Pub Date : 2023-09-13DOI: 10.1080/0951192x.2023.2257918
Minna Saunila, Juhani Ukko, Mina Nasiri, Patrizia Garengo
This study aims to investigate the effect of different types of performance measurement systems (PMSs) use (diagnostic use and interactive use) on Industry 4.0 maturity, examining whether there is a need for digital governance to facilitate the relationship between different types of PMS use (diagnostic use and interactive use) and Industry 4.0 maturity. Although the use of PMSs has been identified as beneficial in the Industry 4.0 context, relatively little research exists on the digital governance that enables firms to lead and control digital processes. The paper posits that digital governance plays an important role in mediating the relationship between PMS use and Industry 4.0 maturity. The data were gathered from 280 small- and medium-sized enterprises (SMEs), which operate in the service and manufacturing industry in Finland. The results demonstrate that different types of PMSs use cannot provide Industry 4.0 maturity alone, so there is a need for digital governance to fuel different types of PMS use, hence leading to Industry 4.0 maturity. However, diagnostic use of PMSs significantly hinders digital governance, while the interactive use of PMSs significantly drives digital governance. Finally, digital governance facilitates Industry 4.0 maturity.
{"title":"The role of digital governance in the integration of performance measurement systems uses and Industry 4.0 maturity","authors":"Minna Saunila, Juhani Ukko, Mina Nasiri, Patrizia Garengo","doi":"10.1080/0951192x.2023.2257918","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257918","url":null,"abstract":"This study aims to investigate the effect of different types of performance measurement systems (PMSs) use (diagnostic use and interactive use) on Industry 4.0 maturity, examining whether there is a need for digital governance to facilitate the relationship between different types of PMS use (diagnostic use and interactive use) and Industry 4.0 maturity. Although the use of PMSs has been identified as beneficial in the Industry 4.0 context, relatively little research exists on the digital governance that enables firms to lead and control digital processes. The paper posits that digital governance plays an important role in mediating the relationship between PMS use and Industry 4.0 maturity. The data were gathered from 280 small- and medium-sized enterprises (SMEs), which operate in the service and manufacturing industry in Finland. The results demonstrate that different types of PMSs use cannot provide Industry 4.0 maturity alone, so there is a need for digital governance to fuel different types of PMS use, hence leading to Industry 4.0 maturity. However, diagnostic use of PMSs significantly hinders digital governance, while the interactive use of PMSs significantly drives digital governance. Finally, digital governance facilitates Industry 4.0 maturity.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135785115","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 : 2023-09-12DOI: 10.1080/0951192x.2023.2257635
Orestis Spyrou, Cor Verdouw, William Hurst
The production of pharmaceutical cannabis is a complex and dynamic industry that has to meet critical challenges concerning product quality, compliance, traceability, food safety, sustainability and health. Digital twins have the potential to be powerful enablers for producers to meet these challenges. However, digital twins for the pharmaceutical production of cannabis are still under exploration and not yet researched. This paper contributes to overcoming this situation by proposing a reference architecture for the development and implementation of digital twins in this domain. Based on a design-oriented methodology, it defines and applies a coherent set of architecture views for modelling digital twin-based systems. Furthermore, a proof of concept of an immersive digital twin has been developed in order to test the applicability of reference architecture. This digital twin is developed in the open, cross-industry platform Unity and includes an extensive 3D model of a cannabis production facility. It is connected with real-world data through an application programming interface integration displaying real-time sensor data from a live greenhouse. The 3D environment is fully explorable, where the user takes control of an avatar character to walk around the facility and view real-time sensor readings. The expert validation shows that the developed digital twin is a valuable and innovative first step for remote management of pharmaceutical cannabis production. Further developments are needed to leverage its full potential, especially adding more types of sensor data, developing implementation-specific 3D models, extending the digital twin with predictive and prescriptive capabilities and connecting it to actuators.
