Product Design Process Research Articles

Production Technological Design for 500,000 Tonnes Per Year of Ethylene Glycol

Effective and reasonable chemical design of ethtlene glycol production process is of vital significance for the further improvement of our ethylene glycol production capacity. This paper carried out reasonable design of ethylene glycol water lawful production process, using reaction rectifying tower device, and optimizing calculation for each link of production, so as to obtain satisfactory products. In the comprehensive consideration of production efficiency and cost saving and other aspects of the relevant equipment is reasonable selection, and draw the PID process flow chart. It is expected to produce 500,000 tons of pharmaceutical grade glycol with mass fraction of 99.89%.

The Content-Specific Display: Between Medium and Metaphor

This paper examines the current generation of displays, as found primarily in smartphones, laptops and tablet computers, from an interaction design perspective. Today’s displays are multifunctional, versatile devices with a standardized, rectangular shape and a standardized interaction. We distinguish two pitfalls. First, they facilitate an interaction that is isolated and detached from the physical environment in which they are used. Second, their multi-touch interface provides users with few tangible clues and handles. From our background in embodied interaction, we establish an alternate, hypothetical vision of displays: the content-specific display. The content-specific display is defined as a display designed for one specific function and one type of on-screen content. We explore this concept in three student projects from the First Year Master’s program at Product Development, University of Antwerp, and present two key themes that emerge from it: causality and transformation. Both themes reside in the field of coupling, a concept well-known within the field of embodied interaction, and aim at a more seamless integration of on-screen content within the physical world. Finally, we discuss how the content-specific display influences the design process of digital products, and how it fosters collaboration between product designers and designers of graphical user interfaces.

Self-supporting thin Shells in Italy. Space and Structure of the Industrial Buildings (1930-1970)

In the Twentieth Century, the industrial buildings featured a broad experimentation on the load-bearing structures suited to the production spaces. Among the structural elements explicitly designed for factories were reinforced concrete thin shells. In Italy, where reinforced concrete was the predominant construction technique for the entire century, the evolution of the thin shells for industrial buildings traced a significant story: starting from the introduction of foreign systems and patents in the 1920s, the design and construction of the thin shells gradually stacked to national productive and technological specificities. This paper presents a historical investigation of the application of thin shells in Italy between 1930 and 1970, remarking, thus, the local contribution of Italian contractors and designers in the evolution of both the design solutions and production process. Furthermore, the study presents a systematic historical and technical framework related to thin shells to support the actions of knowledge and safeguard the industrial building heritage.

The Concept of Building an Integrated Digital Ecosystem to Improve the Quality of Products and Services of Defense Industry Enterprises

Research and development of systems for managing material, technical, and intellectual resources are key directions for improving manufacturing processes in the modern world. In recent years, the question of integrating various management systems has become particularly relevant due to the widespread adoption of quality management systems and their certification to meet current regulatory requirements. This study attempts to merge the advantages of using product lifecycle information support technologies, such as data and information management, with the concept of digital twins, including virtual design, digital product modeling, and real-time analysis. The analysis covers the constituent elements of the generalized product design and manufacturing process, addressing the construction of an integrated digital ecosystem to enhance the quality of products and services in the defense-industrial complex. This ecosystem is based on the integration of technologies for continuous product information support using their digital twins and an integrated quality management system. It is shown that the approach to evaluating the effectiveness of purposeful processes should be complemented by a risk-oriented model, which includes response scenarios to potential adverse effects of the operation of the purposeful organizational-technical system. A structural scheme and formalized description of a generalized product information support model are presented, allowing for the integration of specific quality indicators into a unified strategic outcome. Ultimately, a concept for building an integrated digital ecosystem for defense-industrial enterprises is proposed, aimed at enhancing the overall quality of products and services.

Application of mechanical crack defect detection based on thermal energy parameter simulation and sensing images in product process design

Application of mechanical crack defect detection based on thermal energy parameter simulation and sensing images in product process design

Thermal environment optimization and process product design of CNC grinding machine based on improved neural network

