Complex Product Design Research Articles

Multidisciplinary Reliability Design Optimization Modeling Based on SysML

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

Design of a Hierarchical-Type Control System Based on Smart MBD Approach and its Application to Hydraulic Excavator

Model-based development (MBD), which utilizes system models to design complex products, has received increasing attention. However, an advanced control system design scheme is required to accurately control the developed products under harsh conditions for practical usage. This paper proposes a control system that integrates a data-driven compensator (DDC) with a model-based control (MBC) system design. The proposed method considers a hierarchical control structure comprising an upstream control system based on the MBC design approach and a downstream control system that includes a plant control loop with a DDC. The proposed system can always maintain the desired performance by driving the MBC based on an ideal model because the downstream control system, DDC, maintains the characteristics of the ideal model even if the plant properties change owing to changes in operational and/or environmental conditions. In this study, the design scheme for unifying models and data in the MBD process is called smart MBD (S-MBD), and the proposed control system design scheme is based on the S-MBD approach. The effectiveness of the proposed hierarchical control system is verified by applying it to a hydraulic excavator.

Product Design Cost Estimation for Make-to-Order Industry: A Machine Learning Approach

This research addresses the need for accurate design cost estimation in the Make-To-Order (MTO) industry. The complexity of product customization is key to differentiation. While many studies focus on manufacturing cost estimation, few explore design cost estimation. To improve the accuracy of design cost estimation, this research proposes a new cost driver based on design features available in Computer-Aided Design (CAD) data. The design feature is analyzed to the actual industry cost using machine learning methods, including Artificial Neural Networks (ANNs) and Support Vector Regression (SVR). The cost drivers identified as significant consisted of twenty-six 3D CAD features and four 2D CAD features. The results showed that the ANN models outperformed the SVR models in correctly estimating product design costs, as evidenced by the high R2values in the training and testing phases. The proposed method allows early identification of cost drivers, a significant advantage at the order initiation stage when detailed design features are often ambiguous. The novelty of this research is the use of 3D CAD technology for cost estimation, which quantifies costs based on product design complexity, providing valuable insights into the impact of design adjustments on costs early in the design process. Doi: 10.28991/ESJ-2024-08-03-022 Full Text: PDF

A top-down lashing bridge collaborative design method

ABSTRACT The short detailed design cycle of lashing bridges on container ships needs to ensure that various designs have different requirements at multiple locations on the ship, making the design methodology for complex products challenging. Currently, research on the design methodology of lashing bridges lacks in-depth investigation into detailed design aspects. This work addressed research gaps through a top-down collaborative design approach and analysed the principles of propagating design changes in computer-aided design (CAD), as well as proposed a design methodology consisting of four core steps, namely, skeleton decomposition, feature aggregation, detailed design and product structure (PS) expansion. The effectiveness of this methodology was validated through practical design examples and specific variant design instances of a certain type of container ship. The validation demonstrated that the proposed design methodology, in contrast to traditional three-dimensional design methods, supported design process reuse and significantly improved design efficiency in complex product designs.

Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO) for Early Design of Helicopter Primary Parameters

The design of a helicopter involves multiple disciplines and a large number of technical parameters with conflicting objectives, leading to the challenges of multidisciplinary coupling in design and optimization. In the context of the application of Model-Based Systems Engineering (MBSE) in complex product design, how to bridge the gap between MBSE and Multidisciplinary Design Analysis and Optimization (MDAO) to accelerate the early design process of helicopters remains an urgent problem to be addressed. The paper proposed an optimization framework that links MBSE and MDAO models for the design optimization problem of helicopters. On the MBSE side, the design optimization problem of helicopters was defined through the descriptive SysML model. On the MDAO side, the specific domain analysis model and optimization analysis workflow were established. The integration of design data was achieved through stereotype plugins and design components, bridging the gap between MBSE and MDAO, and providing accurate engineering data for system design verification. The paper provided an optimization case of a helicopter, with the optimal mission performance of the helicopter as the objective function. Through MDAO analysis, the design optimization of fuel consumption, take-off weight, and rotor dynamics parameters was completed, achieving an integrated process of system design and optimization.

