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Machine Learning Can Raise CNC Machining Efficiency Levels

Artificial Intelligence (AI) and Machine learning (ML) represent an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems.

Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts.

In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In a recently published paper, applications of machine learning and artificial intelligence systems in CNC machine tools are reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes.

CNC machining operation is one of the most important part-production methodologies, and it is often referred to as the engine of modern manufacturing processes. The automotive and medical sectors, aerospace, gas and oil are using the CNC machining operations to create parts for different applications. CNC machining is generally used in the manufacture of every machine, molded part, or finished product as one of the most important manufacturing processes. CNC machinery has paved the way in manufacturing and machining, allowing businesses to achieve their goals and targets in a variety of ways. However, because manufacturing methodologies is always evolving and new technologies are being introduced, it is critical to consider future of CNC machining operations. Machine learning (ML) is the study of computer algorithms that gives computers the capacity to automatically learn from data and prior experiences in order to find patterns and make predictions without human involvement.

Machine learning and artificial intelligence in particular raise plenty of concerns about the future of CNC machining operations and how these concepts will evolve future works of manufacturing companies. The way a machine learns, adapts, and optimizes output can also be influenced by real-time data, analytics, and deep learning. Data sets are essential for operators to understand how a machine works and, eventually, how a whole floor of machines works together. Due to the development of affordable, reliable, and resilient sensors and acquisition and communication systems, novel implementations of machine learning approaches for tool condition monitoring can be presented. Machine learning systems are capable of completely examining data and identify various types of areas which should be modified.

Machine tools are increasingly being equipped with edge computing options to record internal drive signals at high frequency in order to supply the necessary vast quantity of data for the use of machine learning techniques in manufacturing. Productivity and efficiency are two areas where artificial intelligence can modify CNC machine tools operations in order to enhance accuracy of CNC machining operations. Machines can generate and analyze production data and provide real-time findings and effective devices for increasing productivity in part production processes. As a result, shop owners can quickly adjust the way a machine operates using the modified data generated by advanced machine learning algorithms in order to enhance productivity of part manufacturing. Having more knowledge and making better decisions in process planning strategies means less downtime on the work floor during process of part production. Production and maintenance process of part manufacturing using CNC machine tools can be developed using the machine learning and the artificial intelligence in order to enhance efficiency in part manufacturing operations.

Artificial intelligence can forecast periods of servicing of CNC machine tools structures by linking to production data such as machine performance and tool life. Data from AI will also indicate how long a machine can operate before it requires maintenance. So, the predictive data of the AI implies fewer tool failures, longer tool life, reduced downtime, and machining time which can lead to money savings in part production.