Abstract

     Artificial Intelligence (AI) and Machine learning (ML) represents 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 this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is 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. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.

Introduction

     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, and warehousing services, are using the CNC machining operations to create parts in different applications [1]. CNC machining is generally used in 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 [2, 3]. 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. The ML and applications in different areas of study are considered to be a component of artificial intelligence [4], [5], [6].

     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 [7]. 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 [8, 9]. 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 [10]. Machine learning systems are capable of completely examine 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 [11]. Productivity and efficiency are two areas where artificial intelligence can modify CNC machine tools operations in order to enhance accuracy of CNC machining operations[12]. Machines can generate and analyze production data and provide real-time findings to human operators are 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 [13]. 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 [14].

Conclusion

     Machine learning and artificial intelligence are applied to various industrial applications in order to improve performances of industrial process. To increase accuracy as well as efficiency during CNC machining operations, different applications of the machine learning and artificial intelligence systems are studied in different research works. Reducing machine downtime, optimization of CNC machine tools, cutting tool wear prediction, cutting force model, CNC machine tool maintenance, monitoring of machining operations, surface quality prediction and energy prediction systems are some examples of machine leniting applications in the development of CNC machining operations. ML techniques are recently applied to energy consumption prediction models in order to decrease the energy consumption during CNC machining operations. Accuracy and radiality of energy consumption models are significantly enhanced using the ML methodologies in comparison to the tradition’s methods of energy usage predictions during CNC machining operations.

     The applications of machine learning and artificial intelligence systems in CNC machine tools are examined in this study by analyzing recent achievements from published papers. The main aim of this study is to provide an overview of current studies on machine learning and artificial intelligence techniques in CNC machining operations in order to provide a useful study for the researchers in the interesting field. Network of sensors and cloud data sources can connect CNC machines together can be employed to provide smart CNC machine tools. The machining industry’s efficiency can be increased as it transitions to smart machining techniques, allowing it to achieve self-optimization and adaption to uncontrolled circumstances. However, developing the applications of advanced machine learning systems in CNC machining operations as the combination of physical, computers, and networking process created challenges and difficulties regarding the safety and security of the web of data. In order to provide secure and advanced connections between the different CNC machine tool, the security of networks should be enhanced.