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A Review of Machine Learning and Artificial Intelligence Applications in CNC Machine Tools

A Review of Machine Learning and Artificial Intelligence Applications in CNC Machine Tools

By: CapableMaching

Preface:

In recent years, the fusion of machine learning and artificial intelligence (AI) with Computer Numerical Control (CNC) machine tools has revolutionized the manufacturing landscape. This amalgamation of cutting-edge technologies has unlocked unprecedented possibilities, enhancing efficiency, precision, and adaptability in manufacturing processes.

This review delves into the dynamic intersection of machine learning, AI, and CNC machine tools, exploring the synergistic relationship between these domains and their transformative impact on modern manufacturing. From predictive maintenance and adaptive control to process optimization and defect detection, the applications of AI and machine learning in CNC machining are diverse and profound.

As we embark on this exploration, it’s essential to acknowledge the contributions of researchers, engineers, and innovators who have tirelessly pushed the boundaries of technology, paving the way for groundbreaking advancements in manufacturing. Their dedication and ingenuity have propelled us into an era where machines are not merely tools but intelligent collaborators, capable of learning, adapting, and evolving.

This review aims to provide a comprehensive overview of the current state-of-the-art, shedding light on emerging trends, challenges, and future directions in the realm of AI and machine learning in CNC machine tools. Whether you’re a seasoned industry professional, an aspiring researcher, or simply curious about the transformative power of technology, I hope this review serves as a valuable resource and inspires further exploration into the exciting frontier of smart manufacturing.

Introduction

The Future of Synergistic Production

In the intricate web of modern manufacturing, CNC machining stands out as a pivotal force driving progress across diverse sectors such as automotive, medical, aerospace, and beyond [1]. Yet, as the relentless march of technology pushes boundaries ever forward, it’s incumbent upon us to peer into the horizon of CNC operations [2], [3]. This journey forward is deeply entwined with the realms of Machine Learning (ML) and Artificial Intelligence (AI) [4]. The potential implications of integrating ML and AI into CNC machining operations are profound, promising transformative advancements that could redefine the very essence of manufacturing processes [5].

ML, an offspring of AI, empowers machines to autonomously learn from vast datasets and prior experiences, thereby revolutionizing the modus operandi of CNC systems [6], [7]. By leveraging advanced sensors and real-time data analytics, ML-driven insights into tool conditions are unlocking new dimensions of productivity and efficiency within CNC machining [8]. Through predictive analytics, AI holds the key to foreseeing maintenance needs, thus minimizing downtime and fine-tuning machining processes to optimal efficiency levels [9], [10].

ML & ai, cnc machining

Moreover, the emergence of deep learning is amplifying the capabilities of CNC monitoring systems, enabling sophisticated defect detection mechanisms and fine-tuned process optimization strategies [11]. Research efforts are underway to explore the multifaceted applications of ML and AI across a gamut of industrial challenges, shedding light on avenues for sustainable manufacturing and the evolution towards smarter production processes [12], [13].

A significant body of work, spearheaded by researchers such as Soori et al., delves into the digital frontiers of machining, welding, and milling processes [13]–[22]. Their endeavors are laser-focused on honing machining techniques, optimizing tool life, and elevating surface integrity standards [23]–[25]. Concurrently, the pioneering efforts of Dastres and colleagues traverse the landscapes of RFID systems, decision support mechanisms, and the expansive spectrum of AI applications, spotlighting the myriad potentials of AI in shaping the future of manufacturing [26]–[30].

cnc machining parts

As we reflect upon these strides, it becomes abundantly clear that AI holds the proverbial key to unlocking unprecedented productivity gains within the realm of CNC machining [31]. Through relentless pursuit of research and innovation, the fusion of AI with CNC operations holds the promise of not just incremental improvements, but a fundamental reshaping of the manufacturing landscape as we know it.

Review Methodology in Data Extraction

The study conducts a comprehensive review of various ML and AI applications within CNC machining operations, analyzing their impact on output quality. Focus areas include reducing machine downtime, optimizing CNC machine tools, predicting cutting tool wear, modeling cutting forces, maintenance strategies, monitoring machining operations, surface quality prediction, and energy prediction systems. By assessing both the challenges and advantages of these methods in enhancing CNC machining productivity, the review aims to elucidate gaps in existing research. Additionally, future research directions are proposed to further develop the applications of ML and AI in boosting CNC machining efficiency.