{"title":"A digital twin reference architecture for pharmaceutical cannabis production","authors":"Orestis Spyrou, Cor Verdouw, William Hurst","doi":"10.1080/0951192x.2023.2257635","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257635","url":null,"abstract":"The production of pharmaceutical cannabis is a complex and dynamic industry that has to meet critical challenges concerning product quality, compliance, traceability, food safety, sustainability and health. Digital twins have the potential to be powerful enablers for producers to meet these challenges. However, digital twins for the pharmaceutical production of cannabis are still under exploration and not yet researched. This paper contributes to overcoming this situation by proposing a reference architecture for the development and implementation of digital twins in this domain. Based on a design-oriented methodology, it defines and applies a coherent set of architecture views for modelling digital twin-based systems. Furthermore, a proof of concept of an immersive digital twin has been developed in order to test the applicability of reference architecture. This digital twin is developed in the open, cross-industry platform Unity and includes an extensive 3D model of a cannabis production facility. It is connected with real-world data through an application programming interface integration displaying real-time sensor data from a live greenhouse. The 3D environment is fully explorable, where the user takes control of an avatar character to walk around the facility and view real-time sensor readings. The expert validation shows that the developed digital twin is a valuable and innovative first step for remote management of pharmaceutical cannabis production. Further developments are needed to leverage its full potential, especially adding more types of sensor data, developing implementation-specific 3D models, extending the digital twin with predictive and prescriptive capabilities and connecting it to actuators.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135826704","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 : 2023-09-11DOI: 10.1080/0951192x.2023.2257633
Praful P. Ulhe, Aditya D. Dhepe, Vaibhav Devidas Shevale, Yash S. Warghane, Prayag S Jadhav, Success L. Babhare
Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations.
{"title":"Flexibility management and decision making in cyber-physical systems utilizing digital lean principles with Brain-inspired computing pattern recognition in Industry 4.0","authors":"Praful P. Ulhe, Aditya D. Dhepe, Vaibhav Devidas Shevale, Yash S. Warghane, Prayag S Jadhav, Success L. Babhare","doi":"10.1080/0951192x.2023.2257633","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257633","url":null,"abstract":"Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135937835","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 : 2023-07-23DOI: 10.1080/0951192x.2023.2228262
A. Mollajan, Fatemeh Hamedani-KarAzmoudehFar, A. Ashofteh, A. Shahdadi, S. Iranmanesh
{"title":"Design of integrated manufacturing information systems for reconfigurability and adaptability by modularizing the system architecture","authors":"A. Mollajan, Fatemeh Hamedani-KarAzmoudehFar, A. Ashofteh, A. Shahdadi, S. Iranmanesh","doi":"10.1080/0951192x.2023.2228262","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2228262","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"15 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74936788","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 : 2023-07-11DOI: 10.1080/0951192X.2023.2235679
Jingchao Jiang, Bin Zou, Jikai Liu, David Rosen
After 30 years of continuous development, additive manufacturing has successfully achieved mainstream acceptance as a popular manufacturing process. By creating products based on a 3D model, layer-bylayer, additive manufacturing allows for the production of complex parts with greater freedom in design optimization, surpassing traditional manufacturing techniques (Gibson et al. 2021). Meanwhile, machine learning has emerged as a hot technology with numerous applications in medical diagnosis, image processing, prediction, classification, learning association, and regression (Kotsiantis, Zaharakis, and Pintelas 2006). This has drawn increasing attention towards the use of machine learning in the manufacturing industry, particularly in the field of additive manufacturing (Jiang et al. 2020; Qin et al. 2022). The rapid progress of machine learning in additive