کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8095751 | 1522066 | 2018 | 34 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cyber Physical System and Big Data enabled energy efficient machining optimisation
ترجمه فارسی عنوان
سیستم فیزیکی سایبر و داده های بزرگ انرژی بهینه سازی بهینه سازی ماشینکاری را فعال کرده اند
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کلمات کلیدی
سیستم فیزیکی سایبری، اطلاعات بزرگ، ماشینکاری کارآمد انرژی، بهینه سازی زمانبندی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
Due to increasingly customised manufacturing, unpredictable ambient working conditions in shop floors and stricter requirements on sustainability, it is challenging to achieve energy efficient optimisation for machining processes. This paper presents a novel Cyber Physical System (CPS) and Big Data enabled machining optimisation system to address the above challenge. The innovations and characteristics of the system include the following four aspects: (1) a novel process of “scheduling, monitoring/learning, rescheduling” is designed to enhance system adaptability during manufacturing lifecycles; (2) an innovative energy model to support energy efficient optimisation over manufacturing lifecycles is developed. The energy model, which is enabled by CPS, Big Data analytics and intelligent learning algorithms, considers dynamic and aging conditions of machine tool systems during manufacturing lifecycles; (3) an effective evolutional algorithm based on Fruit Fly Optimisation (FFO), is applied to generate an adaptive energy efficient schedule, and improve schedule when there are significantly varying working conditions and adjustments on the schedule are necessary (that is rescheduling); (4) the system has been successfully deployed into European machining companies to verify capabilities. According to the results, around 40% energy saving and 30% productivity improvement have been achieved in the companies. A practical case study presented in this paper demonstrates the effectiveness and great potential of applicability of the system in practice.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Cleaner Production - Volume 187, 20 June 2018, Pages 46-62
Journal: Journal of Cleaner Production - Volume 187, 20 June 2018, Pages 46-62
نویسندگان
Y.C. Liang, X. Lu, W.D. Li, S. Wang,