Decreasing Machine Tool Downtime

Equipment failures are common in shipping and industrial sectors, wreaking havoc on production schedules and capacity management [31], [32]. Recent advancements in predictive maintenance, driven by data-driven approaches, aim to enhance safety, reliability, and decision-making across various industries [33].

Factors such as poor maintenance, part failures, and shift changes can lead to machining downtime, hampering part production efficiency [34]. To mitigate this, sensors monitor standard CNC drill, lathe, and mill components, predicting machine tool part failures and extending their lifespan [35]. Sensor-assisted planned downtime enables precise maintenance scheduling, prolonging CNC machine tool components’ working life [36]. ML and AI interpret data, assisting manufacturers in scheduling optimal downtime, thus maximizing efficiency [37]. This efficient maintenance approach, facilitated by ML and AI in CNC machining operations, saves time, money, and resources.

CNC Machine Tool Optimization

Optimizing machining operations is increasingly vital in the era of burgeoning data and complex models [38]. Incremental optimization is essential across manufacturing, from supply chains to finished products. Optimizing CNC machine tool operations is pivotal for cost savings and increased profitability, resulting in enhanced productivity and fewer component defects [39]. Motion system kinematics are employed to generate optimal motion-cueing algorithms, improving simulator performance [40]. Optimization processes for machine tool performance and CNC machining parameters are crucial to enhancing accuracy and efficiency in component manufacturing [41]. Utilizing online data, AI and ML automate optimization, thereby enhancing machined component accuracy and part manufacturing productivity [42]. Fig. 1 demonstrates the application of a Multi-Objective evolutionary algorithm during CNC machining operations [43].

ML & ai, cnc machining

Machine learning aids in improving parallel metaheuristics in CNC machining operations, enhancing efficiency during part production [44]. ML’s application optimizes CNC machine tools to stabilize component production and mitigate unexpected failures [45]. Response surface methods and ML optimize cutting settings for turning Ti-6Al-4V [46]. The Nelder–Mead simplex method, employing machine learning, optimizes machining variables in end milling operations [47]. The integration of ML and AI into CNC machining operations enhances productivity by deriving optimized machining parameters tailored to flexible conditions and workpiece parameters.

Predicting Cutting Tool Wear

Machine learning-based technologies offer advanced tools for predicting tool wear, adept at handling complex processes [48]. Artificial Neural Networks (ANNs) excel in evaluating tool wear due to its nonlinear nature [48]. Modern sensors and computational intelligence facilitate tool condition monitoring, critical for enhancing tool lifespan during machining operations. The need for intelligent autonomous machining systems has spurred the development of cutting tool health monitoring [49]. Tool condition monitoring strategies typically fall into two categories: ‘Offline/Direct methods’ and ‘Online/Indirect methods [50], [51]. Adaptive neuro-fuzzy inference systems and deep learning techniques, illustrated in Figs. 2 and 3, respectively, enable accurate tool wear monitoring and prediction [52]–.[54].

AI and CNC machining

Fig 2

Fig 3

Utilizing ML, optimization algorithms, and sound wave signals, researchers maximize cutting tool life during drilling operations [55], [56]. ML-based approaches enhance tool wear prediction accuracy, utilizing acoustic emission signals and vibration-based networks [57], [58]. Moreover, ML models optimize cutting conditions, taking into account tool wear progression and material properties [58], [59]. By integrating ML and AI into CNC machining operations, accurate tool wear prediction systems can enhance machining efficiency, ensuring component quality while minimizing downtime.

Cutting Force Prediction

Cutting force significantly impacts milling productivity and quality, with ML systems accurately predicting it [60]. A hybrid approach using machine learning simultaneously models cutting force in milling operations [61]. Various ML algorithms, including support vector regression, k-nearest neighbor, polynomial regression, and random forest, estimate cutting forces in milling operations [62]. In high-speed turning, ML predicts cutting force, surface roughness, and tool life to provide prediction models [63]. A hybrid technique combines ML with linear regression to estimate cutting forces considering tool wear conditions [64]. Wavelet packet transform analysis removes noise in cutting force data for surface texture assessment in CNC turning [65]. A neuro-physical learning approach improves prediction accuracy in varied cutting situations [66]. ML-calibrated smart tool holders measure cutting force, enhancing accuracy [67]. Real-time measurements and CNNs categorize online tool wear during dry machining [68]. Neural networks analyze signal spectra to determine cutting tool damage during machining [69]. ML and AI applications develop accurate and flexible cutting force models for various CNC machining conditions.