manufacturing has brought us to this special issue, where we hope to bring together researchers with diverse research backgrounds in a common forum to accelerate the development of additive manufacturing technology through the aid of machine learning. We are enthusiastic about contributing to this cutting-edge research topic and driving further advancements in AM technology. In this special issue, there are nine papers accepted in the end with authors from Germany, India, Denmark, United States, Australia, Canada, Singapore, and China. The paper ‘A survey of machine learning in additive manufacturing technologies’ (Jiang 2023) gives a stateof-the-art survey on machine learning in additive manufacturing and provides some guidelines for future applications of machine learning in additive manufacturing. The next paper ‘Towards deep-learning-based image enhancement for optical camera-based monitoring system of laser powder bed fusion process’ (Zhang et al. 2022) introduces a super-resolution (SR) algorithm based on U-Net that can effectively enhance the details of optical camera monitoring images in the laser powder bed fusion (LPBF) process. A test setup was constructed in the laboratory to generate highresolution images for training. To obtain accurate original images for validation purposes, lowresolution images were created by downscaling and blurring high-resolution images. The effectiveness of the SR algorithm was evaluated using the peak signalto-noise ratio (PSNR) and plausibility of details as metrics. The results clearly demonstrate that this SR algorithm is capable of reconstructing intricate features from low-resolution images in the LPBF process. The paper ‘Prediction of mechanical properties for acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling using artificial neural network and genetic algorithm’ (Mohd et al. 2022) uses a multi-parameter regression model to predict the mechanical properties of acrylonitrile-butadienestyrene parts manufactured by fused deposition modelling. The model establishes a direct relation between choosing process parameters correc
经过30年的不断发展，增材制造已经成功地获得了主流的认可，成为一种流行的制造工艺。通过基于3D模型逐层创建产品，增材制造允许生产复杂零件，在设计优化方面具有更大的自由度，超越了传统制造技术(Gibson et al. 2021)。与此同时，机器学习已经成为一项热门技术，在医学诊断、图像处理、预测、分类、学习关联和回归等领域有许多应用(Kotsiantis, Zaharakis, and Pintelas 2006)。这引起了人们对机器学习在制造业中的应用的越来越多的关注，特别是在增材制造领域(Jiang et al. 2020;Qin et al. 2022)。机器学习在增材制造中的快速发展为我们带来了这个特别的问题，我们希望将具有不同研究背景的研究人员聚集在一个共同的论坛上，通过机器学习的帮助来加速增材制造技术的发展。我们热衷于为这一前沿研究课题做出贡献，并推动增材制造技术的进一步发展。本次特刊共收稿9篇，作者分别来自德国、印度、丹麦、美国、澳大利亚、加拿大、新加坡和中国。论文“增材制造技术中的机器学习调查”(Jiang 2023)对增材制造中的机器学习进行了最新的调查，并为机器学习在增材制造中的未来应用提供了一些指导方针。下一篇论文“基于深度学习的图像增强，用于基于光学摄像机的激光粉床融合过程监控系统”(Zhang et al. 2022)介绍了一种基于U-Net的超分辨率(SR)算法，该算法可以有效增强激光粉床融合(LPBF)过程中光学摄像机监控图像的细节。在实验室建立了一个测试装置，生成高分辨率图像用于训练。为了获得准确的原始图像以进行验证，通过缩小和模糊高分辨率图像来创建低分辨率图像。使用峰值信噪比(PSNR)和细节的合理性作为度量来评估SR算法的有效性。结果清楚地表明，该算法能够在LPBF过程中从低分辨率图像中重建复杂的特征。论文“利用人工神经网络和遗传算法预测用熔融沉积建模制造的丙烯腈-丁二烯-苯乙烯零件的力学性能”(Mohd et al. 2022)使用多参数回归模型预测用熔融沉积建模制造的丙烯腈-丁二烯零件的力学性能。该模型建立了正确选择工艺参数与提高熔融沉积建模性能之间的直接关系。该模型能够提供分配输出值的最优解。论文“缸内光聚合错误检测的在线监测”(Frumosu et al. 2023)提出了一种自下而上光聚合增材制造系统的在线监测系统。传感器产生的数据用于检测制造零件与构建平台的分离误差，这是机器操作员无法实际观察到的。如果未被发现，这种分离可能会导致材料浪费和停机，而不会停止正在进行的构建工作。在线监测过程分两个不同的阶段进行:离线训练阶段，然后是在线监测阶段。在离线阶段，训练一个预测模型，与在线监测的控制图一起使用。监测控制图是特别有益的，因为它允许检测和记录脱离预测。下一篇论文“在智能制造中使用监督数据进行异常检测的学习”(Meiling et al. 2023)提出了一种模型选择架构。9,1255 - 1257 https://doi.org/10.1080/0951192X.2023.2235679
{"title":"Special issue on machine learning in additive manufacturing","authors":"Jingchao Jiang, Bin Zou, Jikai Liu, David Rosen","doi":"10.1080/0951192X.2023.2235679","DOIUrl":"https://doi.org/10.1080/0951192X.2023.2235679","url":null,"abstract":"After 30 years of continuous development, additive manufacturing has successfully achieved mainstream acceptance as a popular manufacturing process. By creating products based on a 3D model, layer-bylayer, additive manufacturing allows for the production of complex parts with greater freedom in design optimization, surpassing traditional manufacturing techniques (Gibson et al. 2021). Meanwhile, machine learning has emerged as a hot technology with numerous applications in medical diagnosis, image processing, prediction, classification, learning association, and regression (Kotsiantis, Zaharakis, and Pintelas 2006). This has drawn increasing attention towards the use of machine learning in the manufacturing industry, particularly in the field of additive manufacturing (Jiang et al. 2020; Qin et al. 2022). The rapid progress of machine learning in