The deep hole processing with small diameter is required from manufacturing the dies with high precise for aluminum die-casting for a next-generation automotive. The automatic deep hole processing of small diameter is difficult, caused by difficulty in chip discharge and automatic control of processing conditions according to drill wear state. Currently, the deep hole processing of the die for aluminum die-casing is carrying out by an expert technician manually. When the information obtained by quantifying the expert technician’s skills and techniques for the deep hole processing is characterized and is inputted to an automatic machining system as the processing conditions, the deep hole processing can be performed automatically and effectively. For the purpose of quantifying the above skills and techniques, cutting resistance during deep hole processing by an expert technician is measured, evaluated and characterized based on IoT concept. In this study, in order to characterize the skills and techniques, an intermediate technician and a beginner carry out the same deep hole processing. It was quantitatively clarified that the expert technician carried out the deep hole processing with high processing efficiency. Maximum cutting resistance of the expert technician has excellent potential as the parameter imputed to automatic machining machine for the automation of deep hole processing with small diameter.
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