CNC Machine Tool Maintenance

Accurately predicting CNC machine tool maintenance needs saves time and money [70]. ML and AI advance prediction and preventative maintenance, minimizing downtime and enhancing productivity [71]. ML accurately predicts optimal repair times, minimizing maintenance costs [72]. Real-time data feedback alerts operators to maintenance needs, ensuring stable workflow [73]. Hybrid predictive maintenance driven by digital twin technology offers accurate predictions [74]. ML evaluates maintenance operations, including tool wear monitoring. ML-driven data monitoring assesses CNC machine tool conditions [75]. Tool health monitoring enhances efficiency in end milling [76]. ML applications optimize CNC machine tool maintenance, ensuring stable production processes.

Machining Operations Monitoring

ML and AI enhance efficiency in monitoring CNC machine tools, ensuring safe and reliable operations [77]. Condition monitoring systems are essential for CNC machine tool maintenance and safety [78]. A cyber-physical structure provides smart monitoring for CNC cutting tools [79]. Advanced decision-making applications monitor CNC machine tool performance. ML studies the impact of process parameters on outputs in turn-milling operations [80]. Sensor integration enhances Stewart structure movement accuracy [81]. Adaptive neuro-fuzzy integration detects and prevents cutting tool errors [82]. Virtual reality and digitized twin systems monitor machining processes [83]. Online monitoring optimizes CNC milling operations [84]. ML predicts chatter in high-speed milling of tough materials [85]. Tool wear prediction systems based on ML techniques enhance cutting tool life [86]. ML-driven optimization methods improve CNC machining parameter selection [87]. AI applications enhance machine tool monitoring accuracy [88]. ML and AI improve machining data monitoring and decision-making systems.

Surface Quality Prediction

Surface roughness is crucial for assessing product quality, with ML predicting it accurately [89]. Neural networks predict surface finish in machined components [90]. ML algorithms predict surface quality, including linear regression and random forest [91]. Data-driven approaches predict machining accuracy and surface quality [92]. Deep learning predicts surface roughness using vibration signal analysis [93]. ML enhances surface roughness prediction accuracy, optimizing machining processes [94]. Neural networks predict surface roughness in aluminum alloy machining [95]. ML analyzes cutting forces in helical ball end milling, improving accuracy [96]. On-machine surface roughness measuring systems ensure precise production [97]. Sensory milling machine tools monitor surface roughness in real-time, enhancing quality [98]. Hybrid ML methods predict cutting tool conditions, improving tool life [99]. Deep learning detects tool wear, prolonging tool life [100]. ML models predict surface roughness using cutting forces and tool oscillations [101]. ML and AI applications enhance surface quality prediction in CNC machining, boosting productivity.

Energy Prediction Systems

ML techniques improve energy consumption prediction models during machining operations [102]. Deep learning embedded semi-supervised learning predicts energy consumption accurately [103]. AI and ML-based energy management systems enhance prediction accuracy [104]. Accurate energy consumption forecasts aid lean management of CNC machine tool energy consumption [105]. Deep learning-based methods predict machining energy usage efficiently [106]. Hybrid methodologies estimate specific cutting power during CNC machining [107]. Data-driven simulation predicts energy consumption in five-axis process planning operations [108]. ML predicts machine tool spindle energy usage, improving fault diagnosis [109]. Integrated process planning and parameter optimization minimize power consumption during CNC machining [110]. Multi-objective optimization considers energy consumption for high-quality CNC lathe machining [111]. ML and AI applications optimize energy usage prediction, enhancing productivity.

ai and cnc machining

Conclusion

ML and AI revolutionize industrial processes, enhancing CNC machining operations’ efficiency and accuracy. ML’s applications, including reducing downtime, optimizing tools, predicting tool wear, and monitoring operations, enhance CNC machining productivity. ML predicts energy consumption during machining, vital for sustainable manufacturing. ML and AI advancements in CNC machining improve part quality and reduce waste, enhancing lean production. Future research should focus on virtual machining, AI-driven decision-making, and smart manufacturing systems. Security enhancements in CNC machine tool networks are essential for safe operation.

Future Research Directions

Advanced data collection and ML techniques improve part production accuracy. Virtual machining systems enhance CNC machine tool simulation. ML-driven modifications optimize cutting tool paths and work-holding fixtures. Deep learning networks increase ML effectiveness in CNC machining. Spatial iterative learning control improves machining accuracy. Optimized collision detection systems and operation training enhance CNC machining. ML and AI applications make industrial robots smarter and more collaborative. Automation improves part production efficiency. ML-based cloud manufacturing and cyber-physical systems enhance CNC machining. AI-driven decision-making and fuzzy logic improve machining processes. These future directions will enhance part manufacturing productivity using CNC machining operations.

