Low-carbon process planning is the basis for the implementation of low-carbon manufacturing technology. And it is of profound significance to improve process executability, reduce environmental pollution, decrease manufacturing cost, and improve product quality. In this paper, based on the perceptual data of parts machining process, considering the diversity of process planning schemes and factors affecting the green manufacturing, a multi-level evaluation criteria system is established from the aspects of processing time, manufacturing cost and processing quality, resource utilization, and environmental protection. An integrated evaluation method of low-carbon process planning schemes based on digital twins is constructed. Each index value is normalized by the polarized data processing method, its membership is determined by the fuzzy statistical method, and the combination weight of each index is determined by the hierarchical entropy weight method to realize the organic combination of theoretical analysis, practical experience, evaluation index, and process factors. The comprehensive evaluation of multi-process planning schemes is realized according to the improved fuzzy operation rules, and the best process planning solution is finally determined. Finally, taking the low-carbon process planning of an automobile part as an example, the feasibility and effectiveness of this method are verified by the evaluation of three alternative process planning schemes. The results show that the method adopted in this paper is more in line with the actual production and can provide enterprises with the optimal processing scheme with economic and environmental benefits, which may be helpful for more data-driven manufacturing process optimization in the future.

Inspirational stimuli are known to be effective in supporting ideation during early-stage design. However, prior work has predominantly constrained designers to using text-only queries when searching for stimuli, which is not consistent with real-world design behavior where fluidity across modalities (e.g., visual, semantic, etc.) is standard practice. In the current work, we introduce a multi-modal search platform that retrieves inspirational stimuli in the form of 3D-model parts using text, appearance, and function-based search inputs. Computational methods leveraging a deep-learning approach are presented for designing and supporting this platform, which relies on deep-neural networks trained on a large dataset of 3D-model parts. This work further presents the results of a cognitive study (n = 21) where the aforementioned search platform was used to find parts to inspire solutions to a design challenge. Participants engaged with three different search modalities: by keywords, 3D parts, and user-assembled 3D parts in their workspace. When searching by parts that are selected or in their workspace, participants had additional control over the similarity of appearance and function of results relative to the input. The results of this study demonstrate that the modality used impacts search behavior, such as in search frequency, how retrieved search results are engaged with, and how broadly the search space is covered. Specific results link interactions with the interface to search strategies participants may have used during the task. Findings suggest that when searching for inspirational stimuli, desired results can be achieved both by direct search inputs (e.g., by keyword) as well as by more randomly discovered examples, where a specific goal was not defined. Both search processes are found to be important to enable when designing search platforms for inspirational stimuli retrieval.

This paper introduces a method to improve the design procedure of axiomatic design theory (AD) with Extenics. A comprehensive review of the AD indicates that the powerful principle of AD has been widely studied and applied to many areas, however, inexperienced practitioners of the AD theory still find it difficult to follow or apply the principles in their design which inadvertently often leads to misunderstanding and skepticism. The lack of definitive descriptions for all the elements and specific approaches to guiding the mapping process restricts the development and application of AD theory. This paper improves the design procedure of AD with Extenics. The elements in AD domain are expressed by basic-elements of Extenics, and the formulations are generated. The mapping process based on AD and Extenics is developed. The improved design procedure provides designers with a theoretical foundation based on the logical and rational thought process, meanwhile the solution space can be expanded and innovative designs are inspired. Based on the proposed design procedure, a computer-aided system is developed, which makes the complex and fuzzy design activity clear and easy to follow by filling in the blanks in a step-by-step manner. An example of a novel corn harvester header design scheme is considered to illustrate the validity of the improved design procedure.

The use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method builds on natural language processing to determine change candidates from textual data and uses dependency modeling to reveal direct and indirect change propagation paths arising from the change candidates. The method was applied in a case example where 3665 YouTube comments on a diesel engine were analyzed. Based on the results, two engine components were recommended for design change with six others predicted as likely to be affected through change propagation. The findings suggest that the method can be used to aid decision quality in product planning through a better understanding of the change impact associated with the opportunities identified.

Access to vast datasets of visual and textual materials has become significantly easier. How to take advantage of the conveniently available data to support creative design activities remains a challenge. In the phase of idea generation, the visual analogy is considered an effective strategy to stimulate designers to create innovative ideas. Designers can read useful information off vague and incomplete conceptual visual representations, or stimuli, to reach potential visual analogies. In this paper, a computational framework is proposed to search and retrieve visual stimulation cues, which is expected to have the potential to help designers generate more creative ideas by avoiding visual fixation. The research problems include identifying and detecting visual similarities between visual representations from various categories and quantitatifying the visual similarity measures serving as a distance metric for visual stimuli search and retrieval. A deep neural network model is developed to learn a latent space that can discover visual relationships between multiple categories of sketches. In addition, a top cluster detection-based method is proposed to quantify visual similarity based on the overlapped magnitude in the latent space and then effectively rank categories. The QuickDraw sketch dataset is applied as a backend for evaluating the functionality of our proposed framework. Beyond visual stimuli retrieval, this research opens up new opportunities for utilizing extensively available visual data as creative materials to benefit design-by-analogy.

