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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence
ترجمه فارسی عنوان مقاله:
یک دوقلوی دیجیتال برای آموزش عامل یادگیری تقویتی عمیق برای کارخانه های تولید هوشمند: محیط ، رابط ها و هوش
منبع:
Sciencedirect - Elsevier - Journal of Manufacturing Systems,Corrected proof,doi:10.1016/j.jmsy.2020.06.012
نویسنده:
Kaishu Xiaa, Christopher Saccoa, Max Kirkpatrickb, Clint Saidya, Lam Nguyena, Anil Kircalialia, Ramy Harika
چکیده انگلیسی:
Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work
proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing
systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system
behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is
accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management
solutions, and control platforms for automation systems. Second, a network of interfaces between the environments
is designed and implemented to enable communication between the digital world and physical
manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in
the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context
of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a
case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed
control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing
tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart
agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent
control algorithms are trained and verified upfront before deployed to the physical world for implementation.
Moreover, DRL approach to automated manufacturing control problems under facile optimization environments
will be a novel combination between data science and manufacturing industries.
Keywords: Smart manufacturing systems | Robotics | Artificial intelligence | Digital transformation | Virtual commissioning
قیمت: رایگان
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