References:

  • [1] A. Keller, A. Kamath, U. Perera Reliability analysis of CNC machine tools Reliab. Eng., 3 (1982), pp. 449-473
  • [2] A.M. Mamadjanov, S.M. Yusupov, S. Sadirov Advantages and the future of CNC machines Sci. Progr., 2 (2021), pp. 1638-1647
  • [3] Y. Ye, T. Hu, C. Zhang, W. Luo Design and development of a CNC machining process knowledge base using cloud technology Int. J. Adv. Manuf. Technol., 94 (2018), pp. 3413-3425
  • [4] G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, L. Zdeborová Machine learning and the physical sciences Rev. Mod. Phys., 91 (2019), Article 045002
  • [5] L.C. Moreira, W. Li, X. Lu, M.E. Fitzpatrick Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence Comput. Ind. Eng., 127 (2019), pp. 158-168
  • [6] D.-.H. Kim, T.J. Kim, X. Wang, M. Kim, Y.-.J. Quan, J.W. Oh, S.-.H. Min, H. Kim, B. Bhandari, I. Yang Smart machining process using machine learning: a review and perspective on machining industry Int. J. Precis. Eng. Manuf.-Green Technol., 5 (2018), pp. 555-568
  • [7] Z.M. Çınar, A. Abdussalam Nuhu, Q. Zeeshan, O. Korhan, M. Asmael, B. Safaei Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0
  • [8] P. Krishnakumar, K. Rameshkumar, K. Ramachandran Acoustic emission-based tool condition classification in a precision high-speed machining of titanium alloy: a machine learning approach
  • [9] S. Wan, D. Li, J. Gao, J. Li A knowledge based machine tool maintenance planning system using case-based reasoning techniques
  • [10] Z. Hussain, H. Jan Establishing simulation model for optimizing efficiency of CNC machine using reliability-centered maintenance approach Int. J. Model., Simul., Sci. Comput., 10 (2019), Article 1950034
  • [11] V. Nasir, F. Sassani A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges Int. J. Adv. Manuf. Technol., 115 (2021), pp. 2683-2709
  • [12] M. Bertolini, D. Mezzogori, M. Neroni, F. Zammori Machine Learning for industrial applications: a comprehensive literature review Expert Syst. Appl., 175 (2021), Article 114820
  • [13] J. Wang, Y. Ma, L. Zhang, R.X. Gao, D. Wu Deep learning for smart manufacturing: methods and applications J. Manuf. Syst., 48 (2018), pp. 144-156
  • [14] M. Soori, B. Arezoo, M. Habibi Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system Int. J. Comput. Appl. Technol., 55 (2017), pp. 308-321
  • [15] M. Soori, B. Arezoo, M. Habibi Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines J. Manuf. Syst., 33 (2014), pp. 498-507
  • [16] M. Soori, B. Arezoo, M. Habibi Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system Comput. Aided Des., 45 (2013), pp. 1306-1313
  • [17] M. Soori, B. Arezoo, M. Habibi Tool deflection error of three-axis computer numerical control milling machines, monitoring and minimizing by a virtual machining system J. Manuf. Sci. Eng., 138 (2016)
  • [18] M. Soori, M. Asmael, D. Solyalı Recent development in friction stir welding process: a review SAE Int. J. Mater. Manuf. (2020), p. 18
  • [19] M. Soori, M. Asmael Virtual minimization of residual stress and deflection error in five-axis milling of turbine blades Strojniski Vestnik/J. Mech. Eng., 67 (2021), pp. 235-244
  • [20] M. Soori, M. Asmael Cutting temperatures in milling operations of difficult-to-cut materials J. New Technol. Mater., 11 (2021), pp. 47-56
  • [21] M. Soori, M. Asmael, A. Khan, N. Farouk Minimization of surface roughness in 5-axis milling of turbine blades Mech. Based Des. Struct. Mach. (2021), pp. 1-18
  • [22] M. Soori, M. Asmael Minimization of deflection error in five axis milling of impeller blades Facta Universitatis, Ser.: Mech. Eng. (2021)
  • [23] M. Soori, B. Arezoo A review in machining-induced residual stress J. New Technol. Mater., 12 (2022), pp. 64-83
  • [24] M. Soori, B. Arezoo Minimization of surface roughness and residual stress in grinding operations of inconel 718 J. Mater. Eng. Perform. (2022), pp. 1-10
