Abstract
Recently, it has been increased with respect to the safe and reliable operations in industry of machine tools and intelligent of the machine tool has consistently been developing in term of an unmanned manufacturing. For such realization, diagnosis monitoring of machining must be carried out while being processed in real-time. When tool wear is reached to criteria of flank wear and crater wear, the tools must be changed to new tools for improving the manless rate of operation. However, time of tool change was when spark generated because of wear about 0.3 mm on a flank face during manufacturing in the field. So, built-in sensor system in a smart machine tool must be necessary for high efficiency unmanned of manufacturing. As mentioned earlier, the various technique for measuring the tool wear was already defined such as sensing of acoustic emissions, vibrations, sounds, currents, cutting force, and other. The representative one of measuring method is current signal, which is used as a representative index of tool state. In this study, we carried out the proposal of tool wear criterion by using built-in wireless current signal system when manufacturing the mold materials of KP-4M and it was investigated via smart machine tools.
Highlights
Nowadays, competitive business environment, companies are facing as a goal with big data and developing decision-making algorithms for improving the productivity
Direct methods measure real wear changes of cutting edge by machine vision [3] and scanning electron microscope (SEM) [4]. These methods are affected by mist, manufacturing environment, reflecting condition and condition of machine tools. Indirect methods are such as acoustic emissions, vibrations, sounds, currents, cutting force, and they are utilizing as tool to diagnose the tool wear and machine tools and representative research case is an in the following
As a result, we found the tool wear point by using current signal and criterion of flank wear proposed via our experiments without quantitative measurement
Summary
Competitive business environment, companies are facing as a goal with big data and developing decision-making algorithms for improving the productivity. Direct methods measure real wear changes of cutting edge by machine vision [3] and scanning electron microscope (SEM) [4]. These methods are affected by mist, manufacturing environment, reflecting condition and condition of machine tools. The measuring methods by the cutting force were developed for fault detection diagnosis based on an observer model of an uncertain linear system from [9]. These are not suitable for detecting both tool wear and machine tool conditions, and a built-in sensor is required to satisfy this. It is necessary to develop algorithms suitable for diagnosing each case by built-in sensor system
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