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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process
ترجمه فارسی عنوان مقاله:
استفاده از الگوریتم یادگیری تقویت کننده برای ترازبندی دقیق بازوی رباتیک در فرایند خودکار سازی نمایش نرم افزاری کفش هایفابریک
منبع:
Sciencedirect - Elsevier - Journal of Manufacturing Systems, 56 (2020) 501-513. doi:10.1016/j.jmsy.2020.07.001
نویسنده:
Yu-Ting Tsaia, Chien-Hui Leeb, Tao-Ying Liua, Tien-Jan Changa, Chun-Sheng Wanga, S.J. Pawarc, Pei-Hsing Huangb, Jin-H. Huanga
چکیده انگلیسی:
As usher in Industry 4.0, there has been much interest in the development and research that combine artificial
intelligence with automation. The control and operation of equipment in a traditional automated shoemaking
production line require a preliminary subjective judgment of relevant manufacturing processes, to determine the
exact procedure and corresponding control settings. However, with the manual control setting, it is difficult to
achieve an accurate quality assessment of an automated process characterized by high uncertainty and intricacy.
It is challenging to replace handicrafts and the versatility of manual product customization with automation
techniques. Hence, the current study has developed an automatic production line with a cyber-physical system
artificial intelligence (CPS-AI) architecture for the complete manufacturing of soft fabric shoe tongues. The
Deep-Q reinforcement learning (RL) method is proposed as a means of achieving better control over the manufacturing
process, while the convolutional and long short-term memory artificial neural network (CNN +
LSTM) is developed to enhance action speed. This technology allows a robotic arm to learn the specific image
feature points of a shoe tongue through repeated training to improve its manufacturing accuracy. For validation,
different parameters of the network architecture are tested, and the test convergence accuracy was found to be as
high as 95.9 %. During its actual implementation, the production line completed 509 finished products, of which
349 products were acceptable due to the anticipated measurement error. This showed that the production line
system was capable of achieving optimum product accuracy and quality with respect to the performance of
repeated computations, parameter updates, and action evaluations.
Keywords: Artificial intelligence | Shoemaking automation | Reinforcement learning | Cyber-physical system
قیمت: رایگان
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