Optimal Product Design Research Articles

Framework for metamodel-based design optimization considering product performance and assembly process complexity

This paper proposes a method for simultaneous evaluation of the assembly process complexity together with the performance of the future product. It allows for product design optimization, considering different aspects of the future design at the early stage of the development process. The proposed method, embodied in a fully automated framework, substitutes the traditional sequential development process with a more efficient and rapid combined procedure, which addresses multiple design aspects simultaneously. Design for assembly (DFA) rules, used as quantitative metrics of the ease-of-assembly of the whole product and individual assembly operations, are automatically evaluated together with performance metrics, estimated based on finite element (FE) simulations. The direct solution to this optimization problem might be inefficient or impossible since it requires the recurrent evaluation of computationally expensive discrete and continuous functions with unknown behavior that represent the optimization objectives and constraints. For that reason, the proposed framework employs regression models based on the Gaussian process and artificial neural networks, thus achieving the optimal design of a product as a result of metamodel-based design optimization (MBDO). The suggested approach is demonstrated in the optimization of a gearbox assembly, considering its mechanical performance and assembly process. Comparing the results of the metamodel-based and direct design optimization shows that MBDO allows finding a better solution using a three times smaller computational budget. In addition, analysis of the results obtained using stationary sampling data sets of different sizes highlighted the limitations of the employed sampling procedure.

Optimizing Product Design Using Genetic Algorithms and Artificial Intelligence Techniques

Optimizing Product Design Using Genetic Algorithms and Artificial Intelligence Techniques

Model-based Design Optimization Taking into Account Design Viability via Classification

Design optimization of real-world industrial products is usually a challenging high-dimensional task with several multi-modal objectives. Therefore, the solution has to be found by global optimization algorithmswhichrequire fast surrogate models to realize a large number of design evaluations. However, approximating the original optimization criteria by surrogates may mislead the optimization by offering solutions in the entire design domain, even if designs are not viable in reality. Therefore, a classification model should be used as additional optimization constraint to guide the optimizer to viable results.

Re-Design Sistem Parkir Berbasis Internet of Things Melalui Pendekatan Ergonomic Partisipatory

The interesting idea of a Smart City can be realized by developing conventional things into more effective and efficient innovations with the Internet of Things (IoT). Parking is one of the most crucial needs in the most city in Indonesia. The large number of vehicles, inefficient parking systems, and about existing parking system in Indonesia. This research aims to redesign an ergonomic, modern and solution parking system to effectively reduce parking problems in Indonesia. VOC was obtained through a questionnaire distributed to parking lot users at FTI UII and Pakuwon Mall to determine consumer needs as an initial database for data processing using the QFD and HOQ methods. Then Usability testing was carried out on the UI prototype of the proposed parking application which had been designed as material for evaluating and developing a parking system based on 3 ISO attributes. Next, the results of the iteration based on the previous method are repeated to achieve optimal product design through an ergonomic participatory FGD approach with stake holders and ergonomic experts. The final iteration results were agreed as a proposed parking system to answer the problems and challenges of the Smart City concept.

Digital Operational Strategies in The Post-Pandemic Era for Travel Companies: A Case Study of Ctrip

The epidemic’s impact has caused the tourism industry to face major challenges and changes in the post-epidemic era, in which the digital operation model has become the key to driving recovery and innovation. Taking Ctrip as an example, this study provides an in-depth analysis of the changes in the digital operation mode of tourism enterprises in the post-epidemic era. Firstly, the context and situation of Ctrip’s activities have changed in the digital environment, expanding the online business space and strengthening the interaction between the company and consumers. Second, the digital environment has changed corporate and consumer behavior from competition to cooperative symbiosis while triggering the socialization and personalization of consumer behavior. In addition, Ctrip’s travel products have changed in the digital environment, with data analysis to tailor travel products for consumers. Practical applications of Ctrip’s digital operations, including user demand forecasting and product design optimization, are further discussed. It is concluded that digital operations are important in improving management efficiency, meeting consumer demand, pricing strategy, and risk control. However, digital operations face legal, protection, and risk issues that require further research and solutions. This study provides useful experiences and insights for developing digital operation modes in tourism enterprises.

