Research in Engineering Design最新文献

Current generative design tools backed by artificial intelligence (AI) primarily allow for quantitative inputs while qualitative aspects of a design, in particular aesthetics, have been shown to be considered indirectly by designers. To explore this further, controlled lab experiments were conducted to understand how designers incorporate quantitative and qualitative objectives while using generative design tools and how their behavior may affect design performance. Thirty-four participants completed a design task with quantitative and qualitative objectives with and without generative design tools. The outcomes produced using generative design tools displayed a greater aesthetic diversity and expanded a larger portion of the objective space compared to those without using a generative design tool. Participants also expressed the ability to focus on the qualitative objectives by delegating the quantitative objective to the generative design tool. This showcases the potential for high-performing generative design tools to assist human designers by alleviating part of their cognitive load when balancing multiple objectives, giving them the bandwidth to focus on other objectives not fully incorporated by the tool. In this way, leveraging the expertise of both the human designer and the generative design tool can allow for greater consideration of various objectives throughout the design process.

Past research theorises that eye-closure and associated mental visualisation, which occurs during the design process, may be important for supporting creativity. Despite these claims, little empirical evidence is available about the results of mental recall in design. In response to this knowledge gap, this research reports the results of a protocol analysis of 35 designers’ creative processes, to examine the impacts of non-perceptual cognition (NPC) on design process and ideation. The results suggest that NCP events facilitate idea generation in the individual design process, confirming that ‘seeing with the mind’s eyes’ is an important process in designing. The research concludes with a discussion about the implications of the findings and its contribution to research into design and creativity.

The primary module of Quality Function Deployment (QFD) is the House of Quality (HoQ), which supports the design of new products and services by translating customer requirements (CRs) into engineering characteristics (ECs). Within the HoQ framework, the traditional technique for prioritizing ECs is the independent scoring method (ISM), which aggregates the weights of the CRs and the relationships between CRs and ECs (i.e., null, weak, medium, and high) through a weighted sum. However, ISM incorporates two questionable operations: (i) an arbitrary numerical conversion of the relationships between CRs and ECs, and (ii) the “promotion” of these relationships from ordinal to cardinal scale. To address these conceptual shortcomings, this paper introduces a novel procedure for prioritizing ECs, inspired by the Thurstone’s Law of Comparative Judgment (LCJ). This procedure offers a solution that is conceptually sound and practical, overcoming the conceptual shortcomings of ISM, while maintaining its simplicity, flexibility, and ease of implementation. The proposed approach is supported by a realistic application example illustrating its potential.

Raising the user’s self-confidence is a promising strategy to reduce product-related user stigma. In the context of product usage, the commonly used term self-confidence refers to the psychological construct of self-efficacy. To strengthen a user’s self-efficacy through product design, providing both good usability and emotionality in a product seems to be a reasonable starting point. However, their suitability and validity for this purpose has not yet been sufficiently assessed. This paper examines whether self-efficacy would be associated with perceptions of a product’s usability and emotionality. By conducting an online survey (n = 105; stigma-sensitive product demonstrator: walker), it was confirmed that the perception of good usability and emotionality of walkers were positively associated with the user’s perceived self-efficacy. Moreover, a negative interaction effect was identified showing that the association between emotionality and self-efficacy increased with lower levels of perceived usability and vice versa. This may indicate that emotions can compensate the importance of usability at least to some extent.

This paper aims to explore metrics for evaluating the effectiveness of functional decomposition methods regarding problem space exploration at the early design stage. Functional decomposition involves breaking down the main purpose of a complex problem or system into a set of more manageable sub-functions, leading to a clearer understanding of the problem space and its various aspects. While various metrics have been used to evaluate functional decomposition outcomes, little literature has focused on assessing its effectiveness in problem space exploration. To address the gap, this research introduces three metrics for problem space evaluation defined by functional models: quantity of unique functions (M1), breadth and depth of the hierarchical structure (M2), and relative semantic coverage ratio of the problem space (M3). An example study is conducted to illustrate the evaluation process, comparing functional analysis with and without explicit human-centric considerations using a power screwdriver as a case product. The analysis in the example study reveals that the breadth of the hierarchical structure (part of M2) is marginally larger in the condition with explicit human-centric considerations (Condition A) compared to the condition without such considerations (Condition B). However, no significant differences are observed in terms of other metrics. The qualitative analysis based on semantic comparisons suggests that Condition A facilitates participants in generating a diverse set of functions supporting user safety requirements more effectively than Condition B. Overall, the example study demonstrates the evaluation process for each metric and discusses their nuances and limitations. By proposing these metrics, this research contributes to benchmarking and evaluating the effectiveness of different methods in promoting functional analysis in engineering design. The metrics provide valuable insights into problem space exploration, offering designers a better understanding of the efficacy of their functional decomposition methods in early design stages. This, in turn, fosters more informed decision-making and contributes to the advancement of functional analysis methodologies in engineering design practices.