In the machining process of CNC grinding machine, the non-uniformity of heat source often leads to the deformation of the workpiece, the intensification of wear and the machining error, so the optimization of thermal energy environment has become an important research direction to improve the machining performance. The aim of this study is to optimize the thermal energy environment of CNC grinding machine by improving the neural network algorithm, so as to achieve the goal of improving the machining precision and surface quality. We hope that by establishing thermal environment model, we can deeply analyze the characteristics of thermal energy distribution and put forward effective optimization measures. The improved neural network model is used to model the heat energy distribution of CNC grinding machine. The accuracy of the model is improved by introducing multi-layer perceptron and convolutional neural network. At the same time, combined with the experimental data, training and testing are carried out to verify the effectiveness of the model. Genetic algorithm and fuzzy control are used to adjust the thermal energy environment and obtain the best process parameters. The experimental results show that the prediction accuracy of the improved neural network model is significantly improved, and the prediction error of the model is significantly reduced compared with the traditional method. After thermal optimization, the surface roughness of the workpiece is reduced, the dimensional accuracy is significantly improved, and the overall processing efficiency is significantly improved. By improving the neural network model, the thermal energy environment of CNC grinding machine is effectively optimized, and the machining precision and surface quality are successfully improved.

Comparison of the Torsional Strength of Material Samples Made Using Selected Rapid Prototyping Methods

In recent years, there has been a marked increase in the use of rapid prototyping techniques that support industrial processes. The analysis of studies on strength tests of parts manufactured using additive techniques from model materials and hybrid composites usually includes tests of material and strength parameters based on uniaxial tension, bending, and compression tests. For elements manufactured from polymeric materials using traditional methods (e.g. injection moulding), data obtained from these tests are sufficient to determine the strength of the material and to apply the results in the product design process. In the case of the layered extrusion method, due to the model construction process, strength data based on standard samples do not have a direct transposition on the strength of machine parts operating under various load ranges, especially torsional loads. The publication presents the results of tests on the torsional strength of cylindrical samples with splines produced by layered extrusion, vacuum casting technology, and hybrid technology, which combines both technologies. The test samples were made using a Prusa i3 MK3 3D printer and the plasticised plastic modelling (fused filament fabrication, FFF) method, while PolyJet technology was used to create a reference model. The findings indicate that hybrid technology, in which a thin-walled mould is filled with a chemically hardened polymer, achieves the desired strength for the manufactured parts while maintaining dimensional accuracy and shape. The research results show that producing rotating elements using the hybrid technology reduces the sample core’s ability to undergo plastic deformation due to the adhesion between the materials and the occurrence of notches resulting from the model production process using the layered extrusion method.

Evaluating Large Language Models for Material Selection

Abstract Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting as a useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the need for further research into how LLMs can be better tailored to replicate expert decision-making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements.

Rapid Prototyping Technology: A Driving Force in Product Design Innovation

Abstract: This paper explores the transformative impact of rapid prototyping on product design processes across various industries. Rapid prototyping, characterized by its ability to quickly create physical models from digital designs, significantly enhances the iterative design phase, facilitating early testing and validation of concepts. RP includes the integration of technologies such as 3D printing, computer-aided design (CAD), and additive manufacturing in accelerating product iterations, reducing time-to-market, and fostering innovation. Rapid prototyping (RP) has emerged as a transformative approach in product development, significantly enhancing design processes across various industries. Sectors including automotive, consumer electronics and healthcare show how rapid prototyping streamlines communication among stakeholders, enhances user feedback incorporation, and reduces production costs

Expert gaze behaviour in design evaluation: An eye-tracking study

Expertise research studies show that experts more effectively allocate their attentional resources and exhibit distinct gaze behaviour. This study aims to investigate the effect of design expertise on gaze behaviour during the product-design evaluation process and, consequently, self-reported evaluations indicating different aspects of perceived design quality. We recruited a sample of participants with three levels of expertise (experts, quasi-experts, novices). The results provided partial support for the hypothesized classical expert gaze behaviour (i.e. fewer fixations of longer durations) identified by previous researchers in non-design fields. The distinct gaze pattern in experts was observed when multiple designs were presented simultaneously. Regarding evaluation outcomes, we found statistically non-significant differences in the design ratings and rankings reported by the three expertise groups. Altogether, the findings provide empirical evidence for the impact of design expertise on visual exploration and design judgement. This study provides valuable insight into the appropriateness of crowdsourced design evaluation.