additive manufacturing has brought us to this special issue, where we hope to bring together researchers with diverse research backgrounds in a common forum to accelerate the development of additive manufacturing technology through the aid of machine learning. We are enthusiastic about contributing to this cutting-edge research topic and driving further advancements in AM technology. In this special issue, there are nine papers accepted in the end with authors from Germany, India, Denmark, United States, Australia, Canada, Singapore, and China. The paper ‘A survey of machine learning in additive manufacturing technologies’ (Jiang 2023) gives a stateof-the-art survey on machine learning in additive manufacturing and provides some guidelines for future applications of machine learning in additive manufacturing. The next paper ‘Towards deep-learning-based image enhancement for optical camera-based monitoring system of laser powder bed fusion process’ (Zhang et al. 2022) introduces a super-resolution (SR) algorithm based on U-Net that can effectively enhance the details of optical camera monitoring images in the laser powder bed fusion (LPBF) process. A test setup was constructed in the laboratory to generate highresolution images for training. To obtain accurate original images for validation purposes, lowresolution images were created by downscaling and blurring high-resolution images. The effectiveness of the SR algorithm was evaluated using the peak signalto-noise ratio (PSNR) and plausibility of details as metrics. The results clearly demonstrate that this SR algorithm is capable of reconstructing intricate features from low-resolution images in the LPBF process. The paper ‘Prediction of mechanical properties for acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling using artificial neural network and genetic algorithm’ (Mohd et al. 2022) uses a multi-parameter regression model to predict the mechanical properties of acrylonitrile-butadienestyrene parts manufactured by fused deposition modelling. The model establishes a direct relation between choosing process parameters correc","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"88 1","pages":"1255 - 1257"},"PeriodicalIF":4.1,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84447268","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 : 2023-07-05DOI: 10.1080/0951192x.2023.2228270
N. Paape, J.A.W.M. van Eekelen, M. Reniers
{"title":"Review of simulation software for cyber-physical production systems with intelligent distributed production control","authors":"N. Paape, J.A.W.M. van Eekelen, M. Reniers","doi":"10.1080/0951192x.2023.2228270","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2228270","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"513 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77851161","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 : 2023-06-30DOI: 10.1080/0951192x.2023.2228254
G. Kokotinis, G. Michalos, Z. Arkouli, S. Makris
{"title":"Α Behavior Trees-based architecture towards operation planning in hybrid manufacturing","authors":"G. Kokotinis, G. Michalos, Z. Arkouli, S. Makris","doi":"10.1080/0951192x.2023.2228254","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2228254","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"13 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78777324","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 : 2023-06-29DOI: 10.1080/0951192x.2023.2229288
Julius Pettersson, P. Falkman
{"title":"Intended Human Arm Movement Direction Prediction using Eye Tracking","authors":"Julius Pettersson, P. Falkman","doi":"10.1080/0951192x.2023.2229288","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2229288","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"4 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72828800","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 : 2023-06-27DOI: 10.1080/0951192x.2023.2228271
K. Manjunath, S. Tewary, Neha Khatri, K. Cheng
{"title":"In-process monitoring of the ultraprecision machining process with convolution neural networks","authors":"K. Manjunath, S. Tewary, Neha Khatri, K. Cheng","doi":"10.1080/0951192x.2023.2228271","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2228271","url":null,"abstract":"","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"19 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84531497","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}
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