Real-World Experience With the Angio-Seal Closure Device: Insights From Manufacturer and User Facility Device Experience Database.

Angio-Seal (Terumo Medical Corporations, Somerset, New Jersey) device is indicated for femoral arteriotomy closure. Real-world published data on complications are limited. We present 1 year of safety events involving Angio-Seal from the US Food and Drug Administration's post-market surveillance database of Manufacturer and User Facility Device Experience (MAUDE). Steps for managing frequent device-related problems are discussed. Angio-Seal MAUDE data from November 2019 to December 2020 was classified according to (1) mode of device failure, (2) complication, (3) treatment, and (4) Cardiovascular and Interventional Radiological Society of Europe (CIRSE) adverse event classification system. There were 715 safety events, involving Angio-Seal VIP (93.1%), Evolution (5.7%), STS Plus (1.1%), and sizes 6F (62.5%) and 8F (37.5%). Failure mode involved unrecognized use of a damaged device (43.4%), failed deployment (20.1%), failed arterial advancement (6.3%), detachment of device component (4.9%), failed retraction (3.6%), operator error (1.1%), and indeterminate (20.6%). Of total, 44.8% of events were associated with patient harm. Complications involved minor blood loss (34.1%), hematoma (5.6%), significant blood loss (1.4%), and pseudoaneurysm (1.4%). Of total, 43.3% of cases required manual compression (MC), whereas 8.8% required more advanced intervention. Interventions included surgical repair (49.2%), thrombin injection (9.5%), balloon tamponade (6.3%), covered stent (4.8%), and unspecified (30.2%). Majority of safety events were CIRSE grade 1 (92.0%), followed by grades 2 (3.1%), 3 (4.6%), and 6 (deaths, 0.3%). Minority of devices were returned for manufacturer analysis (27.8%). The majority of safety events were associated with minor blood loss or local hematoma and could be addressed with MC alone. Most events were attributed to damaged device; however, very few devices were returned to manufacturer for analysis. This should be encouraged to allow for root cause analysis in order to improve safety profile of devices. System-level strategies for addressing barriers to under-reporting of safety events may also be considered. Our study highlights important safety events encountered in real-world practice with Angio-Seal closure device. The MAUDE database captures real-world device malfunctions not typically appreciated in conventional clinical trials. Our study provides valuable insight for clinician-users on anticipating and managing the most common device malfunctions. Additionally, our data provide feedback for manufactures to optimize product design and direct manufacturer user training to improve safety. Finally, we hope that the study promotes system-level strategies that foster reporting of safety events and undertaking of root cause analysis.

User behavior data analysis and product design optimization algorithm based on deep learning

User behavior data analysis and product design optimization algorithm based on deep learning

Bayesian Φ q -optimal designs for multi-factor additive non linear models with heteroscedastic errors

This article considers Bayesian Φ q -optimal designs for multi-factor additive non linear models where model errors are heteroscedastic. For additive non linear models with a constant term, a sufficient condition is given in order to derive Bayesian Φ q -optimal product designs, which are achieved from univariate optimal designs with respect to every marginal model with a single factor. However, in the case of ignoring a constant term, an additional assumption of orthogonality is proposed to ensure that optimal designs can be found. Then, the corresponding optimal product designs can be built with the help of the equivalence theorem for the Bayesian Φ q -optimality criterion. Several examples are given to illustrate the effectiveness of theoretical results on optimal product designs.

