Computers in Industry最新文献_第4页

Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company 利用自然语言处理将维护行动分解为子任务：意大利一家汽车公司的案例研究

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-17 DOI: 10.1016/j.compind.2024.104186

Vito Giordano , Gualtiero Fantoni

{"title":"Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company","authors":"Vito Giordano , Gualtiero Fantoni","doi":"10.1016/j.compind.2024.104186","DOIUrl":"10.1016/j.compind.2024.104186","url":null,"abstract":"<div><p>Industry 4.0 has led to a huge increase in data coming from machine maintenance. At the same time, advances in Natural Language Processing (NLP) and Large Language Models provide new ways to analyse this data. In our research, we use NLP to analyse maintenance work orders, and specifically the descriptions of failures and the corresponding repair actions. Many NLP studies have focused on failure descriptions for categorising them, extracting specific information about failure, or supporting failure analysis methodologies (such as FMEA). Whereas, the analysis of repair actions and its relationship with failure remains underexplored. Addressing this gap, our study makes three significant contributions. Firstly, we focused on the Italian language, which presents additional challenges due to the dominance of NLP systems that are mainly designed for English. Secondly, it proposes a method for automatically subdividing a repair action into a set of sub-tasks. Lastly, it introduces an approach that employs association rule mining to recommend sub-tasks to maintainers when addressing failures. We tested our approach with a case study from an automotive company in Italy. The case study provides insights into the current barriers faced by NLP applications in maintenance, offering a glimpse into the future opportunities for smart maintenance systems.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104186"},"PeriodicalIF":8.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524001143/pdfft?md5=60b4c04fc51db998076996dc8ccd709b&pid=1-s2.0-S0166361524001143-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142243208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedCPG: A class prototype guided personalized lightweight federated learning framework for cross-factory fault detection FedCPG：用于跨工厂故障检测的类原型引导的个性化轻量级联合学习框架

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-16 DOI: 10.1016/j.compind.2024.104180

Haodong Li , Xingwei Wang , Peng Cao , Ying Li , Bo Yi , Min Huang

{"title":"FedCPG: A class prototype guided personalized lightweight federated learning framework for cross-factory fault detection","authors":"Haodong Li , Xingwei Wang , Peng Cao , Ying Li , Bo Yi , Min Huang","doi":"10.1016/j.compind.2024.104180","DOIUrl":"10.1016/j.compind.2024.104180","url":null,"abstract":"<div><p>Industrial equipment condition monitoring and fault detection are crucial to ensure the reliability of industrial production. Recently, data-driven fault detection methods have achieved significant success, but they all face challenges due to data fragmentation and limited fault detection capabilities. Although centralized data collection can improve detection accuracy, the conflicting interests brought by data privacy issues make data sharing between different devices impractical, thus forming the problem of industrial data silos. To address these challenges, this paper proposes a class prototype guided personalized lightweight federated learning framework(FedCPG). This framework decouples the local network, only uploading the backbone model to the server for model aggregation, while employing the head model for local personalized updates, thereby achieving efficient model aggregation. Furthermore, the framework incorporates prototype constraints to steer the local personalized update process, mitigating the effects of data heterogeneity. Finally, a lightweight feature extraction network is designed to reduce communication overhead. Multiple complex industrial data distribution scenarios were simulated on two benchmark industrial datasets. Extensive experiments have demonstrated that FedCPG can achieve an average detection accuracy of 95% in complex industrial scenarios, while simultaneously reducing memory usage and the number of parameters by 82%, surpassing existing methods in most average metrics. These findings offer novel perspectives on the application of personalized federated learning in industrial fault detection.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104180"},"PeriodicalIF":8.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142243071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A technical patent map construction method and system based on multi-dimensional technical feature extraction 一种基于多维技术特征提取的技术专利地图构建方法和系统

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-12 DOI: 10.1016/j.compind.2024.104167

