Product Design Process Research Articles

How Industry 4.0 and Sensors Can Leverage Product Design: Opportunities and Challenges.

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. Thus, it becomes imperative to understand how these innovations can leverage product design to maximize benefits and opportunities. This research paper employs a Systematic Literature Review with Bibliometric Analysis (SLBA) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.

Robust optimization design method for structural reliability based on active-learning MPA-BP neural network

PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

Efficient Development of Applied Technology Innovation Through Design For Manufacture and Assembly

The conventional product design process and the design results from the designer need to be evaluated and optimized in terms of the manufacturing process and assembly process by the manufacturing department. In this process, it is sometimes necessary to make design changes that must be consulted with the designer to make it more efficient. This can be seen from three aspects of the product, namely quality, cost, and production time. A very applicable method to meet the needs of these 3 aspects of the product is the design method for manufacturing. One solution that can be applied is the design for manufacture and assembly (DFMA) method. DFMA is a method to evaluate the design of a product so that it can be easily assembled and manufactured. This paper will explain the application of the DFMA method to several applied technology product designs. As a case example for the application of the DFMA method, the development of a post-stroke bicycle frame from the initial design to the final design step, namely prototyping, is observed. A prototype was also evaluated with the help of the DFMA method and the evaluation results showed that the application of the DFM method provided significant improvements, where the total components of the bicycle frame at the beginning of the design were 53 pieces, in the final design could be reduced to 29 pieces (45%), with design efficiency increasing by 1.04%.

Integrating kansei engineering and interactive genetic algorithm in jiangxi red cultural and creative product design

To show the unique charm of Jiangxi’s traditional culture, it is of great importance to apply Jiangxi’s unique red culture to products’ creative designs. This paper aims to apply Kansei Engineering (KE) and interactive genetic algorithm (IGA) to extract the apparent symbol elements of Jiangxi red culture and then transform them into the creative watch design with modern culture. First of all, KE is used to extract customers’ emotional resonance to red culture, and 16 pairs of Kansei image vocabulary pairs are preliminarily collected. The theory of semiotics is used to extract symbols such as shapes, colors, and patterns from the perspective of Jiangxi’s red architecture. Secondly, through the designers’ subjective aesthetic thinking, these cultural symbols are broken up and reconstructed, thus forming the morphological deconstruction table combined with the case of the watch. Finally, IGA is implemented to code and decode the cultural symbols, thus building a product form’s evolutionary design system. Through biological genetic manipulation, cultural symbols of refinement, particularity, and regionality are retained. Then these superior cultural genes are integrated into the innovation of the watch to get creative products with the characteristics of Jiangxi red culture. The model proposed in this paper optimizes the decision-making process of cultural creative product design, and also explores a sustainable development path of culture.

Process Design for Value-Added Products in a Biorefinery Platform from Agro and Forest Industrial Byproducts

Agroforestry wastes are industrial byproducts available locally such as eucalyptus sawdust (EUC) and sugarcane bagasse (SCB). These byproducts can be used as lignocellulosic raw materials to produce high-value products. This study is a techno-economic analysis of four potential scenarios to produce polyhydroxybutyrate (PHB) and levulinic acid (LA) from hemicellulosic sugars by a fermentative pathway in a biomass waste biorefinery. Mass and energy balances were developed, and technical and economic assessments were carried out to obtain gas, char, and tar from residual solids from autohydrolysis treatment. It was determined that microbial culture could be an attractive option for added-value product production. More than 1500 t/year of PHB and 2600 t/year of LA could be obtained by the proposed pathways. Microbial and enzymatic conversion of LA from sugars could significantly improve energy consumption on the conversion strategy. The products from solid residual valorization (char and tar) are the most important for economic performance. Finally, a variation in specific variables could mean substantial improvements in the final indicators of the processes, reaching a higher NPV than USD 17 million.

A New Hybrid Fermatean Fuzzy Set and Entropy Method for Risk Assessment

Risk evaluation is an important part of the product design and product manufacturing process; it entails the pursuit of the highest product quality and preventing failure under the constraints of limited resources. The failure mode and effects analysis approach is one of the most widely applied risk evaluation tools that uses the product of the three risk elements of product failure items, severity, occurrence probability, and detection probability, to calculate the risk priority number, the priority of failure risk. However, the typical failure mode and effects analysis method ignores the consideration of objective weights, which may lead to incorrect evaluation results. Moreover, the method of expressing information about product failure provided by experts also directly affects the results of risk assessment. To comprehensively assess the risk of product failure, in this study, the hybrid of the Fermatean fuzzy set and entropy method was used to prioritize product failure items risk. This study used a service failure mode and effects analysis numerical example of self-service electric vehicles to illustrate and test the correctness of the proposed new hybrid Fermatean fuzzy set and entropy method. The mathematical operation results were also compared with the listing of different calculation methods. The test results prove that the proposed new hybrid Fermatean fuzzy set and entropy method can fully consider the cognitive information provided by experts to provide more accurate risk ranking results of failure items.

