Currently, improvements are made to the existing cutting tool monitoring technologies overcoming the current challenges, especially in AI-Based cutting tool monitoring technologies.
FREMONT, CA: Manufacturing industries require replacing cutting tool for machining process to meets new process demands. However, to replace a cutting tool when it was dull, on-line tool monitoring was needed. Cutting tool wear is a phenomenon influencing the quality of the machined part. Cutting tool wear condition is an important technique that can be useful, especially in automated cutting processes and unmanned factories to prevent any damage to the machine tool and workpiece. It can help in on-line realization of the tool wear, breakage, and workpiece surface roughness. Currently, there are many manual and automatic processes to monitor tool wear. They perform early detection of tool wear, maintain machine accuracy by providing corrective action for tool wear and prevent cutting tool from breakage.
Some new methods are more accurate employing optical, radioactive, or electrical sensors which are implemented on CNC machines. Here are some tools that will bring highly accurate automated tool monitoring in early 2020 or next year.
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Even though a few methods are existing to measure tool wear, each has its own limitations. For example, sensors can analyze the actual geometric parameters of the cutting tool, measure surface roughness, and tooltip heat. However, it cannot be reliably measured during cutting. A recent study has shown some uncommonly measured factors like chip color and spark proliferation can predict tool wear. Researchers have used an artificial neural network (ANN) for cutting tool monitoring. It simulates the human brain to process information in the service of a clearly defined task. It is unique from other algorithms because traditionally, algorithms are defined by humans. But ANN has its own algorithms developed based on learning samples.
Traditional visual methods need expert experience and human resources to obtain an accurate tool to wear information. The onset of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become easy to use the convolutional neural network (CNN) model to automatically find the wear types of high-temperature alloy tools used in the face milling process.