Chuanxiao Li , Wenqiang Li , Hai Xiang , Yida Hong

{"title":"A technical patent map construction method and system based on multi-dimensional technical feature extraction","authors":"Chuanxiao Li , Wenqiang Li , Hai Xiang , Yida Hong","doi":"10.1016/j.compind.2024.104167","DOIUrl":"10.1016/j.compind.2024.104167","url":null,"abstract":"<div><p>A patent map is widely used in the field of technical information mining, which can support tasks such as detecting patent vacuums and predicting technical trends. However, existing patent map construction methods have the limitations of insufficient intelligence and accuracy in mining patent technical features, which leads to failure to effectively complete the above tasks. To address these limitations, this paper proposes a patent map construction method based on multi-dimensional technical feature mining that mainly includes the following three stages. First, on the basis of the dependency parsing technology, the technical features contained in patents are fully mined in the form of triplets from three dimensions: function, behaviour and structure. Second, on the basis of Wordnet, the original triplets in three dimensions are standardised for different task scenarios. Finally, on the basis of the standard triplets, the patent map can be constructed to detect patent vacuums and support design tasks. In addition, a prototype system is developed based on the proposed method, and the effectiveness and practicability of the method and system are verified using a 3D printer as an engineering example.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104167"},"PeriodicalIF":8.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study 通过数字化库存和按需制造实现虚拟仓储：案例研究

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-11 DOI: 10.1016/j.compind.2024.104184

Elham Sharifi , Atanu Chaudhuri , Saeed D. Farahani , Lasse G. Staal , Brian Vejrum Waehrens

{"title":"Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study","authors":"Elham Sharifi , Atanu Chaudhuri , Saeed D. Farahani , Lasse G. Staal , Brian Vejrum Waehrens","doi":"10.1016/j.compind.2024.104184","DOIUrl":"10.1016/j.compind.2024.104184","url":null,"abstract":"<div><p>Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104184"},"PeriodicalIF":8.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016636152400112X/pdfft?md5=60389a1452533dbdc2dab1d2bedbd96f&pid=1-s2.0-S016636152400112X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning 3D human–object interaction graphs from transferable context knowledge for construction monitoring 从可转移的情境知识中学习三维人-物互动图，用于建筑监测

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-10 DOI: 10.1016/j.compind.2024.104171

Liuyue Xie, Shreyas Misra, Nischal Suresh, Justin Soza-Soto, Tomotake Furuhata, Kenji Shimada

{"title":"Learning 3D human–object interaction graphs from transferable context knowledge for construction monitoring","authors":"Liuyue Xie, Shreyas Misra, Nischal Suresh, Justin Soza-Soto, Tomotake Furuhata, Kenji Shimada","doi":"10.1016/j.compind.2024.104171","DOIUrl":"10.1016/j.compind.2024.104171","url":null,"abstract":"<div><p>We propose a novel framework for detecting 3D human–object interactions (HOI) in construction sites and a toolkit for generating construction-related human–object interaction graphs. Computer vision methods have been adopted for construction site safety surveillance in recent years. The current computer vision methods rely on videos and images, with which safety verification is performed on common-sense knowledge, without considering 3D spatial relationships among the detected instances. We propose a new method to incorporate spatial understanding by directly inferring the interactions from 3D point cloud data. The proposed model is trained on a 3D construction site dataset generated from our crafted simulation toolkit. The model achieves 54.11% mean interaction over union (mIOU) and 72.98% average mean precision(mAP) for the worker–object interaction relationship recognition. The model is also validated on PiGraphs, a benchmarking dataset with 3D human–object interaction types, and compared against other existing 3D interaction detection frameworks. It was observed that it achieves superior performance from the state-of-the-art model, increasing the interaction detection mAP by 17.01%. Besides the 3D interaction model, we also simulate interactions from industrial surveillance footage using MoCap and physical constraints, which will be released to foster future studies in the domain.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104171"},"PeriodicalIF":8.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016636152400099X/pdfft?md5=5de4190059c557871f94dcddc09652d4&pid=1-s2.0-S016636152400099X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Operational process monitoring: An object-centric approach 运行过程监控：以对象为中心的方法

