دانلود مقاله انگلیسی رایگان:یک دوقلوی دیجیتال برای آموزش عامل یادگیری تقویتی عمیق برای کارخانه های تولید هوشمند: محیط ، رابط ها و هوش - 2020
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  • A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence
    A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 21
    حجم فایل: 12609 کیلوبایت

    قیمت: رایگان


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