This paper outlines a procedure for assessing the quality of failure explanations in engineering failure analysis. The procedure structures the information contained in explanations such that it enables to find weak points, to compare competing explanations, and to provide redesign recommendations. These features make the procedure a good asset for critical reflection on some areas of the engineering practice of failure analysis and redesign. The procedure structures relevant information contained in an explanation by means of structural equations so as to make the relations between key elements more salient. Once structured, the information is examined on its potential to track counterfactual dependencies by offering answers to relevant what-if-things-had-been-different questions. This criterion for explanatory goodness derives from the philosophy of science literature on scientific explanation. The procedure is illustrated by applying it to two case studies, one on Failure Analysis in Mechanical Engineering (a broken vehicle shaft) and one on Failure Analysis in Civil Engineering (a collapse in a convention center). The procedure offers failure analysts a practical tool for critical reflection on some areas of their practice while offering a deeper understanding of the workings of failure analysis (framing it as an explanatory practice). It, therefore, allows to improve certain aspects of the explanatory practices of failure analysis and redesign, but it also offers a theoretical perspective that can clarify important features of these practices. Given the programmatic nature of the procedure and its object (assessing and refining explanations), it extends work on the domain of computational argumentation.

Surface roughness (SR) is one of the major parameters used to govern the quality of the fused deposition modeling (FDM)-printed products, and the FDM process parameters can be easily regulated in order to obtain a good surface finish. The surface quality of the product produced by the FDM is generally affected by the staircase effect that needs to be managed. Also, the production time (PT) to fabricate the product and volume percentage error (VPE) should be minimized to make the FDM process more efficient. The aim of this paper is to accomplish these three objectives with the use of the parametric optimization technique integrating the artificial neural network (ANN) and the whale optimization algorithm (WOA). The FDM parameters which have been taken into consideration are layer thickness, nozzle temperature, printing speed, and raster width. Experimentation has been conducted on printed samples to examine the impact of the input parameters on SR, VPE, and PT according to Taguchi's L27 orthogonal array. The ANN model has been built up using the experimental data, which was further used as an objective function in the WOA with an aim to minimize output responses. The robustness of the proposed method has been validated on the optimal combinations of FDM process parameters.

In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R2 = 0.9893 and R2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

The goal of this paper is to develop and test a gamified design thinking framework, including its pedagogical elements, for supporting various learning objectives for school students. By synthesizing the elements and principles of design, learning and games, the authors propose a framework for a learning tool for school students to fulfil a number of learning objectives; the framework includes a design thinking process called “IISC Design Thinking” and its gamified version called “IISC DBox”. The effectiveness of the framework as a learning tool has been evaluated by conducting workshops that involved 77 school students. The results suggest that the gamification used had a positive effect on the design outcomes, fulfilment of learning objectives, and learners' achievements, indicating the potential of the framework for offering an effective, gamified tool for promoting design thinking in school education. In addition to presenting results from empirical studies for fulfilment of the objectives, this paper also proposes an approach that can be used for identifying appropriate learning objectives, selecting appropriate game elements to fulfil these objectives, and integrating appropriate game elements with design and learning elements. The paper also proposes a general approach for assessing the effectiveness of a gamified version for attaining a given set of learning objectives. The methodology used in this paper thus can be used as a reference for developing and evaluating a gamified version of design thinking course suitable not only for school education but also for other domains (e.g., engineering, management) with minimal changes.

In this work, an artificial neural network model is established to understand the relationship among the tensile properties of as-printed Ti6Al4V parts, annealing parameters, and the tensile properties of annealed Ti6Al4V parts. The database was established by collecting published reports on the annealing treatment of selective laser melting (SLM) Ti6Al4V, from 2006 to 2020. Using the established model, it is possible to prescribe annealing parameters and predict properties after annealing for SLM Ti-6Al-4V parts with high confidence. The model shows high accuracy in the prediction of yield strength (YS) and ultimate tensile strength (UTS). It is found that the YS and UTS are sensitive to the annealing parameters, including temperature and holding time. The YS and UTS are also sensitive to initial YS and UTS of as-printed parts. The model suggests that an annealing process of the holding time of fewer than 4 h and the holding temperature lower than 850°C is desirable for as-printed Ti6Al4V parts to reach the YS required by the ASTM standard. By studying the collected data of microstructure and tensile properties of annealed Ti6Al4V, a new Hall-Petch relationship is proposed to correlate grain size and YS for annealed SLM Ti6Al4V parts in this work. The prediction of strain to failure shows lower accuracy compared with the predictions of YS and UTS due to the large scattering of the experimental data collected from the published reports.