Augmented Reality and Virtual Reality Applications: Enhancing User Experience Across Industries

Augmented Reality (AR) and Virtual Reality (VR) technologies have emerged as powerful tools for enhancing user experiences across various industries. This article explores the applications of AR and VR and their impact on user experiences in diverse sectors. Employing a qualitative method, this study conducts a literature review through library research to examine the current trends and developments in AR and VR applications. The findings reveal that AR and VR technologies have been increasingly adopted across industries such as education, healthcare, entertainment, manufacturing, and retail, among others. In education, AR and VR offer immersive learning experiences, allowing students to interact with digital content in a simulated environment. In healthcare, these technologies enable medical professionals to visualize complex anatomical structures and perform virtual surgeries, leading to improved diagnostic accuracy and patient outcomes. In the entertainment industry, AR and VR provide users with immersive gaming and storytelling experiences. In manufacturing, AR and VR enhance product design, prototyping, and assembly processes, leading to increased efficiency and reduced costs. Lastly, in retail, AR and VR applications enable virtual product try-ons, personalized shopping experiences, and interactive product showcases, thereby enhancing customer engagement and satisfaction. Overall, AR and VR technologies offer promising opportunities to revolutionize user experiences across industries, although challenges such as hardware limitations, content creation, and user acceptance need to be addressed for wider adoption

Weighted unsupervised domain adaptation considering geometry features and engineering performance of 3D design data

The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Recently, deep learning-based engineering performance prediction models have been proposed to accelerate design optimization. However, they only guarantee predictions on training data and may be inaccurate when applied to new domain data. In particular, 3D design data have complex features, which means domains with various distributions exist. Thus, the utilization of deep learning has limitations due to the heavy data collection and training burdens. We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. The proposed method is the first to apply domain adaptation to deep learning-based engineering performance prediction to overcome the problem of insufficient industrial data. In addition, we present a source instance weighting method called the bi-weighting strategy to consider the geometry features and engineering performance of 3D design data and avoid negative transfer for accurate prediction. Domain-invariant features can be extracted through an adversarial training strategy by using hypothesis discrepancy, and a multi-output regression task can be performed with the extracted features to predict the engineering performance. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels. This mechanism can reduce the target risk for unlabeled target domains on the basis of weighted multi-source domain knowledge and can efficiently replace conventional finite element analysis.

Integration of Value Engineering for Design for Assembly in Product Design: A Comprehensive of Literature Review

This study explains the benefits obtained from the integration of Design for Assembly (DFA) and Value Engineering (VE) methods in the product design process. DFA is proven to provide benefits such as product simplification, reduced assembly costs, and improved quality by creating product structures that are simpler and easier to install. While VE adds value by taking into account the replacement of expensive or difficult-to-assemble components, speeding up overall assembly time. Although specific research on the integration of these two methods may be limited, studies show that the combination of DFA and VE produces design alternatives that consider various aspects of the product life cycle. The result is reduced costs, faster development times, and a reduced need for rework. The concepts and benefits of integrative DFA and VE approach can be an effective strategy for enhancing assembly efficiency and reducing production costs in product design.

Design Method of Museum Cultural and Creative Products Based on Multi sensory Experience

To explore the design method of museum cultural and creative products based on multi sensory experience, improve product level and user experience, and provide new directions for the development of museum cultural and creative design. The method first introduces the cognitive principles and sensory expression methods of sensory experience, and then focuses on the study of three common multi sensory phenomena: multi sensory integration, synesthesia, and sensory compensation. It analyzes their application in the design of cultural and creative products, summarizes the transformation process of user generated multi sensory experience, and establishes a multi sensory experience cultural and creative product design process based on this. Conclusion: Product design with multi sensory experience can bring users a more comprehensive and profound experience, help promote the inheritance and development of museum culture, and improve the serious problem of homogenization in the cultural and creative industry.

The relevance of introducing a requirements management system in the production process of the aircraft engine construction industry

It is necessary to have access to documents containing product requirements during the design process. Search and processing of this data slows down the product design significantly. The reduction of time losses calls for the availability of a proper software tool. In this work, programs for the creation of requirements management systems are considered. The proposed concept of the requirements management system will make it possible to increase the efficiency of the product design process. The identified potential of the software allows us to talk about the possibility of introducing this program into the production process of enterprises in the aircraft engine industry.

Simulation and conceptual design of an aniline production process from catalytic hydrogenation of nitrobenzene in ChemCAD

Aniline is a very important compound in organic chemistry, mainly used to synthesize polyurethane and its precursors. In the present work, the simulation and conceptual design of an aniline production process through the catalytic hydrogenation route of nitrobenzene was carried out using ChemCADÒ simulator. The simulated aniline production process consisted of a fired heater, a tubular reactor, several heat exchangers, a phase separator, a decanter, as well as two distillation columns. By means of the simulation, the temperatures, pressures, mass flowrates and enthalpies of the main intermediate and final streams were known, as well as the main operating and design parameters of some equipment. About 1,743 kg/h of aniline with a purity of 99.78% are produced at the top stream of the second distillation column, while about 49,881 kg/h of hydrogen are obtained as a by-product in the phase separator. In this work a first-of-its-kind ChemCAD simulation model was obtained in this work, which can be utilized for further optimization studies and tasks involving the increment of aniline purity, yields and productivity.