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-10 DOI: 10.1016/j.compind.2024.104170

Gyunam Park, Wil M.P. van der Aalst

{"title":"Operational process monitoring: An object-centric approach","authors":"Gyunam Park, Wil M.P. van der Aalst","doi":"10.1016/j.compind.2024.104170","DOIUrl":"10.1016/j.compind.2024.104170","url":null,"abstract":"<div><p>In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems are detected, organizations can act upon the corresponding problems with viable actions, e.g., adding more resources, bypassing problematic activities, etc. A plethora of approaches have been proposed to implement operational process monitoring. The lion’s share of existing approaches assumes that a single case notion (e.g., a purchase order in a P2P process) exists in a business process and analyzes operational problems defined over the single case notion. However, most real-life business processes manifest the interplay of multiple interrelated objects. For instance, an execution of an omnipresent P2P process involves multiple objects of different types, e.g., purchase orders, goods receipts, invoices, etc. Applying the existing approaches in these object-centric business processes results in inaccurate or misleading results. In this study, we propose a novel approach to assessing operational problems within object-centric business processes. Our approach not only ensures an accurate assessment of existing problems but also facilitates the analysis of object-centric problems that consider the interaction among different objects. We evaluate this approach by applying it to both simulated business processes and real-life business processes.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104170"},"PeriodicalIF":8.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000988/pdfft?md5=98100c0921f5cbb1ff207fca5e3978cf&pid=1-s2.0-S0166361524000988-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning 通过动态混合域适应和注意力对比学习建立跨域滚动轴承剩余使用寿命预测模型

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-10 DOI: 10.1016/j.compind.2024.104172

Xingchi Lu , Xuejian Yao , Quansheng Jiang , Yehu Shen , Fengyu Xu , Qixin Zhu

{"title":"Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning","authors":"Xingchi Lu , Xuejian Yao , Quansheng Jiang , Yehu Shen , Fengyu Xu , Qixin Zhu","doi":"10.1016/j.compind.2024.104172","DOIUrl":"10.1016/j.compind.2024.104172","url":null,"abstract":"<div><p>Performance degradation and remaining useful life (RUL) prediction are of great significance in improving the reliability of mechanical equipment. Existing cross-domain RUL prediction methods usually reduce data distribution discrepancy by domain adaptation, to overcome domain shift under cross-domain conditions. However, the fine-grained information between cross-domain degradation features and the specific characteristics of the target domain are often ignored, which limits the prediction performance. Aiming at these issues, a RUL prediction method based on dynamic hybrid domain adaptation (DHDA) and attention contrastive learning (A-CL) is proposed for the cross-domain rolling bearings. In the DHDA module, the conditional distribution alignment is achieved by the designed pseudo-label-guided domain adversarial network, and is assigned with a dynamic penalty term to dynamically adjust the conditional distribution when aligning the joint distribution, for improving the fine-grainedness of domain adaptation. The A-CL module aims to help the prediction model actively extract the degradation information of the target domain, to generate the degradation features that match the characteristics of the target domain, for improving the robustness of RUL prediction. Then, the proposed method is verified by the ablation and comparison experiments conducted on PHM2012 and XJTU-SY datasets. The results show that the proposed method performs high accuracy for cross-domain RUL prediction with good generalization performance under three different cross-domain scenarios.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104172"},"PeriodicalIF":8.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting coagulation time in cheese making by means of computer vision and machine learning techniques 利用计算机视觉和机器学习技术检测奶酪制作过程中的凝固时间

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-09 DOI: 10.1016/j.compind.2024.104173

Andrea Loddo , Cecilia Di Ruberto , Giuliano Armano , Andrea Manconi

{"title":"Detecting coagulation time in cheese making by means of computer vision and machine learning techniques","authors":"Andrea Loddo , Cecilia Di Ruberto , Giuliano Armano , Andrea Manconi","doi":"10.1016/j.compind.2024.104173","DOIUrl":"10.1016/j.compind.2024.104173","url":null,"abstract":"<div><p>Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104173"},"PeriodicalIF":8.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524001015/pdfft?md5=049ee78fc600c8a36c293c17fd46e748&pid=1-s2.0-S0166361524001015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples 先验知识嵌入卷积自动编码器：小样本下的单源域广义故障诊断框架

