Optimal Product Design Research Articles

A hierarchical consensus reaching model based on trust and uncertain degree for heterogeneous large-scale group decision making and application to product design

Nowadays, due to the fierce market competition, companies in many industries needs to constantly introduce new products. How to select an optimal product design has become a widespread problem. Due to complexity and a large number of decision makers (DMs) participating in decision-making, this paper formulates the product design selection as a type of large-scale group decision making (LSGDM). Because of the differences in occupation, purpose, and personality of DMs in LSGDM, they prefer to provide evaluations by heterogeneous representation formats. A hierarchical consensus reaching model is developed based on trust and uncertain degree for heterogeneous LSGDM. Firstly, the heterogeneous evaluations of DMs are transformed into fuzzy preference relations (FPRs) through possibility degrees. According to trust relationships and original evaluations of DMs, the trust and uncertain degrees of DMs are extracted considering the inherent personalities and psychological traits implied in the heterogeneous evaluations. Thereby, this paper (1) constructs an optimization model to determine the weights of DMs and (2) divides the feedback mechanism into four cases. Case study of product design selection is provided to illustrate the applicability of the proposed model. Several comparisons validate the advantages of the proposed model.

Effect of education based on creative problem-solving and asynchronous online discussions on student creativity in a product design course

Research in education has employed creative problem-solving (CPS) methodologies to enhance students’ learning efficacy. However, the majority of studies have focused on the effect of CPS on individual creativity. Few studies have examined the effects of CPS on teamwork and creativity. This study adopted CPS in a product design course, incorporating teamwork facilitated by an asynchronous online discussion platform. This platform fostered student engagement and iteration in knowledge construction through collaborative discussion. Additionally, experiments were conducted to evaluate the effectiveness of the proposed method. The study recruited 61 university students divided into two groups. One group underwent CPS-based instruction, which involved knowledge construction, utilization of the asynchronous online discussion platform, and CPS guidance. The second group received conventional instruction, where students were tasked with solving problems set by lecturers. The 14-week experiment revealed that CPS effectively enhanced students’ creativity, fostering increased discussion and reflection. Moreover, CPS encouraged students to engage in deeper thinking processes through teamwork, resulting in the development of more optimal product designs.

Study on finite element simulation and experiment based on the design of zero-point clamping system

The zero-point positioning system (Z-PCS) is more suitable for the increasingly common production modes of multiple types, small batches, and changing conditions than to conventional fixtures. Currently, the Z-PCS cannot provide sufficient clamping force, and the design of the positioning structure is prone to over-positioning. The working principles of each functional module in the system are unclear, making it difficult to promote product design optimization and troubleshooting. This article aims to design a high-performance Z-PCS, revealing the structural composition and working principle of the system, including the clamping structure, positioning structure, and air circuitry arrangement. During the design process, the finite element numerical calculations were adopted to verify the mechanical properties of each key load-bearing component. Finally, the designed product underwent positioning accuracy and clamping force testing. The results indicate that the Z-PCS designed in this article can provide a clamping force of at least 73.7 kN and control the repetitive positioning error below 0.002 mm. Micro-deformation grooves can be adaptively added to compensate for the over-positioning error. Heat-treated martensitic-type stainless steel is an ideal material for constructing the body of a high-performance Z-PCS.

A Supply Chain Oriented Product Design Optimization Decision Method Based on Improved CUR Matrix Decomposition

A Supply Chain Oriented Product Design Optimization Decision Method Based on Improved CUR Matrix Decomposition

A BP Neural Network Product Design Optimization Model Based on Emotional Design and Sustainable Product Design

The objective of this study is to investigate the impact of emotional design and sustainable product design on user experience and satisfaction. Additionally, the study aims to optimize the combination efficiency and material selection methods of these two design approaches, extracting optimization parameters based on emotional and sustainable product design principles. By analyzing user satisfaction through questionnaires, various product indicators were examined. The study ultimately establishes a BP neural network optimization model by analyzing the design elements of different products and the satisfaction levels of various indicators. This BP neural network design optimization model, integrating emotional and sustainable design, demonstrates an accuracy rate of over 95% in predicting user satisfaction for different design schemes. Upon examining the emotional design and sustainable design parameter tables following data standardization, and analyzing various product models, it was concluded that the rate of change in each design parameter significantly impacts the satisfaction of each product indicator, highlighting the importance of these parameters in product design optimization.