  • [25] M. Soori, B. Arezoo Cutting tool wear prediction in machining operations, a review J. New Technol. Mater., 12 (2022), pp. 15-26
  • [26] R. Dastres, M. Soori, M. Asmael Radio Frequency Identification (RFID) based wireless manufacturing systems, a review Independent J. Manage. Prod., 13 (2022), pp. 258-290
  • [27] R. Dastres, M. Soori Advances in web-based decision support systems Int. J. Eng. Future Technol., 19 (2021), pp. 1-15
  • [28] R. Dastres, M. Soori Artificial neural network systems Int. J. Imaging Robot. (IJIR), 21 (2021), pp. 13-25
  • [29] R. Dastres, M. Soori The role of Information and Communication Technology (ICT) in environmental protection Int. J. Tomogr. Simul., 35 (2021), pp. 24-37
  • [30] R. Dastres, M. Soori Secure socket layer in the network and web security Int. J. Comput. Inf. Eng., 14 (2020), pp. 330-333
  • [31] [31]M. Zhang, A. Matta Models and algorithms for throughput improvement problem of serial production lines via downtime reduction IISE Trans., 52 (2020), pp. 1189-1203
  • [32] [32]R. Wolniak Downtime in the automotive industry production process–cause analysis Qual. Innov. Prosperity, 23 (2019), pp. 101-118
  • [33] Y. Wen, M.F. Rahman, H. Xu, T.-L.B. Tseng Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective Measurement, 187 (2022), Article 110276
  • [34] P. Ong, W.K. Lee, R.J.H. Lau Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision Int. J. Adv. Manuf. Technol., 104 (2019), pp. 1369-1379
  • [35] N. Ahmed, A. Day, J. Victory, L. Zeall, B. Young Condition monitoring in the management of maintenance in a large scale precision CNC machining manufacturing facility 2012 IEEE International Conference on Condition Monitoring and Diagnosis, IEEE (2012), pp. 842-845
  • [36] P. BA, S. PN, S. PM Shop floor to cloud connect for live monitoring the production data of CNC machines Int. J. Comput. Integr. Manuf., 33 (2020), pp. 142-158
  • [37] K. Vijayakumar Computational Intelligence, Machine Learning techniques, and IOT SAGE Publications Sage UK, London, England (2021), pp. 3-5
  • [38] S. Sun, Z. Cao, H. Zhu, J. Zhao A survey of optimization methods from a machine learning perspective IEEE Trans. Cybern., 50 (2019), pp. 3668-3681
  • [39] Y. Wu, N. Yue, K. Qian Performance optimization of CNC machine tool system based on sensor data Sci. Program. (2022), p. 2022
  • [40] M. Aminzadeh, A. Mahmoodi, M. Sabzehparvar Optimal motion-cueing algorithm using motion system kinematics Eur. J. Control, 18 (2012), pp. 363-375
  • [41] N. Sharma, V. Chawla, N. Ram Comparison of machine learning algorithms for the automatic programming of computer numerical control machine Int. J. Data Network Sci., 4 (2020), pp. 1-14
  • [42] M.-.A. Dittrich, F. Uhlich, B. Denkena Self-optimizing tool path generation for 5-axis machining processesCIRP J. Manuf. Sci. Technol., 24 (2019), pp. 49-54
  • [43] T. Ghosh, K. Martinsen Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms Eng. Sci. Technol., Int. J., 23 (2020), pp. 650-663
  • [44] W.T. de Sousa Junior, J.A.B. Montevechi, R. de Carvalho Miranda, M.L.M. de Oliveira, A.T. Campos Shop floor simulation optimization using machine learning to improve parallel metaheuristics Expert Syst. Appl., 150 (2020), Article 113272
  • [45] W. Luo, T. Hu, C. Zhang, Y. Wei Digital twin for CNC machine tool: modeling and using strategy J. Ambient Intell. Humaniz. Comput., 10 (2019), pp. 1129-1140
  • [46] M.S. Surya, G. Prasanthi, A.K. Kumar, V. Sridhar, S. Gugulothu Optimization of cutting parameters while turning Ti-6Al-4V using response surface methodology and machine learning technique Int. J. Interact. Des. Manuf. (IJIDeM), 15 (2021), pp. 453-462
  • [47] Y. Lee, A. Resiga, S. Yi, C. Wern The optimization of machining parameters for milling operations by using the Nelder–Mead simplex method J. Manuf. Mater. Process., 4 (2020), p. 66
