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Abstract

Benefiting from the advancement of information and communication technology, more and more data related to product manufacturing process can be collected. Owing to long manufacturing cycle, dynamic manufacturing process and diverse sensors, the collected data featured by high volume, high velocity and high variety generate a typical big data pool. Therefore, a rapidly increasing number of big data analytics research on production application emerges. Accordingly various knowledge patterns underlid the data are recognized to support production decision-making activities. On one hand, the data analytics results accelerate the decision-making efficiently. On the other hand, the investigation on physics of manufacturing process are slacked up. Actually they do not collide with each other, but complement each other. Hence, a machine learning enabled transparent manufacturing framework is proposed.

Because the energy consumption of manufacturing equipments such as CNC machines is directly related to the workpiece, cutters and cutting parameters, it is selected as an indirect indicator to monitor the manufacturing process due to the cost-effective power sensors. By applying machine learning algorithms, the energy consumption patterns which corresponding to multifarious combinations of machines, cutters and cutting parameters can be recognized from the monitoring power data. Since cutters wear out gradually along with the consecutive manufacturing process, the energy consumption of producing the identical components with the same cutting parameters are different. Therefore the energy consumption for individual combination obtained from the collected data fluctuate within an interval. In contrast, the energy consumption of CNC machines can also be calculated based on the physics of cutting process which are expressed as the integral of the product of cutting force and cutting speed over cutting time. The accuracy of cutting force estimation heavily relies on the cutting force coefficients which are related to the tool wear condition. Generally the cutting force coefficients are calculated based on very limited number of physical experiments, a single value for each coefficient are applied for cutting force estimation. Apparently it cannot reflect the real cutting process. Therefore, their conjunction can explain the manufacturing process in more accurate way and make the manufacturing process transparent.

Since the energy consumption models based on collecting data and cutting physics have their own pros and cons, a hybrid models based decision making engine are expect to be developed to support production activities efficiently within transparent manufacturing scenario.

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