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-07 DOI: 10.1016/j.compind.2024.104169

Feiyu Lu , Qingbin Tong , Xuedong Jiang , Xin Du , Jianjun Xu , Jingyi Huo

{"title":"Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples","authors":"Feiyu Lu , Qingbin Tong , Xuedong Jiang , Xin Du , Jianjun Xu , Jingyi Huo","doi":"10.1016/j.compind.2024.104169","DOIUrl":"10.1016/j.compind.2024.104169","url":null,"abstract":"<div><p>The proposed transfer learning-based fault diagnosis models have achieved good results in multi-source domain generalization (MDG) tasks. However, research on single-source domain generalization (SDG) is relatively scarce, and the limited availability of small training samples is seldom considered. Therefore, to address the insufficient feature extraction capability and poor generalization performance of existing models on single-source domain small sample data, a novel single-source domain generalization fault diagnosis (SDGFD) framework, the prior knowledge embedded convolutional autoencoder (PKECA), is proposed. During the training phase, first, single-source domain data are used to construct prior features based on the time domain, frequency domain, and time-frequency domain. Second, a prior knowledge embedding structure based on the convolutional autoencoder is built, which compresses the prior knowledge and original vibration data into a high-dimensional space of consistent dimensions, embedding the prior knowledge into the features corresponding to the vibration data using a mean squared error loss function. Subsequently, the proposed centroid-based self-supervised learning (CBSSL) strategy further constrains high-dimensional features, improving the generalization ability. The designed sparse regularized activation (SRA) function significantly enhances the regularization effect on features. During the testing phase, it is only necessary to input the data from the unknown domain to identify the fault types. The experimental results show that the proposed method achieves superior performance in fault diagnosis tasks involving cross-speed, time-varying speed, and small sample data in SDGFD, demonstrating that PKECA has strong generalizability. The code can be found here: https://github.com/John-520/PKECA. © 2024 Elsevier Science. All rights reserved</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104169"},"PeriodicalIF":8.2,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computers as co-creative assistants. A comparative study on the use of text-to-image AI models for computer aided conceptual design 作为共同创作助手的计算机。关于在计算机辅助概念设计中使用文本到图像人工智能模型的比较研究

IF 8.2 1区计算机科学

Computers in Industry Pub Date : 2024-09-07 DOI: 10.1016/j.compind.2024.104168

Jorge Alcaide-Marzal, Jose Antonio Diego-Mas

{"title":"Computers as co-creative assistants. A comparative study on the use of text-to-image AI models for computer aided conceptual design","authors":"Jorge Alcaide-Marzal, Jose Antonio Diego-Mas","doi":"10.1016/j.compind.2024.104168","DOIUrl":"10.1016/j.compind.2024.104168","url":null,"abstract":"<div><p>This preliminary research presents a comparative study between Text-to-Image AI models and Shape Grammars, one of the main generative approaches to Computer Aided Conceptual Design. The goal is to determine to which extent AI models can reproduce or complement the performance of grammar algorithms as creative support tools for shape exploration in conceptual product design. Workflows, advantages and limitations are identified through a comprehensive practical comparison example. The results show many similarities regarding generative capabilities and highlight several advantages of Text-to-Image AI models, including an easier way of capturing product grammars and a wider and more immediate range of further applications. In contrast, Shape Grammars approach proved more solid in aspects related to exploration workflows and cognitive stimulation. These results encourage the research on new ways to address the interaction between designers and AI generative models, combining the AI potential with well-established generative strategies.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104168"},"PeriodicalIF":8.2,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000964/pdfft?md5=1f56acc9291a1170ec8ff973996755fd&pid=1-s2.0-S0166361524000964-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0