  • [48] D.Y. Pimenov, A. Bustillo, S. Wojciechowski, V.S. Sharma, M.K. Gupta, M. Kuntoğlu Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review J. Intell. Manuf. (2022), pp. 1-43
  • [49] S. Ravikumar, K. Ramachandran Tool wear monitoring of multipoint cutting tool using sound signal features signals with machine learning techniques Mater. Today: Proc., 5 (2018), pp. 25720-25729
  • [50] V. Parwal, B. Rout Machine learning based approach for process supervision to predict tool wear during machining Procedia CIRP, 98 (2021), pp. 133-138
  • [51] P.J. Bagga, M.A. Makhesana, A.D. Pala, K.C. Chauhan, K.M. Patel, A novel computer vision based machine learning approach for online tool wear monitoring in machining, (2021).
  • [52] M. Rizal, J.A. Ghani, M.Z. Nuawi, C.H.C. Haron Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system Appl. Soft Comput., 13 (2013), pp. 1960-1968
  • [53] X. Zhang, C. Han, M. Luo, D. Zhang Tool wear monitoring for complex part milling based on deep learning Appl. Sci., 10 (2020), p. 6916
  • [54] X. Wu, Y. Liu, X. Zhou, A. Mou Automatic identification of tool wear based on convolutional neural network in face milling process Sensors, 19 (2019), p. 3817
  • [55] L.H. Saw, L.W. Ho, M.C. Yew, F. Yusof, N.A. Pambudi, T.C. Ng, M.K. Yew Sensitivity analysis of drill wear and optimization using adaptive neuro fuzzy–genetic algorithm technique toward sustainable machining J. Clean. Prod., 172 (2018), pp. 3289-3298
  • [56] A. Kothuru, S.P. Nooka, R. Liu Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling Int. J. Adv. Manuf. Technol., 95 (2018), pp. 3797-3808
  • [57] A.D. Patange, R. Jegadeeshwaran A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC) Measurement, 173 (2021), Article 108649
  • [58] R. Liu, A. Kothuru, S. Zhang Calibration-based tool condition monitoring for repetitive machining operations J. Manuf. Syst., 54 (2020), pp. 285-293
  • [59] A. Bustillo, D.Y. Pimenov, M. Mia, W. Kapłonek Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth J. Intell. Manuf., 32 (2021), pp. 895-912
  • [60] V.F. Sousa, F.J. Silva, J.S. Fecheira, H.M. Lopes, R.P. Martinho, R.B. Casais, L.P. Ferreira Cutting forces assessment in CNC machining processes: a critical review Sensors, 20 (2020), p. 4536
  • [61] S. Vaishnav, A. Agarwal, K. Desai Machine learning-based instantaneous cutting force model for end milling operation J. Intell. Manuf., 31 (2020), pp. 1353-1366
  • [62] P. Charalampous Prediction of cutting forces in milling using machine learning algorithms and finite element analysis J. Mater. Eng. Perform., 30 (2021), pp. 2002-2013
  • [63] Y. Zhang, X. Xu Machine learning cutting force, surface roughness, and tool life in high speed turning processes Manuf. Lett., 29 (2021), pp. 84-89
  • [64] B. Peng, T. Bergs, D. Schraknepper, F. Klocke, B. Döbbeler A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear Procedia CIRP, 82 (2019), pp. 302-307
  • [65] E.G. Plaza, P.N. López Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning Mech. Syst. Signal Process., 98 (2018), pp. 634-651
  • [66] K. Xu, Y. Li, J. Zhang, G. Chen ForceNet: an offline cutting force prediction model based on neuro-physical learning approach J. Manuf. Syst., 61 (2021), pp. 1-15
  • [67] L.-.W. Tseng, T.-.S. Hu, Y.-.C. Hu A smart tool holder calibrated by machine learning for measuring cutting force in fine turning and its application to the specific cutting force of low carbon steel S15C Machines, 9 (2021), p. 190
  • [68] G. Terrazas, G. Martínez-Arellano, P. Benardos, S. Ratchev Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach J. Manuf. Mater. Process., 2 (2018), p. 72
  • [69] G. Kucukyildiz, H.G. Demir A multistage cutting tool fault diagnosis algorithm for the involute form cutter using cutting force and vibration signals spectrum imaging and convolutional neural networks Arab. J. Sci. Eng., 46 (2021), pp. 11819-11833