Sustainability aspects of additive manufacturing: Leveraging resource efficiency via product design optimization and laser powder bed fusion

Natural resource is used for producing useful components and in the process fractions of Natural resources are used for producing useful components and in the process fractions of valuable resources are lost as waste or emissions into the ecosystem. The continuous usage of natural resources causes their scarcity alongside the negative impacts. Manufacturing sectors aim to control these impacts. Industry 4.0 (I4.0) technologies including additive manufacturing (AM), simulations, internet of things and connectivity promise resource consumption, functions and sustainability improvements.Simulation-assisted design for AM boosts productivity via swift and novel product designs to achieve resource efficiency, cost effectiveness, and process efficacy. New integrated and iterative methods of creating optimized product designs enabled by I4.0 elements offer alternative route of light weighting, and digitalization. Optimized designs help control resource scarcity including efficient use of raw materials, energy efficient products, reduced waste, and pollution to achieve sustainable goals. This study aims to guide AM users on how computer design and simulation assisting tools help to digitally create optimized lightweight products, test performance, plan manufacturing without committing to physical manufacturing cost. The study using review and experimental case study shows how AM and, particularly, laser powder bed fusion (LPBF) through optimized product designs influence either component functionality, build weight or manufacturing time towards resource and energy improvements. The results of the experimental study show prospective scenarios for maximizing resource efficiency in L-PBF through combined build and optimized build orientations. This study adds to ongoing sustainability aspects of AM relating to product design optimization in support to sustainable excellence.

Additive manufacturing best practices and its impact on supply chain performance through structural equation modelling: A case study in Ethiopian footwear industry

The aim of this study is to analyze the effects of additive manufacturing (AM) best practices on supply chain (SC) performance. The study developed conceptual framework with four AM best practice as independent variables and five SC performance measuring factors as dependent variables. To test the developed model and hypothesis, data’s has been collected from 129 respondents from 29 footwear industries in Ethiopia. AMOS v.23 is used to test model fit with confirmatory factor analysis (CFA) (χ2/df, RMSEA, CFI, GFI, RMR, Tucker-Lewis index (TLI)). Reliability analysis was carried out to test the reliability and stability of the research questionnaires. Correlation analysis was performed to test the strength and association between dependent and independent variables. And to test the developed hypothesis structural equation modelling (SEM) or paths analysis was used. The findings of the CFA result indicated that data fit the model reasonably well. The finds of correlation analysis indicated that, AM best practices have positive, strong and significant relationship with cost, time and reliability related factors of SC performance. But it has very weak and insignificant relationship with customer related factors. In addition, the findings revelled that AM best practices have negative and insignificant relationship with supplier related factors. On the other hands, the findings of SEM confirmed that the identified four AM best practices positively and significantly improve SC performance. And this study concludes that manufacturing flexibility, material utilization, environmental control and product design optimization of AM plays a vital role in improving SC performance.

Integrated Sustainable Product Design With Warranty and End-of-Use Considerations

Abstract The concept of integrated sustainable product design has recently emerged, aiming to incorporate downstream life cycle performance into the initial product design to enhance sustainability. Various sustainable product design tools based on life cycle assessment or quality function deployment have been established while the impact of reliability on circular practices has received limited attention. Recognizing the critical role of product reliability in post-design performance, this paper develops a product design optimization model that considers the warranty performance and the effect of end-of-use options. The model takes into account the effect of uncertain operating conditions on product reliability. Two optimization goals including the minimization of expected unit life cycle cost and environmental impact are achieved by the model. To demonstrate the benefits of the integrated approach, the model is applied to an electric motor design problem. The results highlight that integrating end-of-use options in the early design phase leads to adjustments in component selection and reliability design. Moreover, the circular utilization of used products enables cost savings throughout the product’s life cycle and contributes to environmental impact reduction. Lastly, the study analyzes the effects of operating conditions, warranty policies, and take-back prices for used products on design decisions, providing valuable insights for product designers.