During an engineering design process, designers create sequences of product models by switching between product models with different purposes. To enable an efficient use of these product models, researchers are required to anticipate the compatibility of the models in application by designers. This is necessary as the information in an existing product model may not be usable for a subsequent product model. However, the corresponding information on compatibility was not accessible to researchers as it was scattered across various publications. Hence, the objective of this contribution was to develop a structured overview, a landscape of linkable and non-linkable product models in embodiment design to merge the information. For this purpose, a literature review containing a grounded theory-based analysis was conducted and the results were visualized using the network visualization software Gephi. The key learnings of the visualized landscape of 52 product models can be summarized as follows: (1) some models are already closely linked to each other by compatible inputs and outputs; (2) other product models are noticeable with mostly unknown linking possibilities due to incompatible inputs and outputs or insufficient descriptions in the literature. 14 product models offer two linking possibilities or less. In these cases, it is unclear how they interact with other product models in an engineering design process. In conclusion, the product model landscape provides insight into the compatibility of product models to support the development of existing and new product models for sequential use by designers.

The rationality of product module partition is crucial to the success of modular design. The correlations between components of complex products are complex, increasing the difficulty of module partition. Thus, many existing methods of module partition have difficulty realizing this process effectively for complex products with a large number of components. This paper proposes a module partition method for complex products based on stable overlapping community detection and overlapping component allocation. The correlations between components are analyzed to obtain a comprehensive correlation strength matrix. The undirected weighted network is used to represent components and the correlations between them. A stable overlapping community detection algorithm based on the improved judgement of within-community Shapley values is proposed to generate multiple preliminary schemes of module partition. Overlapping components among modules are allocated to the most suitable modules by adopting a genetic algorithm (GA). The scheme with the largest modularity measure Q is selected as the final scheme of module partition. The proposed method is applied to a computer numerical control (CNC) grinding machine. The proposed module partition method for complex products is demonstrated to be superior to other effective methods.

In this paper, we address the issue of whether to optimize or satisfice in model-based engineering design. When dealing with operations research problems in the context of engineering design, one may encounter (i) nonlinear, nonconvex objectives and constraints, (ii) objectives with different units, and (iii) computational models that are abstractions of reality and fidelity, Seeking a single-point optimal solution that meets the necessary and sufficient Karush–Kuhn–Tucker (KKT) conditions makes it impossible to obtain a solution that satisfies all the targeted goals. Instead, a method to identify satisficing solutions that satisfies necessary KKT condition but not the sufficient condition is proposed. These solutions are relatively robust, easy to acquire, and often good enough. In this paper, we demonstrate the combined use of the compromise Decision Support Problems and the adaptive linear programming algorithm, as proposed by Mistree and co-authors. This method is appropriate in formulating design problems and obtaining solutions that satisfy only the necessary KKT condition. Further, the use of the proposed method circumvents complications associated with the use of gradient-based optimization algorithms typically used to solve optimization problems. We discuss the efficacy of our proposed method using four test problems to illustrate how the satisficing strategy outperforms the optimizing strategy in model-based engineering design.

Changes and modifications to existing products, known as engineering changes (EC), are common in complex product development. They require appropriate implementation planning and supervision to mitigate the economic downsides due to complexity. These tasks, however, take a high administrative toll on the organization. In response, automation by computer tools has been suggested. Due to the underlying process complexity, the application of artificial intelligence (AI) is advised. To support research and development of new AI-artifacts for EC management (ECM), a knowledge base is required. Thus, this paper aims to gather insights from existing approaches and discover literature gaps by conducting a systematic literature review. 39 publications applying AI methods and algorithms in ECM were identified and subsequently discussed. The analysis shows that the methods vary and are mostly utilized for predicting change propagation and knowledge retrieval. The review’s results suggest that AI in EC requires developing distributed AI systems to manage the ensuing complexity. Additionally, five concrete suggestions are presented as future research needs: Research on metaheuristics for optimizing EC schedules, testing of stacked machine learning methods for process outcome prediction, establishment of process supervision, development of the mentioned distributed AI systems for automation, and validation with industry partners.

Increasing product diversity, rising performance and reliability demands, and industry competitiveness are some of the many reasons that increase the need of more complex product designs in almost all sectors. The complexity of parts increases with their geometrical features to be designed and manufactured. Researchers agreed that it can be qualitatively evaluated and expressed with terms like low, medium, high, and very high. However, it might be evaluated differently, depending on the designer’s considerations, domain and experience. Quantitative evaluation of a design complexity is, therefore, indispensable and expedites the decision-making about the selection of the manufacturing process. However, having a well-defined and unambiguous metric for quantitative evaluation is challenging. Most of existing metrics are not objective and are only valid for their specific applications. This paper presents a novel, unambiguous, and generalized approach for shape complexity evaluation. The developed metric enables determining if the selected part should be produced by conventional methods such as machining, or by non-conventional methods such as additive manufacturing. In order to ensure its objectivity, only geometrical features have been considered. The metric was tested through 25 different part designs of varying complexity. The investigations showed an accordance between the qualitatively evaluated shape and the calculated complexity factor. Also, the comparison of the results with other metrics showed the weakness of the latter and the efficiency and reliability of our metric. The results have been also validated by 50 experts from 23 countries. Based on these results, a threshold between machining and additive manufacturing is fixed allowing an easier decision-making.

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