PKC– Production Knowledge Chart for Knowledge Transfer

In the design of production processes in the manufacturing industry, it is necessary to transfer the defect investigation knowledge, which is the empirical knowledge of experienced workers, to beginners. Leonard and Swap pointed out the importance of acquiring empirical knowledge through experimental learning. However, they have not clarified the explicit empirical knowledge representation method and the experimental learning method. If the knowledge cannot be represented, empirical knowledge remains tacit, and the knowledge transfer from experts to beginners is not only individual and inefficient, but also difficult to reuse.In this paper, we propose a production knowledge chart (PKC) that expresses the production process to acquire the empirical knowledge necessary for investigating defects in the production process. In addition, we clarify the effectiveness of the proposed method by expressing a general production process with PKC. In addition, we will discuss future research.

Implicit multi-sensor reconstruction based on neural signed distance functions for reverse engineering

Reverse engineering has been gaining considerable interest in the product design process, in which the obtaining of measurement data from digitizing sensors are important beginning procedures. Considering the requirements of both accuracy and efficiency, multi-sensor measurement and reconstruction emerge as promising techniques to take advantage of the complementary capabilities of the different sensors. However, most exiting multi-sensor reconstruction methods are only applicable to explicit surfaces, which are defined by parametric equations with two parameters. The limitation of explicit representation significantly restricts the further application of multi-sensors reconstruction for more complex products. This paper proposes an implicit multi-sensor reconstruction method, in which the surface is represented implicitly by a deep neural network controlled by the signed distance function. Therefore, the multi-sensor reconstruction problem is then transferred to a multi-sources neural network training problem, which can be addressed by parameters frozen and finetuning techniques from the transfer learning field. The reconstruction experiments on two real workpieces show the effectiveness of the proposed method, and the limitations of neural network representation are also discussed.

Creative Exploration through Idea Sketches in the Product Design Process: A Case of the Designers Highly Skilled in Digital Technologies

For developing the creativity support system, to understand human creative activity is crucial. In the field of product design, current designers aimed at creating novel user experiences have been recently prevalent for designers to generate ideas rather than determine user needs. Therefore, dialogues between the designers and users tend to be valued in gathering user requirements. This research aims to investigate the designer's exploration by interpretation of idea sketches by reflection. Three cases are discussed: the designer tried to create awareness on how designers can understand the users' real requirements through dialogue, to find inexplicit requirements of the users, and to generate the images from the users' denial comments. To investigate idea generation processes, a designer investigated their own sketches and determined key points of detecting the users' real requirements. This paper also discusses the influence of digital design technologies from the perspective of creativity.

State of the art applications of engineering graphics and design to enhance innovative product design: a systematic review

The skills and abilities that Engineering Graphics and Design (EGD) has to offer is critical to support creation of product design in various fields such as architecture, mechanical, electrical, civil etc. To date, the knowledge of EGD in the product design process has not been fully harnessed by design experts in the field. Thus, this study aims to investigate the applications of EGD and provide its prospects in various technical fields to enhance future product design process. In this study, a systematic literature review using articles from various academic research data bases were used. The results of the study revealed that application of EGD in product design originates back from the creation traditional drawing to more sophisticated three dimensional methods of drafting such as computer aided design (CAD). A review of pedagogical techniques in EGD revealed that several teaching strategies may be employed to improve teaching and encourage students to take a more in-depth approach to learning. Furthermore, the results showed that the knowledge of EGD and its related tools enhance creativity in design of innovative products. The study provides prospect on the application of EGD in practice with the view to improve product design.

Generation of bicycle frame image designs using DCGAN network

The crucial step in the creation of every new product is the design process which is highly subjective and time-consuming. Artificially generated design suggestions can help to accelerate this process. We propose a method which can automatically generate 2D images with different design ideas for the specific design nodes. Images are generated by the DCGAN network. The DCGAN model is trained using real data in combination with synthetically generated images. Synthetic data is created using the generative design module of PTC Creo Parametric. In the paper, we train the DCGAN model to generate bicycle frame images. After some improvements to the model, it can become a helpful tool in the product design process.