Structural Analysis and Design Optimization of Ceramic Products Based on 3D Scene Simplification Algorithm

Structural Analysis and Design Optimization of Ceramic Products Based on 3D Scene Simplification Algorithm

Design and evaluation of a sustainable entropy-weighted and VIKOR-based method for offshore oil collecting

With the expansion of marine oil exploitation, offshore oil leakage has become an urgent problem that cannot be ignored in marine ecological protection. To address the problem, this paper proposes a comprehensive design and evaluation approach aiming at the design of offshore oil collection. This approach combines the entropy weight and VIKOR method with three principles of sustainable design. Specifically, to establish the evaluation model, this paper transforms these principles of sustainability into three aspects of practicality, economy, and environmental protection, thus constructing a multi-level evaluation index system. Subsequently, entropy weights are then used to decline the one's own element in the decision maker's weights. Besides this, the VIKOR method is applied to acquire the optimal ranking of design alternatives for offshore oil collection to choose the optimal product design strategy. According to the obtained experimental results, the proposed design method combining the entropy weight and the VIKOR method is feasible and reasonable. To be exact, it improves the comprehensive index of the product in the design concept stage by over 20%, and it effectively optimizes the resource allocation of the design process.

The intervention of consumer product design optimization incorporating color psychology on consumers’ anxiety disorders

BackgroundAny product design cannot do without color design, and color is also an important form of psychological treatment for anxiety disorders. This study is based on color, optimizing product design, intervening, and treating consumer anxiety disorders, and combining conventional medical methods to improve consumers’ sub-health status and generate positive social effects.Subjects and MethodsWe performed a MEDLINE search focusing on the past 10 years. Keywords used were attachment, neurobiology, and psychotherapy. We included original studies and existing reviews looking at all types of formal psychotherapy used and focusing on human research. Exclusion criteria were non-psychotherapeutic interventions and attachment based on couples only.ResultsThe experimental results showed that the number of people who observed red light was greater than that of those who observed blue light, and the number of people who observed green and blue light was 21,25. The order of changing colors has little effect on the experimental results. The level of activity in brain regions varies with color, with darker red areas indicating more active brain activity, while darker blue areas indicating less active brain activity.ConclusionsRed light makes the brain tense and excited, while blue light makes the brain calm and relaxed. Design products with colors, expressing emotions through color combinations on the screen. Faced with the psychological dilemma of anxiety, through the application of color in product design, color optimization is carried out to explore effective color application solutions that can be suitable for various psychological needs.

Shopping for Information: Implications of Consumer Learning for Optimal Pricing and Product Design*

I study a seller's pricing problem where consumers perform costly product research about value before purchase. They buy the product when sufficiently optimistic about value and cease research when sufficiently pessimistic. I find that the seller encourages product research when prior belief about value is high, even though he could sell immediately for a high price. The prior affects both expected value and how additional information changes consumers' beliefs. I show that an increase in research cost affects equilibrium price nonmonotonically. Finally, when the seller chooses price and product value dispersion, the optimal level of dispersion need not be extremal.

Study on the functional relationship between pressure and displacement with time for waist of elastic legwear using FEM

PurposeThe inward displacement perpendicular to the body surface produced by compression garment is an important index to evaluate pressure comfort and optimal design of tight clothing products. The purpose of this study is to explore the pressure distribution state at waist position of elastic legwear and then to solve the common problem of excessive pressure or easy slippage for waist of elastic legwear.Design/methodology/approachIn this paper, the authors obtained the waist cross-section model of human body using CT scanning and mimics modeling and then simulated the pressure and displacement distribution after wearing sample four elastic legwear using finite element method. The dressing process of elastic legwear was divided into six periods (instantaneous, 1, 2, 4, 8 and 12 h) in this study, and the finite element software ANSYS was used to simulate the displacement and deformation of the waist cross section. The authors finally obtained the functional relationship between pressure/displacement ratio and angle using curve fitting.FindingsIn this paper, the authors obtained the functional relationship between pressure/displacement ratio and angle using curve fitting. Comparison found that the “pressure/displacement–angle” function curve showed an almost consistent trend at any time. That was to say, when the human body was in the state of clothing pressure, the corresponding displacement value of the human body can be calculated by the curve equation under the premise of known pressure value.Originality/valueThis study solves the difficult problem which hard to measure displacement values by conventional methods due to the small deformation of the human body after dressing the compression garment. Conclusions also provide a theoretical reference for evaluating pressure comfort and optimizing clothing structure for the elastic legwear, and this method is also applicable to other types of compression garment.

