Abstract

In CNC milling, the feed rate scheduling is a frequently used method to increase machining quality and efficiency. Among the benefits of feed rate scheduling, this paper focuses on controlling the tool load and optimizing the machining time. Although the advantages of feed rate scheduling are undeniable, some areas remain still to be addressed. In order to control the tool load, geometric methods are often used, which are based on keeping a specific parameter, such as chip thickness or material removal rate (MRR) constant. However, a high level of tool load control can only be provided if cutting force models or experimental-based techniques are used. Besides traditional methods, this paper presents an artificial neural network (ANN)-based feed rate scheduling method to keep the tool load constant, using data gained by preliminary cutting experiments. A case study demonstrates that a significantly higher level of tool load control can be achieved with this method as compared to the geometric models. Besides controlling the tool load, the present feed rate scheduling method also addresses the consideration of acceleration limits which is of great importance for practical uses. The application of feed rate scheduling in trochoidal milling is also discussed in detail in this paper. This area has not received enough attention, as due to the limited fluctuation of cutter engagement, the tool load was considered as well-controlled. However, experiments have shown that in the case of trochoidal milling, the introduction of feed rate scheduling can still further increase the machining efficiency. Using the developed ANN-based feed rate scheduling method, significant progress could be made as compared to conventional technologies in controlling the cutting force and optimizing the machining time. In the present case study, a reduction of 50% in machining time was achievable by adjusting the feed rate without increasing the peak value of cutting force.

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