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The machining know-how or knowledge exists in the NC programs if they are generated through experienced workers. To realize autonomous CNC machining, accumulation and representation of such know-how in a reusable way is needed. In this paper, an autonomous machining process analyzer for hole machining is studied. With the method, the machining process can be analyzed and the know-how can be extracted from existing successful NC programs. Specifically, machining feature, operation sequence, and cutting parameters including used tools, feedrate and spindle/cutting speed can be extracted. Based on the proposed method, a prototype system has been developed to verify the feasibility of the know-how extraction.

The paper deals with the end-milling machinability of α-β titanium alloy (Ti-6AI-4V alloy) by new PCD (Polycrystal-line Diamond) cutting tools at high cutting speeds in order to develop a new technology for a high productivity titanium finishing. The main focus of this paper is on investigating the relationships among cutting conditions, PCD tool materials, and tool wear. It was found that the edge engagement time of cutting tools with titanium workpiece has a great effect on PCD tool wear. Compared to cemented carbide tools, PCD tools have a longer tool life, especially at higher cutting speeds. The geometric shape of cutting edges and tool material greatly influences the performance of PCD tools in cutting titanium alloy.