Promoting knowledge recommendation in innovative engineering design: a BERT-GAT-based patent representation learning approach

Since innovation in complex product design hinges on thorough engineering knowledge application, high-quality patent recommendations foster innovation in engineering design. However, many patent knowledge recommendation studies perform patent analysis without comprehensive exploration and proper organisation of knowledge, causing a superficial understanding of patents and returning arbitrary results. To mitigate this issue, a deep learning-based approach for patent representation learning and knowledge recommendation is proposed. First, a four-dimensional patent knowledge model is defined to formalise the patent attributes that critically affect the engineering design outcomes, namely patents’ domain(D), function(F), technology(T) and citation(C). Second, to exploit patent knowledge from their content and citation relationships, a representation learning approach integrating Bidirectional Encoder Representations from Transformers(BERT) and Graph Attention Network(GAT) is introduced. Thereafter a patent knowledge space is established in which each patent is characterised by the function, technology, and citation embeddings. Third, a knowledge requirement space is also constructed by vectorising a designer’s search query via BERT model and linking it to a requirement-representing patent based on similarity. Finally, a recommender prototype is developed and showcased by the knowledge recommendation in sealing structure design tasks. Comparative experiments and application cases validate the effectiveness of our method in patent representation learning and knowledge recommendation.

A survey of LLM-augmented knowledge graph construction and application in complex product design

In the field of complex product design, deploying knowledge graphs (KGs) has become a promising trend due to its strength on exploiting and applying the large-scale, complex, and specialized domain knowledge. In recent years, large language models (LLMs) have also attracted much attention due to their outstanding performance in natural language understanding and generation. However, in the research of complex product design dominated by domain knowledge, few studies involve LLMS and KG at the same time. To fill this gap, we survey 42 articles published in the last four years, focusing on three key questions. The combination of LLM and KG in specific applications in complex product design is deeply discussed. The analysis reveals how these techniques facilitate data collection, design concept formation and design process optimization and proposes a technical framework combining LLM and KG for complex product design domain. In addition, we identify key challenges and propose directions for future research. As an explorative survey paper, this paper provides insightful ideas for implementing more specialized domain knowledge graph in complex product design field.

Analyzing properties of semi-molten powder granules in laser powder bed fusion

The research focused on the properties of partially fused powder particles and their possible effects on the mechanical and corrosive properties of products produced by Laser Powder Bed Fusion (LPBF). Scanning electron microscopy was used to investigate surfaces and pores, both for adherent particles and for particles completely enclosed in the molten bath. The study focused on Ti-6Al-4V, considering powder particles between 10–45 µm and a layer thickness of 25 µm. Different combinations of laser powers and scanning speeds were chosen to investigate the melting, mixing and solidification behavior. The analysis revealed that certain partially melted powder particles caused microcracks and increased the corrosive surface area, which showed distinct microstructures compared to the core area of the product. The investigation identified fully integrated particles and mostly melted particles by microstructure analysis. The results suggest potential innovative applications in complex product designs where the removal of these particles is a challenge, as well as in bone implantation to improve osseointegration.

Visual complexity in product design: How does the degree of elaborateness of the front‐pack image impact consumers' responses?

AbstractPrevious studies have documented the decisive impact of various product design elements on consumers' attitudes and decisions. However, research is missing on the impact of visual complexity. Using four studies that consider wine and perfume products, the present research manipulates visual complexity by focusing on the degree of elaborateness of front‐pack images. Using an affective priming task as an implicit technique, more positive automatically activated affective responses are shown for lower complexity designs (Study 1), in line with a processing‐fluency account. However, explicit measures show that higher complexity is associated with greater product evaluations (regarding esthetics, quality, prestige, trustworthiness, acceptable price, and purchase intention; Studies 2 and 3). In addition, more pronounced effects emerge for participants with higher sensitivity to design (Study 3). Finally, using an alternative implicit technique that does not involve very brief presentations of the products (implicit association test (IAT); Study 4), the association between higher complexity and product evaluation is also found under conditions of automaticity. Overall, this research provides insights into the diverging affective and cognitive effects of visual complexity. It suggests that the positive effect of lower complexity through higher processing fluency is restricted to responses that are affective and generated under conditions of automaticity that involve particularly low‐level processes. It also suggests that priming techniques based on very short presentations of the products' visuals may be unsuitable for examining responses to fairly complex concepts such as consumer products. On the whole, this research emphasizes that design complexity should receive more attention from consumer researchers and marketers.

