A novel framework for diagnosing automatic tool changer and tool life based on cloud computing

Abstract

Tool change is one among the most frequently performed machining processes, and if there is improper percussion as the tool's position is changed, the spindle bearing can be damaged. A spindle malfunction can cause problems, such as a knife being dropped or bias in a machined hole. The measures currently taken to avoid such issues, which arose from the available machine tools, only involve determining whether the clapping knife's state is correct using a spindle and the air adhesion method, which is also used to satisfy the high precision required from mechanical components. Therefore, it cannot be used with any type of machine tool; in addition, improper tapping of the spindle during an automatic tool change cannot be detected. Therefore, this study proposes a new type of diagnostic framework that combines cloud computing and vibration sensors, among of which, tool change is automatically diagnosed using an architecture to identify abnormalities and thereby enhances the reliability and productivity of the machine and equipment.