A Mixed Reality-enhanced Rapid Prototyping Approach for Industrial Articulated Products

The design of industrial articulated products is challenging due to the consideration of multiple poses, movements, and especially in-field constraints, such as the design of a personalized robot arm or the layout of articulated hoists. Traditionally, depicting 2D sketches from multiple perspectives and poses of the product is tedious. Meanwhile, on-site evaluation after fabricating the physical prototype is costly and time-consuming. In order to assist in context-aware decision-making at the early design stage, this research proposes a mixed reality-enhanced rapid prototyping approach for industrial articulated products. In a 3D mixed reality environment that incorporates the surroundings, designers can depict key joints and rough shapes through intuitive gesture-based interaction. After a quick prototype creation, the kinematics preview is provided to designers for preliminary functional verification. Finally, a user study shows that the proposed approach can help designers rapidly complete the MR prototype of articulated products at the early design stage, provide a better understanding of the product movement, assist in-field solution generation and facilitate communication. It is hoped this explorative study can offer insightful ideas with effective toolkits for assisting the new product design process more intuitively and interactively.

Proposal, design, and cost analysis of a hydrogen production process from cellulose via supercritical water gasification.

Hydrogen production from biomass, a renewable resource, has been attracting attention in recent years. We conduct a detailed process design for cellulose-derived hydrogen production via glucose using supercritical water gasification technology. Gasification of biomass in supercritical water offers advantages over conventional biomass conversion methods, including high gasification efficiency, elevated hydrogen molar fractions, and the minimization of drying process for wet biomass. In the process design, a continuous tank reactor is employed because the reaction in the glucose production process involves solids, and using a tube-type reactor may clog the reactor with solids. In the glucose separation process, glucose and levulinic acid, which cannot be separated by boiling point difference, are separated by using an extraction column. In the hydrogen separation process, the hydrogen purity, which could not be sufficiently increased with a single pressure swing adsorption (PSA) process, is increased to the target value by employing two sets of PSA columns. The overall utility cost is significantly reduced by $0.020/mol-H2 through heat integration. Our economic evaluation for this process results in a deficit of $0.015/mol-H2, as a price to be paid by the human for renewable hydrogen production from biomass at the present stage. By simply adopting the reported experimental condition, our process contains a large amount of water and sulfuric acid, which requires an enormous cost for the neutralizer, drying utility, and extractant. To improve the economic performance of the process, it is necessary to consider the reaction of cellulose solution at a higher concentration to reduce the burden of glucose separation. In addition, the effective use of the wasted hydrogen with a purity of about 95 vol% from the second PSA column may also improve the process economics. Whilst, the required energy cost for hydrogen production for our process is calculated to be significantly lower than those for other various representative hydrogen production methods: 0.37 (0.44) times less than that of steam reforming of methane with (without) CO2 capture, 0.15 times less than that of the water electrolysis by the electric power system, and 0.073 times less than that of electrolysis of water by wind power. This result implies the practical potential of our cellulose-based green hydrogen production scheme.

Molecular dynamics simulation of the amorphous graphite-like membrane deposited on ideal graphite substrate.

To understand the growth mechanism of pyrolytic graphite on ideal graphite substrate by chemical vapor deposition (CVD), the process simulated by molecular dynamics is performed. The simulation revealed that only an amorphous graphite-like membrane (a-GLM) can be obtained using the Tersoff potential function. It is revealed that the formation process for the first layer of the a-GLM initiates from the firstly deposited carbon monomers, dimers, and trimers as growth points, on which Y-shaped long chain is formed, and then the network with pores are developed, and finally the graphene-like layer with six-membered dominated rings appears after repair treatment. The following deposition simulation for more deposited carbon atoms indicates that a second layer like-graphene is more prone to superpose onto the microdomains of the first layer like-graphene until the formation of the island-like bumps, and the concave zone around the bumps will be filled by deposited atoms and modified by defect repair. The growth mechanism for pyrolytic graphite prepared by CVD can be deduced by the simulation inspiration. The optimal deposition temperature of 2400 K is acquired by evaluating the integrity of the first deposited graphene film by statistics of five-to-seven-membered carbon ring number. This study can provide theoretical references for the design of the pyrolytic graphite production process.