Redesign is a widespread strategy for product improvement whose essence is the optimization of design parameters (DPs) considering the trade-off between customer satisfaction and cost concerns. Similar to the relation between customer requirements (CRs) and customer satisfaction, the sensitivity of customer satisfaction is diverse to different DPs. In this study, a sensitivity-enhanced customer satisfaction function is defined for redesign model construction. This fills the research gap in product redesign that lacking of consideration and quantification of customer satisfaction sensitivity. First, a sensitivity index is defined based on Kano indices for analyzing sensitivity of customer satisfaction in different DP categories. Second, traditional customer satisfaction function has been improved by injecting the sensitivity of customer satisfaction to the variations of DPs. Subsequently, a DP optimization model is established to maximize shared surplus between customers and enterprise. Finally, a case study involving the redesign of a braking system of automobile is implemented to demonstrate the effectiveness and rationality of the proposed approach. The results show that the improved customer satisfaction function can reflect a more nuanced relationship between customer satisfaction and fulfilment level of DPs. Additionally, the proposed redesign model helps designers determine the target values of DPs under a better trade-off and enhances enterprise competitiveness.

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

In the era of rapid product update and intense competition, aesthetic design has been increasingly important in various fields, as aesthetic feelings of customers largely influence their purchase preferences. However, the quantification of aesthetic feeling is still a very subjective process due to vague evaluations. The determination of form parameters according to aesthetics is difficult hitherto. Aesthetic measure recently arises as a prominent tool for this purpose using formulas derived from aesthetic theory. But as revealed by existing studies, it needs to be customized with deterministic and objective methods to be reliable in practice use. To facilitate this application, this paper proposes an evolutionary form design method, integrating aesthetic dimension selection and parameter optimization. After summarizing initial aesthetic dimensions, aesthetic dimension selection based on expert decision-making and particle swarm optimization (PSO) is carried out. With filtered aesthetic dimensions, design parameters are optimized with NSGA-II (non-dominated sorting genetic algorithm). The quality of pareto solutions obtained to be design schemes is assessed by three criteria to conduct sensitivity analysis of cross and mutation probability and population size. Our experiment using bicycle form design shows that the proposed evolutionary form design method can generate numerous and variant aesthetic design schemes rapidly. This is very useful for both product redesign and innovative new product development.

Although an accurate reliability assessment is essential to build a resilient infrastructure, it usually requires time-consuming computation. To reduce the computational burden, machine learning-based surrogate models have been used extensively to predict the probability of failure for structural designs. Nevertheless, the surrogate model still needs to compute and assess a certain number of training samples to achieve sufficient prediction accuracy. This paper proposes a new surrogate method for reliability analysis called Adaptive Hyperball Kriging Reliability Analysis (AHKRA). The AHKRA method revolves around using a hyperball-based sampling region. The radius of the hyperball represents the precision of reliability analysis. It is iteratively adjusted based on the number of samples required to evaluate the probability of failure with a target coefficient of variation. AHKRA adopts samples in a hyperball instead of an n-sigma rule-based sampling region to avoid the curse of dimensionality. The application of AHKRA in ten mathematical and two practical cases verifies its accuracy, efficiency, and robustness as it outperforms previous Kriging-based methods.

The use of topology optimization in the design of fluid dynamics systems is still in its infancy. With the decreasing cost of additive manufacture, the application of topology optimization in the design of structural components has begun to increase. This paper provides a method for using topology optimization to reduce the power dissipation of fluid dynamics systems, with the novelty of it being the first application of stochastic mechanisms in the design of 3D fluid–solid geometrical interfaces. The optimization algorithm uses the continuous adjoint method for sensitivity analysis and is optimized against an objective function for fluid power dissipation. The paper details the methodology behind a vanilla gradient descent approach before introducing stochastic behavior through a minibatch-based system. Both algorithms are then applied to a novel case study for an internal combustion engine's piston cooling gallery before the performance of each algorithm's resulting geometry is analyzed and compared. The vanilla gradient descent algorithm achieves an 8.9% improvement in pressure loss through the case study, and this is surpassed by the stochastic descent algorithm which achieved a 9.9% improvement, however this improvement came with a large time cost. Both approaches produced similarly unintuitive geometry solutions to successfully improve the performance of the cooling gallery.