Optimizing product design and development of engine carbon cleaning maintenance tools using reverse engineering and VDI 2222 methods

This research article explains the product design and development optimization of multi feature engine carbon cleaning maintenance tools. The implementation of the Reverse Engineering method was integrated with VDI 2222 in the research process. The results of the optimal design and development of this tool obtained a component structure that was able to meet 14 types of consumer needs with a total tool cost of IDR 1.137.900 with a total tool weight of 6.9 kg. The results of the load test simulation concluded that the housing case was able to withstand the overall weight of the components that make up the tool. Depending on the liquid chemical used, the results of tool tests on a limited scale have proven to be able in save fuel consumption by ±12.5% per hour with engine workload which can also be reduced by 3,14%.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

Contextual Knowledge Framework in CAD Education from the Perspectives of Practicing Engineers

The contemporary industry landscape requires engineering graduates to have expertise in utilizing advanced Computer-Aided Design (CAD) tools. However, prevailing practices reveal a lack of contextual knowledge among Mechanical Engineering undergraduates and fresh graduate engineers in effectively employing 3D CAD modelling software for optimal product design development. Consequently, this study was undertaken to address this issue, with a specific focus on the integration of contextual knowledge into 3D CAD modelling. The study aims to elucidate the fundamental elements of contextual knowledge regularly employed by practicing engineers in their daily design endeavours, specifically in 3D CAD modelling. A transcendental phenomenology approach was used, and four engineers from the engineering department of a shipbuilding company in Peninsular Malaysia were purposefully selected as respondents. The analysis yielded three recurring themes central to the application of contextual knowledge in visualizing and presenting models through 3D CAD modelling: Realization, Design Intention, and Normalization. These elements are pivotal in assisting engineers in contextualizing their design work during the modelling and presentation stages of the new product development process.

Customised product design optimisation considering module synergy effects and expert preferences

ABSTRACT Fierce marketing competition and varied customer requirements on products make a lot of companies transfer to the customisation strategy. However, customers may be overwhelmed by the vast assortment of products and options. This paper proposes a methodology aimed at optimising the range of customised product varieties considering synergy effects of modules and preferences of experts. The methodology outlines key steps required for a successful customised product configuration. Firstly, a questionnaire based on the Kano model is designed to identify customer preferences for product module customisation. The potential customers can be segmented based on the distance among their multidimensional preferences. Secondly, within each segmented market, the customer satisfaction function that considers module synergies is established using the quantified Kano model and coefficient matrix. Thirdly, the cost function that considers experts' preferences is established based on the prospect theory. Finally, a bi-objective optimisation model is formulated to maximise customer satisfaction and minimise cost under technical constraints. Pareto solutions are obtained by solving the model with the non-dominated sorting genetic algorithm II (NSGA-II). Modules and attributes available to customers can be determined by the solutions. An illustrative example of modular laptop computers confirms the methodology's effectiveness in optimising product configuration.

P‐85: The Methods to Improve Low‐gray Lens Mura

With the development of OLED display technology and the intensified competition in the OLED mobile phone product market, consumers are increasingly demanding the display quality of OLED mobile phones. The higher image quality requirements have prompted panel makers to minimize the impact of mura through process improvement, product design optimization, etc. Low‐gray lens mura is an important one which affect display quality. This paper provides an effective direction for optimizing the process parameters of exposure machine and product design to weakening the influence of lens mura.

Product Design Optimization Strategies for Metal Additive Manufacturing in Aerospace Applications

Product Design Optimization Strategies for Metal Additive Manufacturing in Aerospace Applications

Optimal new product design using reverse quality function deployment with nonlinear regression modeling

PurposeQuality function deployment (QFD) has been widely applied in new product development, but existing research on QFD has some limitations. Primarily, QFD lacks the capability to provide feedback on the satisfaction degree of customer requirements (CRs) according to the actual values of engineering characteristics (ECs). In addition, QFD does not quantitatively consider the interrelationships among ECs. Reverse QFD (R-QFD) was introduced to implement the feedback process. On this basis, this paper quantitatively considers the interrelationships among ECs in the R-QFD model and extends these relationships to encompass combinations of multiple ECs, aiming to improve the inference accuracy of the model.Design/methodology/approachA nonlinear regression model was established between CRs and ECs, aiming to infer the satisfaction degree of CRs based on the implementation status of ECs. This model considers the interdependencies among ECs and extends the consideration of pairwise EC correlations from every two to every fifteen. Lingo Software is utilized to seek solutions for this program. To facilitate the implementation of the program, a directive to simplify the solution has been proposed.FindingsThe experimental results indicate that the interrelationships among ECs significantly affect the inference accuracy of the R-QFD model, thereby verifying the necessity of considering higher-order interrelationships among ECs within the R-QFD framework. Based on the results from data experiments, this paper also proposes research recommendations pertaining to ECs hierarchy for varying quantities of ECs.Originality/valueThe outcomes of this study have further refined the R-QFD model, addressing its limitations of ignoring the interrelationships among ECs. This transformation elevates the R-QFD model from a relatively simple linear model to a nonlinear model formed through modeling, thereby enhancing its accuracy and applicability. In practical terms, this study provides case support for the application of the R-QFD model in manufacturing industry.