Research on the manufactured Technology of High-precision Load Connecting Frame Structure with Special-shaped Variable Shaped Cross-section

Aiming at the research and development technology of high-precision load connecting frame structure with special-shaped variable cross-section reinforced by internal cross bars, the two-step weaving and stitching fiber preform forming process scheme and the general RTM product forming process scheme of load connecting frame are discussed by using the comparison method; The virtual simulation of RTM glue injection process is studied by using ESI Group software, and the results show that the completion time of the glue injection process with four sprues is about 29.4% of that of the glue injection process with only one sprue, which guides the design of RTM molding design; The key process of product manufacturing, such as the glue injection and curing process of the product and the quality control process during the technological process, are analyzed, and the performance of the final product is evaluated. Through the research on the manufacturing process, we have overcomed the major constraints on several molding technologies, such as the two-step fiber preform molding technology of weaving and stitching, the RTM molding design and processing technology of complex and special-shaped composite products, the split molding technology, and combination finishing processing technology after secondary bonding. The results prove that the formed products have better forming quality, dimensional accuracy and thermal cycle dimensional stability. The nondestructive testing results of composite parts show that the internal structure of the product is good. The fiber volume fraction is controlled at (55 ± 3)%, the flatness of the important surface of the product is 0.05 mm and 0.03mm respectively, and the dimensional accuracy of the important interface is controlled within ± 0.02 mm. The dimensional stability after the thermal cycle test is good, and all indicators of the product meet the user’s requirements, which achieved expected goals. The success on the manufacturing of these products provides a technical basis for the design and manufacture of high-precision and high stiffness composite structures for deep space exploration and manned spaceflight.

A Decision-Making Method for Design Schemes Based on Intuitionistic Fuzzy Sets and Prospect Theory

Conceptual design is a key link in the process of complex product design, and it is very important to select the appropriate design scheme; however, there are many types and inaccuracies of the evaluation data, and there is a problem of mutual influence between the evaluation criteria, which leads to unreliable decision making of the optimal solution. In order to solve this problem, a decision-making method based on intuitionistic fuzzy sets (IFS) and prospect theory is proposed. This method can be used for symmetric and asymmetric evaluation data. The evaluation data are classified according to different expression types and unified using intuitionistic fuzzy numbers. The intuitionistic fuzzy prospect value of decision information is calculated using prospect theory, and the prospect transformation of decision information is completed. At the same time, the Gray Relational Analysis (GRA) method and the Criteria Importance Though Intercriteria Correlation (CRITIC) method are used to calculate the subjective and objective weights of the technical and economic evaluation indexes of the product, and the combination weights are given; then, based on the evidence theory, the basic probability distribution of the evidence chain of all conceptual design schemes is synthesized, and the comprehensive prospect evaluation results of the schemes are obtained to complete the optimization of the conceptual design schemes. Finally, the effectiveness of the proposed method is verified by the conceptual design of the chip removal system of the deep hole machining machine tool. This work provides a promising method for decision makers to optimize the design scheme and provides insights into multi-objective decision-making problems.