  • [70] A. Jimenez-Cortadi, I. Irigoien, F. Boto, B. Sierra, G. Rodriguez Predictive maintenance on the machining process and machine tool Appl. Sci., 10 (2019), p. 224
  • [71] B. Luo, H. Wang, H. Liu, B. Li, F. Peng Early fault detection of machine tools based on deep learning and dynamic identification IEEE Trans. Ind. Electron., 66 (2018), pp. 509-518
  • [72] E. Traini, G. Bruno, G. D’antonio, F. Lombardi Machine learning framework for predictive maintenance in milling IFAC-PapersOnLine, 52 (2019), pp. 177-182
  • [73] J. Diaz-Rozo, C. Bielza, P. Larrañaga Machine learning-based CPS for clustering high throughput machining cycle conditions Procedia Manuf., 10 (2017), pp. 997-1008
  • [74] W. Luo, T. Hu, Y. Ye, C. Zhang, Y. Wei A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin Robot Comput. Integr. Manuf., 65 (2020), Article 101974
  • [75] J. Moore, J. Stammers, J. Dominguez-Caballero The application of machine learning to sensor signals for machine tool and process health assessment Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 235 (2021), pp. 1543-1557
  • [76] T. Mohanraj, J. Yerchuru, H. Krishnan, R.N. Aravind, R. Yameni Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms Measurement, 173 (2021), Article 108671
  • [77] A.D. Patange, R. Jegadeeshwaran Review on tool condition classification in milling: a machine learning approach Mater. Today: Proc., 46 (2021), pp. 1106-1115
  • [78] D. Goyal, C. Mongia, S. Sehgal Applications of digital signal processing in monitoring machining processes and rotary components: a review IEEE Sens. J., 21 (2021), pp. 8780-8804
  • [79] K. Zhu, Y. Zhang A cyber-physical production system framework of smart CNC machining monitoring system IEEE/ASME Trans. Mechatron., 23 (2018), pp. 2579-2586
  • [80] C. Ratnam, K.A. Vikram, B. Ben, B. Murthy Process monitoring and effects of process parameters on responses in turn-milling operations based on SN ratio and ANOVA Measurement, 94 (2016), pp. 221-232
  • [81] S. Wang, S. To, C.F. Cheung Effect of workpiece material on surface roughness in ultraprecision raster milling Mater. Manuf. Process., 27 (2012), pp. 1022-1028
  • [82] S. Jovic, O. Anicic, M. Jovanovic Adaptive neuro-fuzzy fusion of multi-sensor data for monitoring of CNC machining Sensor Rev. (2017)
  • [83] S. Liu, S. Lu, J. Li, X. Sun, Y. Lu, J. Bao Machining process-oriented monitoring method based on digital twin via augmented reality Int. J. Adv. Manuf. Technol., 113 (2021), pp. 3491-3508
  • [84] I. Daniyan, I. Tlhabadira, O. Daramola, K. Mpofu Design and optimization of machining parameters for effective AISI P20 removal rate during milling operation Procedia CIRP, 84 (2019), pp. 861-867
  • [85] K. Zacharia, P. Krishnakumar Chatter prediction in high speed machining of titanium alloy (Ti-6Al-4V) using machine learning techniques Mater. Today: Proc., 24 (2020), pp. 350-358
  • [86] A. Gouarir, G. Martínez-Arellano, G. Terrazas, P. Benardos, S. Ratchev In-process tool wear prediction system based on machine learning techniques and force analysis Procedia CIRP, 77 (2018), pp. 501-504
  • [87] E. Hazir, T. Ozcan Response surface methodology integrated with desirability function and genetic algorithm approach for the optimization of CNC machining parameters Arab. J. Sci. Eng., 44 (2019), pp. 2795-2809
  • [88] K. Martinsen, J. Downey, I. BaturynskaHuman-machine interface for artificial neural network based machine tool process monitoring Procedia CIRP, 41 (2016), pp. 933-938
  • [89] J. Verma, P. Agrawal, L. Bajpai Turning parameter optimization for surface roughness of ASTM A242 Type-1 alloys steel by Taguchi method Int. J. Adv. Eng. Technol., 3 (2012), p. 255
  • [90] C.-.H. Chen, S.-.Y. Jeng, C.-.J. Lin Prediction and analysis of the surface roughness in CNC end milling using neural networks Appl. Sci., 12 (2021), p. 393