Implications of Context Effects in Consumer Utility Models for Optimal Product Design and Differentiation

Abstract Consumer choice models used in optimal product design typically ignore potential context effects by assuming the utility of each product is independent of the attributes of other products in the choice set. We characterize implications of context effects for profit-optimal designs by deriving the first-order conditions of the design problem under alternative utility formulations, and we propose a utility function that incorporates context effects and has well-defined optimal design solutions for all products in the choice set. We then conduct a discrete choice survey experiment of automobile options and find statistically significant context-effect parameters and superior out-of-sample prediction when context-effect parameters are used in both logit and mixed logit models. These results suggest that context effects can be important in engineering design contexts and have the potential to affect optimal design differentiation.

Optimization of a parametric product design using techno-economic assessment and environmental characteristics with application to an electric traction motor

Accelerating the development of clean energy technologies is required to move towards a sustainable future. New design approaches are being proposed to help accelerate these developments. This study proposes and improves a novel design framework that incorporates life cycle assessment (LCA) and techno-economic assessment (TEA) with an optimization-based design model of a discrete product at early stage. Objective functions for mass, energy consumption, and two metrics relating to manufacturer and consumer economic interests are considered; these objectives are minimized using a Pareto-like approach. An electric traction motor for an electric vehicle is used as a discrete product case study. The approach demonstrates the utility of considering four-objectives in the design problem, uncertainty analysis via Monte Carlo simulation, and the power of employing LCA and TEA methods. Future work may explore a broader range of objective functions to cover different challenges that inhibit the development of clean energy technologies.

Data Science Applications in Circular Economy: Trends, Status, and Future.

The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

LOW CARBON FOOTPRINT ALUMINIUM COMPONENTS FOR E-MOBILITY

In the fast-evolving E‐mobility transformation, the circular economy is one of the key factors to make Europe carbon neutral by 2050, together with sustainability, achievable only with a synergic approach, from raw material choice to recycling, through product design for re‐purposing. Secondary aluminium alloys have a twenty times lower carbon footprint than primary metals, leading to significant CO2 savings. Their properties can satisfy engineering targets through optimized product design. Adopting a smart system layout, in which functions are assigned to assemblies, some of the low‐end mechanical properties of secondary alloys can be offset. Design for easy disassembling can then guarantee a selective re‐purposing and, finally, an environmentally friendly recycling of components. Innovative products in this field have been developed and successfully produced by means of an optimized high-pressure die casting (HPDC) technology, adopting low carbon footprint raw materials supplied in alternative to ingot format. In this work, the characterization of a component for e-mobility has been conducted, evaluating both the characteristics of the scrap alloy used to produce the castings and the casting itself. The results demonstrate a fine and high-quality microstructure of the casting, proving the feasibility of the production process using exclusively scrap alloy.

Application of EEG Technology in Kansei Engineering

This paper introduces the theory of EEG technology, operation process and the concept of Kansei Engineering, and discusses how EEG technology can be applied to Kansei Engineering. By recording the EEG signals of participants and analyzing the changes of these signals, EEG technology can help designers to understand the needs and responses of users more deeply, thus optimizing product design and improving user experience and market competitiveness. And the future combination of the two in more aspects is prospected.

Big Data Enables the Intelligent Manufacturing Industry

With the rapid development of big data technology, the intelligent manufacturing industry is ushering in unprecedented opportunities. Big data enables intelligent manufacturing, helping to achieve more accurate demand forecasting, production planning and supply chain management, and improve production efficiency and product quality. At the same time, big data analysis can provide real-time monitoring and early warning system, timely find and solve problems in the production process, and reduce operating costs. Through big data technology, intelligent manufacturing enterprises can better grasp consumer needs, optimize product design, provide personalized customized services, and enhance market competitiveness. In the future, big data will continue to play an important role in intelligent manufacturing, helping enterprises to realize digital transformation and upgrade the industrial value chain.

Application of Computer Vision and Neural Networks in Feature Extraction and Optimization of Industrial Product Design

Application of Computer Vision and Neural Networks in Feature Extraction and Optimization of Industrial Product Design

APPLICATION OF FUZZY LOGIC OF THE SUGENO METHOD TO DETERMINE THE AMOUNT OF PRODUCTION OF DRY POTATO SAMBAL

Home industry has become the primary choice in the entrepreneurial world. One example of a home industry is Putro Prasojo's dried cassava chili paste. In the realm of production, this capability involves the skill of planning or determining the production volume. The goal is to ensure that market demand is met accurately while also considering inventory levels to achieve maximum profit. Several factors are considered in determining the production volume, involving an evaluation of the existing inventory and consideration of the demand levels. Facing these challenges, determining the right production volume to meet consumer demand requires alternatives. Applying the Sugeno Fuzzy Method using the MATLAB toolbox. This research is expected to establish an optimal production design. The Sugeno Fuzzy Logic Method used to determine the production volume of Putro Prasojo's dried cassava chili paste based on inventory and demand data can be an effective decision-making tool for the company. This method provides truth values to the decisions made, with a truth value reaching 82.787%.