A Machine Learning Approach for Collaborative Robot Smart Manufacturing Inspection for Quality Control Systems

Brito, Thadeu; Queiroz, Jonas; Piardi, Luis; Fernandes, Lucas A.; Lima, José; Leitão, Paulo

doi:10.1016/j.promfg.2020.10.003

Cited by 63 publications

(21 citation statements)

References 14 publications

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“…To support the workforce in quality related tasks, cobots can contribute to more stable processes. Brito et al (2020) addressed the collaborative cooperation to combine the accuracy of the robot with the flexibility of the workforce. In case of an unforeseen inspection incident, the workforce taught the robot its new path, which was learned and reproduced by the DDPG.…”

Section: Quality Controlmentioning

confidence: 99%

Deep reinforcement learning in production systems: a systematic literature review

Panzer

Bender

2021

International Journal of Production Research

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Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimisation of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyse safety aspects and demonstrate reliability under prevailing conditions.

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Section: Quality Controlmentioning

confidence: 99%

Deep reinforcement learning in production systems: a systematic literature review

Panzer

Bender

2021

International Journal of Production Research

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“…Robot soft hands are designed for flexibility and safety reasons, making it a promising feature for medical device manufacturing [110]. These collaborative ways can be used for carrying out tasks in quality control, such as smart inspection [111].…”

Section: Human-robot Collaborationmentioning

confidence: 99%

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Lepasepp

Hurst

2021

Future Internet

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Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

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“…Penerapan machine learning dalam industri, terutama industri manufaktur banyak diterapkan guna meningkatkan produktivitas dalam proses bisnisnya (Brito et al, 2020) (Ruiz et al, 2020). Datadata yang selama ini diolah secara manual akan di gantikan dengan algoritma dalam machine learning, sehingga di harapkan suatu keputusan penting dapat dilakukan secara cepat dan akurat (Ağbulut et al, 2020).…”

Section: Pendahuluanunclassified

Implementation of machine learning to increase productivity in the manufacturing industry: a literature review.

Purmala¹

2021

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Industry 4.0 is currently developing quite rapidly, one of the technologies that is currently very popular in the industry is artificial intelligence, where an event can be diagnosed and predicted more quickly and accurately. One of the branches of artificial intelligence that can do this is Machine Learning, and its application can now be found in daily activities. In the manufacturing industry, the application of Machine Learning is one of them is to increase productivity through the results of analysis and predictions given based on the experience gained. This study uses a systematic literature review method, in which several articles were collected from several journal databases such as Elsevier, IEE, Springer, Taylor & Francis and ACM, with the publication period of the articles from 2015 to 2020. A total of 100 articles were collected, then re-validated. suitability based on the main goals and objectives of the research. There were 36 articles that were validated and used as a reference for a more in-depth review and analysis of their boundaries, so that there was a gap for further research. In this literature review study, its application is very helpful in making decisions in improving the quality, efficiency, and performance of companies in the manufacturing industry. The most popular algorithms used in this study include random forest, support vector machine, neural network, linear regression, and k-nearest neighbor. Finally, in this study it was found that the application of Machine Learning in diagnosing or predicting an event is suggested by modeling more than one algorithm to find and determine which algorithm is the most accurate and suitable to be applied to the phenomenon that occurs.

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A Machine Learning Approach for Collaborative Robot Smart Manufacturing Inspection for Quality Control Systems

Cited by 63 publications

References 14 publications

Deep reinforcement learning in production systems: a systematic literature review

Deep reinforcement learning in production systems: a systematic literature review

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Implementation of machine learning to increase productivity in the manufacturing industry: a literature review.

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