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Cyber Physical System and Big Data enabled energy efficient machining optimisation
سیستم فیزیکی سایبری و بهینه سازی ماشین انرژی توانای داده های بزرگ-2018 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 al
gorithms, considers dynamic and aging conditions of machine tool systems during manufacturing life
cycles; (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.
Keywords: Cyber physical system ، Big data ، Energy efficient machining ، Scheduling optimisation |
مقاله انگلیسی |