A multi-subgroup decision-making method for design selection based on subjective reports and objective physiological index data

Selecting an optimal product design scheme requires considering multiple criteria and engaging the wisdom of crowds. However, existing studies regarding design selection rarely considered the participation of group wisdom, and few studies considered subjective reports and physiological index data comprehensively for evaluation. This study developed a multi-subgroup decision-making method based on the participation of designers, experts, and customers. Multi-modal aggregation strategies are applied to aggregate subjective evaluations of both individuals and groups represented in probabilistic linguistic term sets (PLTSs), objective eye movement, and Electroencephalogram feature data. Specifically, a probabilistic linguistic information aggregation operator based on the Choquet integral is proposed to aggregate the values of subjective criteria with correlations, and the double normalization-based multi-aggregation method is then applied to aggregate objective criteria values, which can avoid information loss in the normalization and aggregation. Finally, the Choquet integral-based Borda operator is developed to integrate the evaluation results of multiple subgroups. An example of evaluating intelligent shared vehicle design schemes is presented to validate the proposed method.

Topology Optimization of Electric Train Cable Carrier

In the area of mobility (transport), great efforts are being made to optimize means of transport. While reducing their mass the main goal is to maintain or even improve the performance of the means of transport. Additive manufacturing allows the production of very complex geometric shapes of products that cannot be made by traditional production processes; therefore it has become one of the significant tools in the design of modern vehicles or their parts. Additive manufacturing, combined with appropriate computer tools for numerical design analysis, also allows optimization of product design by topology optimization. In this paper, concept of topology optimization is applied to the roof carrier of a high voltage cable of electric train. Design optimization process was conducted based on two different optimization criteria: minimized part mass and maximized part stiffness.

Quality Monitoring of Resistance Spot Welding Based on a Digital Twin

As an important means to realize intelligent manufacturing, a digital twin is a digital expression of physical entities, which realizes virtual–real interaction and the iterative optimization of product design and manufacturing by constructing a bridge of information mapping between the physical world and the virtual world. Resistance spot welding technology is widely used in automotive manufacturing, aerospace and other fields as a spot linking process for the manufacture of thin sheet structures. The fusion nugget growth process of resistance spot welding is particularly important for its joint quality. Resistance spot welding is a highly nonlinear coupled process, and physical models make it difficult to accurately monitor its quality. Taking 2219/5A06 aluminum plates with different thicknesses as the research object, digital twin technology is applied to monitor the welding process of aluminum plate. In order to improve the key technologies such as information interaction in the digital twin system, a data acquisition system for resistance spot welding process is established and a real-time data processing technology based on wavelet threshold analysis is proposed. Based on real-time data, the processed process parameters are tested in twin space to validate the feasibility of the solution.

Application of Artificial Intelligence in Spacecraft Ground Testing

With the accelerated application of new-generation information technologies, developed countries have implemented digitalization strategies to gain a competitive advantage in the digital era. Artificial intelligence (AI) is widely used in a wide range of modern industries. Our country has put increasing emphasis on AI technologies, and our aerospace industry is being more digitalized. This study focuses on the application prospects of AI in spacecraft ground testing. It aims to build smart labs driven by products and discusses how to integrate AI into the process of quality supervision, instability prediction, situational awareness analysis for equipment, and optimization of knowledge-driven product design, thus digitalizing spacecraft ground tests.