clinicians (n=16) were asked to participate in a qualitative study to evaluate the performance of different personae in activities of daily living. The experiences of patients who use rehabilitation products as part of their therapy session at the clinic were evaluated. Based on this evaluation, a set of clinician requirements were formulated to reflect the overall experience when using rehabilitation products. Understanding these requirements brought about several implications to be considered during the design cycle. Through these considerations and by adopting a user-centred design approach, designers are in a better position to design innovative products targeted at providing a high-quality user experience which increases customer satisfaction.

Forecasting the importance of product attributes using online customer reviews and Google Trends

During the early stage of product design, product manufacturers seek to identify the most relevant product features that will meet the demands and needs of consumers. Conventionally, several surveys have to be undertaken during the time interval between product design and the launch of anew product, to understand any changes on the importance of the product attributes. However, the process is time-consuming and costly. Recently, online customer reviews have been generated on many websites and can be used to analyse the change of the importance of the product attributes. Also, Google Trends has been adopted in previous studies to understand consumers interests in certain products over a period of time and can be considered in analysing the change in product attributes importance. However, no such kinds of studies have been reported. This study aims to present an empirical approach that uses online big data, to identify and predict product design attributes of products that will be relevant to consumers in the future. To achieve this aim, we propose a methodology for forecasting the future importance of product attributes based on online customer reviews and Google Trends. A case study on an electric hairdryer is presented to illustrate the proposed methodology. Validation tests on the proposed fuzzy rough set time series method were conducted. The test results indicate that the proposed method outperforms the fuzzy time series, the fuzzy k medioid clustering time series and the ANFIS method in terms of forecasting accuracy. Our results contribute to the processes of new product development and can potentially assist R&D managers to establish methodologies and processes for product designs capable of generating higher returns.

Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL‐MAIRCA based on fuzzy rough numbers

Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts' subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended multi-criteria group decision-making (MCGDM) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker's risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended decision-making trial and evaluation laboratory is presented to determine the criteria weights and a fuzzy rough number extended multi-attribute ideal real comparative analysis is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.