  • [91] W. Zhang Surface roughness prediction with machine learning J. Phys.: Conf. Ser. (2021), Article 012040
  • [92] H.-.W. Chiu, C.-.H. Lee Prediction of machining accuracy and surface quality for CNC machine tools using data driven approach Adv. Eng. Software, 114 (2017), pp. 246-257
  • [93] W.-.J. Lin, S.-.H. Lo, H.-.T. Young, C.-.L. Hung Evaluation of deep learning neural networks for surface roughness prediction using vibration signal analysis Appl. Sci., 9 (2019), p. 1462
  • [94] D.Y. Pimenov, A. Bustillo, T. Mikolajczyk Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth J. Intell. Manuf., 29 (2018), pp. 1045-1061
  • [95] N. Fang, P.S. Pai, N. Edwards Neural network modeling and prediction of surface roughness in machining aluminum alloys J. Comput. Commun., 4 (2016), pp. 1-9
  • [96] A.N. Balasubramanian, N. Yadav, A. Tiwari Analysis of cutting forces in helical ball end milling process using machine learning Mater. Today: Proc., 46 (2021), pp. 9275-9280
  • [97] W. Gao, H. Haitjema, F. Fang, R. Leach, C. Cheung, E. Savio, J.-.M. Linares On-machine and in-process surface metrology for precision manufacturing CIRP Ann., 68 (2019), pp. 843-866
  • [98] H.-.C. Möhring, S. Eschelbacher, P. Georgi Machine learning approaches for real-time monitoring and evaluation of surface roughness using a sensory milling tool Procedia CIRP, 102 (2021), pp. 264-269
  • [99] P. Wang, Z. Liu, R.X. Gao, Y. Guo Heterogeneous data-driven hybrid machine learning for tool condition prognosis CIRP Ann., 68 (2019), pp. 455-458
  • [100] X. Xu, J. Wang, B. Zhong, W. Ming, M. Chen Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism Measurement, 177 (2021), Article 109254
  • [101] Y. Chen, R. Sun, Y. Gao, J. Leopold A nested-ANN prediction model for surface roughness considering the effects of cutting forces and tool vibrations Measurement, 98 (2017), pp. 25-34
  • [102] M.K.M. Shapi, N.A. Ramli, L.J. Awalin Energy consumption prediction by using machine learning for smart building: case study in Malaysia Dev. Built Environ., 5 (2021), Article 100037
  • [103] C. Chen, Y. Liu, M. Kumar, J. Qin, Y. Ren Energy consumption modelling using deep learning embedded semi-supervised learning Comput. Ind. Eng., 135 (2019), pp. 757-765
  • [104] P.W. Khan, Y. Kim, Y.-.C. Byun, S.-.J. Lee Influencing factors evaluation of machine learning-based energy consumption prediction Energies, 14 (2021), p. 7167
  • [105] J. Cao, X. Xia, L. Wang, Z. Zhang, X. Liu A novel CNC milling energy consumption prediction method based on program parsing and parallel neural network Sustainability, 13 (2021), p. 13918
  • [106] Y. He, P. Wu, Y. Li, Y. Wang, F. Tao, Y. Wang A generic energy prediction model of machine tools using deep learning algorithms Appl. Energy, 275 (2020), Article 115402
  • [107] Z. Liu, Y. Guo A hybrid approach to integrate machine learning and process mechanics for the prediction of specific cutting energy CIRP Ann., 67 (2018), pp. 57-60
  • [108] V. Vishnu, K.G. Varghese, B. Gurumoorthy Energy prediction in process planning of five-axis machining by data-driven modelling Procedia CIRP, 93 (2020), pp. 862-867
  • [109] W.H. Choi, J. Kim, J.Y. Lee Development of fault diagnosis models based on predicting energy consumption of a machine tool spindle Procedia Manuf., 51 (2020), pp. 353-358
  • [110] L. Li, C. Li, Y. Tang, L. Li An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining J. Clean. Prod., 162 (2017), pp. 458-473
  • [111] S. Jia, S. Wang, J. Lv, W. Cai, N. Zhang, Z. Zhang, S. Bai Multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption Sustainability, 13 (2021), p. 13803

A Review of Machine Learning and Artificial Intelligence Applications in CNC Machine Tools by capablemachining is licensed under CC BY-NC 4.0

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