Innovation design oriented functional knowledge integration framework based on reinforcement learning

According to the basic law of Design Science, new product design is based on existing design knowledge. Knowledge integration can be applied to product function design to shorten design time and improve the design quality through effective use of the existing knowledge. With the increase of the product design complexity and the number of design knowledge, it is harder and harder for traditional traversal-based algorithms to complete knowledge integration under acceptable time cost. A Reinforcement Learning (RL) based functional knowledge integration framework is proposed. The functional knowledge is represented by its input and output, and organized using a knowledge graph. The Q-network is constructed and trained for the deep Monte Carlo method-based functional unit chain generation algorithm. The performance experiments show that comparing with the traditional searching algorithms, the RL based algorithm can provide same quality design scheme with much shorter time. The proposed algorithm is promising to realize real-time functional knowledge integration in large-scale knowledge bases.

An Orchestration Method for Integrated Multi-Disciplinary Simulation in Digital Twin Applications

In recent years, the methodology of Model-Based System Engineering (MBSE) has become relevant to the design of complex products, especially when safety critical systems need to be addressed. It allows, in fact, the deployment of product development directly through some digital models, allowing an effective traceability of requirements, being allocated upon the system functions, components, and parts. This approach enhances the designer capabilities in controlling the product development, manufacturing and after-market services. However, the application of such a methodology requires overcoming several technological barriers, especially in terms of models integration. The interoperability and management of several models—developed within different software to cover multiple levels of detail across several technical disciplines—is still very difficult, despite the level of maturation achieved by Systems Engineering. This paper describes a possible approach to provide such a connection between tools to allow a complete multi-disciplinary and heterogeneous simulation to analyse complex systems, such as safety-critical ones, which are typical of aerospace applications. Such an application is within a defined industrial context, placing particular attention on the compatibility of the approach with the legacy processes and tools.

Reliability Topology Optimization of Collaborative Design for Complex Products Under Uncertainties Based on the TLBO Algorithm

The topology optimization design of complex products can significantly improve material and power savings, and reduce inertial forces and mechanical vibrations effectively. In this study, a large-tonnage hydraulic press was chosen as a typically complex product to present the optimization method. We propose a new reliability topology optimization method based on the reliability-and-optimization decoupled model and teaching-learning-based optimization (TLBO) algorithm. The supports formed by the plate structure are considered as topology optimization objects, characterized by light weight and stability. The reliability optimization under certain uncertainties and structural topology optimization are processed collaboratively. First, the uncertain parameters in the optimization problem are modified into deterministic parameters using the finite difference method. Then, the complex nesting of the uncertainty reliability analysis and topology optimization are decoupled. Finally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parameters and a fast solution. The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial stage and performing finer searches in the later stages. A numerical example of the hydraulic press base plate structure is presented to underline the effectiveness of the proposed method.

A novel collaborative decision-making method based on generalized abductive learning for resolving design conflicts

In complex product design, lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts. However, since complex products involve intensive multi-domain knowledge, preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain. In recent years, enormous challenges are involved in the design process, within the increasing complexity of preference. This article mainly proposes a novel decision-making method based on generalized abductive learning (G-ABL) to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively. The proposed G-ABL framework, containing three cores: classifier, abductive kernel, and abductive machine, supports preference integration from data and fuzzy knowledge. In particular, a subtle improvement is presented for WK-means based on the entropy weight method (EWM) to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant. Furthermore, fuzzy comprehensive evaluation (FCE) and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels. Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set. Finally, an engineering application is provided to verify the effectiveness of the proposed method, and the superiority of which is illustrated by comparative analysis.

Design-Manufacturing-Operation & Maintenance (O&M) Integration of Complex Product Based on Digital Twin

This paper presents a complex product design-manufacturing-operations and maintenance integration method based on digital twin technology. This method aims to solve the problem of information silos in the design, manufacturing and operation and maintenance phases of complex products in the context of intelligent manufacturing and to integrate the design, manufacturing and operation and maintenance processes of complex products. To address the integration needs of complex product design, manufacturing, operation and maintenance business integration, a framework for complex product design-manufacturing-operation and maintenance integration based on the digital twin is first proposed, in addition to designing a model and operation mechanism for combining the virtual and real of the digital twin model. Then, the implementation of multistage collaborative design technology, data intelligent sensing technology, and data integration and fusion technology for the digital twin-based design-manufacturing-operations and maintenance integration processes are analyzed and discussed. Finally, a case study involving the fault prediction of key components of the bogie of an EMU demonstrated the integrated mode of operation in the design-manufacture-operation and maintenance process of the EMU. It verified the effectiveness of the proposed framework, process and methodology.