Optimized design and techno-economic analysis of novel DME production processes

Novel process concepts for DME production based on reactive distillation were proposed and evaluated techno-economically.

A DESCRIPTIVE REVIEW ON PROCESS ANALYTICAL TECHNOLOGY

Measurement of essential process parameters that impact critical quality characteristics is at the heart of process analytical technology (PAT), which is used in the design, analysis, and control of pharmaceutical production processes. Physical and Chemical Attribute Testing (PAT) ensures high quality raw materials (i.e. at off-line, on-line, in-line). With PAT, we move away from testing the structures themselves and instead test the goods themselves at various stages of production. PAT drastically reduces the resources needed for product sampling and testing. In order to be effective, PAT must provide instruments such contemporary process analysers or analytical chemistry, endpoint process monitoring and regulating instruments, and continuous improvement and knowledge enhancement instruments. In this review, we've tried to dig into what PAT is, what it's for, what kinds of tools you can use for it, what problems it's meant to solve, how it operates, and what advantages it offers.

A DESCRIPTIVE REVIEW ON PROCESS ANALYTICAL TECHNOLOGY

Measurement of essential process parameters that impact critical quality characteristics is at the heart of process analytical technology (PAT), which is used in the design, analysis, and control of pharmaceutical production processes. Physical and Chemical Attribute Testing (PAT) ensures high quality raw materials (i.e. at off-line, on-line, in-line). With PAT, we move away from testing the structures themselves and instead test the goods themselves at various stages of production.

A DESCRIPTIVE REVIEW ON PROCESS ANALYTICAL TECHNOLOGY

Measurement of essential process parameters that impact critical quality characteristics is at the heart of process analytical technology (PAT), which is used in the design, analysis, and control of pharmaceutical production processes. Physical and Chemical Attribute Testing (PAT) ensures high quality raw materials (i.e. at off-line, on-line, in-line). With PAT, we move away from testing the structures themselves and instead test the goods themselves at various stages of production. PAT drastically reduces the resources needed for product sampling and testing. In order to be effective, PAT must provide instruments such contemporary process analysers or analytical chemistry, endpoint process monitoring and regulating instruments, and continuous improvement and knowledge enhancement instruments. In this review, we've tried to dig into what PAT is, what it's for, what kinds of tools you can use for it, what problems it's meant to solve, how it operates, and what advantages it offers.

A reliability analysis method based on fault tree analysis and analytic hierarchy process

In view of the limitation that Fault tree analysis ignores many factors that have an impact on reliability allocation, making the allocation result unreasonable and unable to obtain the optimal allocation result, a fault analytic hierarchy process based on analytic hierarchy process (AHP) is proposed, Combine the subjective evaluation of experts with the objective calculation results of fault trees. This method is applied to various devices and systems, allowing for a deep understanding of the main factors affecting the system and their degree of correlation, thereby effectively completing maintenance work, improving equipment reliability, and providing important basis for product design and production processes.

A Combined Reverse Engineering and Multi-Criteria Decision-Making Approach for Remanufacturing a Classic Car Part

Engineering Design and Additive Manufacturing (AM) technologies play a significant enabling role for business models with a proposed contribution to lifecycle management strategies for extending product life and enabling more efficient production via the digitisation of product design and production processes. This paper demonstrates a strategic approach for reverse engineering (RE) to support new business models capitalising on economic and organisational contexts such as on-demand and decentralised manufacturing and digital automation without the requirement for tooling. Additionally, it integrates a decision-making tool to evaluate the strategy of the various RE processes. This study provides an informed comparison between workflows of non-contact data acquisition using photogrammetry, metrology grade laser scanner, and 3D re-modelling using scanned data as a reference. This approach creates a digital thread for end-of-life (EOL) parts and enables remanufacturing using AM processes. The workflows are documented and assessed based on the manufacturing triangle of cost, time, and quality as application-relevant design pillars for evaluating the suitability of the business case proposed, namely, remanufacturing of parts for classic car restoration projects. The calculated and measured results based on the three different approaches were assessed through a Multi-Criteria-Decision-Making (MCDM) process, more precisely, the Analytic Hierarchy Process (AHP). The outcome of the study shows the general applicability of the proposed workflow. The combination of the measured and calculated data of the remanufactured parts and the decision-making tool provided the following ranking of the proposed work to the practitioner: (1) laser scanning, (2) 3D modelling, and (3) photogrammetry.