Multi-objective optimization of milling process: exploring trade-off among energy consumption, time consumption and surface roughness

Despite the continuous acceleration of global industrial informatization process, the manufacturing industry is still facing many problems, such as the lack of data in the machining process, the low utilization rate of energy and so on. This paper proposes an integrated framework based on Internet of things (IoT) and machine learning to realize the monitoring and collection of original real-time data, data association and achieve the trade-off optimization among energy consumption, processing time and surface roughness for the milling process in manufacturing system. This framework is composed of four functional modules, i.e. IoT-based processing data acquisition, milling experiment design, performance index prediction based on machine learning and performance index multi-objective optimization. The optimization results exhibit obvious advantages in energy consumption saving, processing time reduction and surface roughness improvement for the milling process.