Customized Product Configuration Rule Intelligent Extraction and Dynamic Updating Method Based on the Least Recently Used Dynamic Decision Tree

Abstract In the traditional customized product (CP) configuration design system, the configuration rules in the database usually rely on the manual input and maintenance of experienced designers. In complex product design, configuration rules are numerous, complex, and difficult to understand. The way of human input based on experience consumes plenty of resources, which are strictly limited by experienced designers. This overreliance on those experienced designers has seriously restricted the development of enterprises. To address this problem, a least recently used dynamic decision tree (LRU-DDT) algorithm for CP configuration rule intelligent extraction and dynamic updating is put forward in this article. Based on the decision table majority (DTM) classifier, the condition attributes are first reduced. An improved J48 decision tree algorithm is proposed to extract the CP configuration rules. In addition, the CP configuration rules can be dynamically updated based on LRU-DDT. To verify the proposed method, the GB10 high-speed elevator configuration design process is taken as an example. The configuration rules are extracted and updated by the proposed algorithms. The results show that the configuration solution efficiency is improved by 16%.

Overview and Prospects of Uncertain Intelligent Design for Complex Products

Overview and Prospects of Uncertain Intelligent Design for Complex Products

Drawn to the gloss: Examining the effect of product reflection on product aesthetics

AbstractWhile advertising products with reflections is a common industry practice, this research is the first to investigate their impact on product aesthetics. Reflections are endowed with an inherent shine since they are naturally formed by the interaction of light with an object and a reflecting surface. We draw upon this property along with Evolutionary Psychology (EP) research documenting an intrinsic human attraction towards gloss to predict their positive effect on product aesthetics. Further in line with the EP principles, we propose that this effect is mediated by enhanced pristineness perceptions about the product in the presence of its reflection in the ad. Finally, we draw upon the Elaboration Likelihood Model (ELM) to identify product design complexity as a boundary condition for the positive effect of reflection on product attractiveness perceptions. For a product with relatively high (vs. low) design complexity, with design being the focal aspect that is elaborated via a central route, we predict the effect of a peripheral stylistic cue like product reflection to attenuate. Three experimentally designed studies employing different product types offer empirical support to these predictions. Our work extends prior marketing research studying the effects of executional elements in advertising on consumers' product reactions, particularly the aesthetic impact of product reflections as creative stylistic cues. The managerial implications of this work span both traditional and digital marketing channels. Incorporating a product reflection in advertising can work as an easy‐to‐implement option that can help practitioners elevate product pristineness and aesthetics. On the other hand, avoiding their use when advertising products with relatively complex designs can save both ad space and money.

A feature-based assembly information modeling method for complex products’ 3D assembly design

During the assembly design of complex products, the process data management is disordered and the data expression is not standardized, and depends mainly on the experience of technical personnel resulting in a lack of unified data source, low design efficiency, and low automation. This paper proposes a feature-based assembly information model to address these problem, providing standardized original design data for complex product 3D-assembly design. Taking the geometric feature of the mating faces as the study object, the overall structure of complex products and the information requirements of each assembly design stage were analyzed to construct the assembly feature information model. By focusing on the composition and expression form of information in the assembly feature information model, a geometric feature classification criterion applicable to assembly design is presented, and the matching rules of feature pairs, assembly relationship, and assembly constraint instructions are established. Based on these, assembly design information is extracted and expressed in a standardized form. As a validation, the modeling method was conducted on a marine diesel engine cylinder cap as a case study; the performance and applicability of the established rules and the extraction and expression methods were verified through the comparison with the assembly process of the cylinder cap.