Effect of Escherichia coli on the photocatalytic behavior of TiO2 nanoparticles for H2 evolution from water

Various microorganisms are present in natural waters, and understanding their impact on the photocatalytic behavior of photocatalysts is crucial, especially in large-scale hydrogen production. In line with this, we conducted a systematic investigation into the influence of Escherichia coli on the photocatalytic performance of TiO2 nanoparticles for H2 evolution. The findings revealed a significant reduction in the photocatalytic activity of TiO2 nanoparticles in the presence of Escherichia coli. Specifically, the H2 evolution rate was observed to be 57% lower (8.3 mmol g−1 h−1) compared to when Escherichia coli was absent (19.1 mmol g−1 h−1). The primary reason for this phenomenon appears to be the rupture of Escherichia coli cells during the photocatalytic process. As a result, intracellular proteins are released and subsequently adsorbed onto the surface of TiO2 nanoparticles, impeding the photocatalytic hydrogen evolution reaction. These results provide valuable insights that can inform the design of future photocatalytic hydrogen production processes.

Product Development and Design Framework Based on Interactive Innovation in the Metaverse Perspective

Based on the theory of user needs and the product development mode and framework of mobile Internet interactive innovation, a new “reality → virtual → reality” interactive innovation product development mode is constructed. It draws on the unique characteristics, systematic technical system, and comprehensive scientific and technological layout of the Metaverse. On this basis, a framework for product development and design based on interactive innovation from the Metaverse perspective is innovatively proposed. In the Metaverse scenario, interactive innovation knowledge can be easily and effectively transformed into design knowledge, and all groups of users truly participate in the whole process of product design. Moreover, the development of interactive innovative products in the Metaverse scenario can be combined with artificial intelligence (AI) technology to further automate the statistical analysis of user needs and preferences so as to meet the dynamic needs of users and accurately develop products that fit user needs and enterprise standards. In addition, users, designers, and enterprises can make joint decisions on product design solutions and development forms, and the Metaverse technology can also optimize the products with continuous iteration and obtain the optimal solutions. An automotive case study illustrates the feasibility and effectiveness of the model for product development innovation and enterprise digital transformation.

제품디자인에서 생성형 인공지능 활용을 통한 협업프로세스 활용 가능성 탐색

With the rapid advancement of generative AI, most industries are entering a new phase. Generative AI is being utilized across a wide range of fields, including design. Consequently, this study explored the application of AI in the product design field and integrated it into the design process. The goal was to identify the differences between traditional product design processes and those enhanced by AI. Through this research, the potential of AI in product design was confirmed, and future methods of its application were investigated. First, the study considered the domains of human and AI design separately. AI's role was seen as generating and displaying various images, while humans decided on keywords and concrete designs. Next, experimental results indicated that at crucial moments in the design process, the designer’s ability and judgment in selecting designs remained important. Additionally, the effective use of prompts through questions became crucial in applying generative AI. Language, as the culmination of human knowledge and the most fundamental means of expression, plays a critical role. Ultimately, the effectiveness and efficiency of AI usage will depend on the user's level of knowledge in the respective field or their ability to extract knowledge from AI learning. As AI technology advances, paradigms across all industries are shifting. This phenomenon will merge with traditional design methods, resulting in an evolved form of design approach.

Optimization to identify the adapted product design and product adaptation process with initial evaluation of information quality in branches of AND-OR tree based on information entropy

Adaptable products are used to satisfy new customer requirements through changes of products such as upgrading of modules during their utilization stages. Compared with the creation of a new product from scratch, the process to adapt an existing product to a new one can result in savings for the customer. In this research, a new optimization approach to identify the adapted product design and product adaptation process is developed with initial evaluation of information quality in branches of AND-OR tree based on information entropy. In this approach, various candidates of design configurations and operation processes for adaptation of a product are modelled by an AND-OR tree with design and process nodes. Design and process nodes are further associated with design and process parameters. Optimization to identify the best adapted design and adaptation process is conducted at two levels: configuration/process optimization level and parameter optimization level. Information entropy is employed to evaluate the information quality of partial configuration/process candidates modelled as branches in the AND-OR tree to eliminate the branches that are unlikely to lead to the optimal solution for improvement of the optimization efficiency. A numerical example and an engineering application are developed to demonstrate the effectiveness of the newly introduced approach.