Pub Date : 2022-09-19DOI: 10.1017/S0890060422000087
Vahid Pourmostaghimi, M. Zadshakoyan, S. Khalilpourazary, M. Badamchizadeh
Abstract 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.
{"title":"A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation","authors":"Vahid Pourmostaghimi, M. Zadshakoyan, S. Khalilpourazary, M. Badamchizadeh","doi":"10.1017/S0890060422000087","DOIUrl":"https://doi.org/10.1017/S0890060422000087","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45637672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-08DOI: 10.1017/S0890060422000099
Kristian González Barman
Abstract 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.
{"title":"Procedure for assessing the quality of explanations in failure analysis","authors":"Kristian González Barman","doi":"10.1017/S0890060422000099","DOIUrl":"https://doi.org/10.1017/S0890060422000099","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45236482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-08DOI: 10.1017/S0890060422000142
Praveen Kumar, Pardeep Gupta, I. Singh
Abstract 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.
{"title":"Parametric optimization of FDM using the ANN-based whale optimization algorithm","authors":"Praveen Kumar, Pardeep Gupta, I. Singh","doi":"10.1017/S0890060422000142","DOIUrl":"https://doi.org/10.1017/S0890060422000142","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42681778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-03DOI: 10.1017/S0890060422000075
Wenjuan Li, C. Suh, Xiangyang Xu, Zhenghe Song
Abstract 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.
{"title":"Extenics enhanced axiomatic design procedure for AI applications","authors":"Wenjuan Li, C. Suh, Xiangyang Xu, Zhenghe Song","doi":"10.1017/S0890060422000075","DOIUrl":"https://doi.org/10.1017/S0890060422000075","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48448578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-02DOI: 10.1017/S0890060422000063
Zijian Zhang, Yan Jin
Abstract 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.
{"title":"Data-enabled sketch search and retrieval for visual design stimuli generation","authors":"Zijian Zhang, Yan Jin","doi":"10.1017/S0890060422000063","DOIUrl":"https://doi.org/10.1017/S0890060422000063","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49247275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.1017/S0890060422000129
E. C. Koh
Abstract 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.
{"title":"Design change prediction based on social media sentiment analysis","authors":"E. C. Koh","doi":"10.1017/S0890060422000129","DOIUrl":"https://doi.org/10.1017/S0890060422000129","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"36 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41407209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.1017/S0890060422000130
E. Kwon, Forrest Huang, K. Goucher-Lambert
Abstract 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.
{"title":"Enabling multi-modal search for inspirational design stimuli using deep learning","authors":"E. Kwon, Forrest Huang, K. Goucher-Lambert","doi":"10.1017/S0890060422000130","DOIUrl":"https://doi.org/10.1017/S0890060422000130","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48463298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-25DOI: 10.1017/S0890060422000105
Zhaoming Chen, Jinsong Zou, Wei Wang
Abstract 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.
{"title":"Towards comprehensive digital evaluation of low-carbon machining process planning","authors":"Zhaoming Chen, Jinsong Zou, Wei Wang","doi":"10.1017/S0890060422000105","DOIUrl":"https://doi.org/10.1017/S0890060422000105","url":null,"abstract":"Abstract 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.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41724051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1017/S0890060422000051
Masih Hanifi, H. Chibane, R. Houssin, D. Cavallucci, Naser Ghannad
Abstract Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.
{"title":"Artificial intelligence methods for improving the inventive design process, application in lattice structure case study","authors":"Masih Hanifi, H. Chibane, R. Houssin, D. Cavallucci, Naser Ghannad","doi":"10.1017/S0890060422000051","DOIUrl":"https://doi.org/10.1017/S0890060422000051","url":null,"abstract":"Abstract Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45691392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-19DOI: 10.1017/S0890060421000433
Renee Noortman, P. Lovei, M. Funk, E. Deckers, S. Wensveen, Berry Eggen
Abstract Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.
{"title":"Breaking up data-enabled design: expanding and scaling up for the clinical context","authors":"Renee Noortman, P. Lovei, M. Funk, E. Deckers, S. Wensveen, Berry Eggen","doi":"10.1017/S0890060421000433","DOIUrl":"https://doi.org/10.1017/S0890060421000433","url":null,"abstract":"Abstract Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42086474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：