| Summary: | 碩士 === 崑山科技大學 === 機械工程研究所 === 105 === Internationally for the machine tool began to green production, with high efficiency, low pollution, low energy consumption, recyclable, resource reusable and other aspects of development, and is expected to tool machine energy into control; this study is divided into two directions: 1 . CNC machine tool intelligent system development, the establishment of a cloud data collection system to provide accessory auxiliary function. The cutting energy measurement analysis tests validation of intelligent system were also executed. 2. The numerical control machine tool energy consumption of five-axis machine tool. Effects of cutting parameters on energy consumption were studied. Experiment was carried on five-axis synchronous operation. The cutting energy analysis tests were carried out on aluminum.
The study completed the following work: This experiment uses C # to establish a CNC machine tool intelligent system processing program, through the CNC machine tool signal acquisition and senior engineers experience in processing, when the auxiliary system and the machine after the connection read the workpiece of the program, CAD / CAM features, tooling machine processing of real-time information, processing parameters and so on. The cutting energy measurement analysis test validation of intelligent system in the three parameters of the total consumption of the third sets of experimental results were economic. The numerical control machine tool energy consumption of five-axis machine tool showed that the most obvious difference in the selected three parameters is the depth of the cut for processing the same finished product. The use of a higher depth of cut could be done in a relatively short period of time, but also consume less energy. Under the same cutting depth, for feed rate 1400 mm / min, the total power consumption of the workpiece was slightly better than that of the feed rate of 1000 mm / min.
The future of the company through the system, the previous production of parts of the information to be summarized in the storage, and further to do the new parts processing parameters set the benchmark to accumulate predecessors wisdom and reduce staff training.
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