Among the monitoring technologies in the metal-cutting process, tool wear is the most critical monitoring factor in real machining sites. Extensive studies have been conducted to monitor equipment breakdown in real-time. For example, tool wear prediction studies using cutting force signals and deducting force coefficient values from the cutting process. However, due to many limitations, those wearable monitoring technologies have not been directly adopted in the field. This paper proposes a novel tool wear predictor using the cutting force coefficient with various cutting tools, and its validity evaluates through cutting tests. Tool wear prediction from the cutting force coefficient should conduct in real-time for adoption in real machining sites. Therefore, a real-time calculation algorithm of the cutting force coefficient and a tool wear estimation method proposes, and they compare with actual tool wear in cutting experiments for validation. Validation cutting tests are conducted with carbon steel and titanium, the most commonly used materials in real cutting sites. In future work, validation will be conducted with different materials and cutting tools, considering the application in real machining sites.

The cutting force signal has traditionally served as a reference in conducting the monitoring studies using a variety of sensors to identify the cutting phenomena. There have been continuing studies on how to monitor the cutting force indirectly. It is because it is easier to access when considering an application to the actual machining site. This paper discusses a method of indirectly monitoring the cutting force using the feed drive current to analyze the change in the trend of the cutting force over the lapse of machining time. This enables the analysis of the cutting force by separating it in the X and Y axes of the machining plane. To increase the discrimination of the signal related to the actual cutting phenomenon from the feed drive current signal, a bandpass filter was applied based on the tooth passing frequency. The relationship between the feed drive current and the cutting force analyzed from the machining signal of actual machining conditions was applied to convert the feed drive current into the cutting force. It has been verified through experiments that the cutting load can be estimated with markedly high accuracy as a physical quantity of force from the feed motor current.

Machine tools are one of the energy-intensive equipment used in the manufacturing industry. The importance of energy has increased and the machine tools are required to be energy-efficient. The servo systems of the machine tool consume electrical power to rotate a spindle and to feed a tool during machining. Servo system consumes a lot of energy when the machine tool is operated. The energy consumption pattern of each axis needs to be investigated in order to optimize the machining process with regard to energy cost. In this paper, an energy monitoring system is developed considering various measuring points of servo system in order to grasp the energy consumption pattern of each axis.