Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

The Fourth Industrial Revolution incorporates the digital revolution into the physical world, creating a new direction in a number of fields, including artificial intelligence, quantum computing, nanotechnology, biotechnology, robotics, 3D printing, autonomous vehicles, and the Internet of Things. The artificial intelligence field has encountered a turning point mainly due to advancements in machine learning, which allows machines to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and productivity rates, to monitor the health of systems, and to optimize design and process parameters. This is known as smart machining, referring to a new machining paradigm in which machine tools are fully connected through a cyber-physical system. This paper reviews and summarizes machining processes using machine learning algorithms and suggests a perspective on the machining industry.

[1]  Ponnuthurai N. Suganthan,et al.  Modeling of steelmaking process with effective machine learning techniques , 2015, Expert Syst. Appl..

[2]  Chia-Hao Kuo,et al.  A PNN self-learning tool breakage detection system in end milling operations , 2015, Appl. Soft Comput..

[3]  Tae Jo Ko,et al.  Optimal strategy to deal with decision making problems in machine tools remanufacturing , 2016 .

[4]  Ulaş Çaydaş,et al.  A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method , 2008 .

[5]  Yiorgos Makris,et al.  Spatial estimation of wafer measurement parameters using Gaussian process models , 2012, 2012 IEEE International Test Conference.

[6]  Roselina Sallehuddin,et al.  Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining , 2013 .

[7]  S. Shankar Sastry,et al.  Provably safe and robust learning-based model predictive control , 2011, Autom..

[8]  P J Antony,et al.  Machine learning models for material selection: Framework for predicting flatwise compressive strength using ANN , 2016 .

[9]  Fengjun Li,et al.  Cyber-Physical Systems Security—A Survey , 2017, IEEE Internet of Things Journal.

[10]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[11]  N. R. Sakthivel,et al.  Chatter prediction in boring process using machine learning technique , 2017, Int. J. Manuf. Res..

[12]  Jerzy Jedrzejewski,et al.  Discussion of Machine Tool Intelligence, Based on Selected Concepts and Research , 2015 .

[13]  Andreas Krause,et al.  Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.

[14]  Yong He,et al.  A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis , 2013 .

[15]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[16]  Brian Surgenor,et al.  Vision Based Fault Detection of Automated Assembly Equipment , 2011 .

[17]  Nihat Tosun,et al.  A study of tool life in hot machining using artificial neural networks and regression analysis method , 2002 .

[18]  Hua Wang,et al.  Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM , 2016 .

[19]  Jui-Pin Hung,et al.  Design analysis of machine tool structure with artificial granite material , 2016 .

[20]  Andrzej Kochanski,et al.  Knowledge Discovery and Analysis in Manufacturing , 2010 .

[21]  SchmidhuberJürgen Deep learning in neural networks , 2015 .

[22]  Sara McMains,et al.  CyberCut: An Internet-based CAD/CAM System , 2001, J. Comput. Inf. Sci. Eng..

[23]  Joaquín B. Ordieres Meré,et al.  Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm , 2014, 2014 IEEE International Conference on Industrial Engineering and Engineering Management.

[24]  Xuefeng Chen,et al.  The concept and progress of intelligent spindles: A review , 2017 .

[25]  Paul K. Wright,et al.  21st Century Manufacturing , 2000 .

[26]  K. Rameshkumar,et al.  Use of Machine Learning Algorithms for Weld Quality Monitoring using Acoustic Signature , 2015 .

[27]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[28]  Sangkee Min,et al.  CAD/CAM for scalable nanomanufacturing: A network-based system for hybrid 3D printing , 2017, Microsystems & Nanoengineering.

[29]  Soumaya Yacout,et al.  Diagnosis of machining outcomes based on machine learning with Logical Analysis of Data , 2015, 2015 International Conference on Industrial Engineering and Operations Management (IEOM).

[30]  Konstantinos C. Gryllias,et al.  Rolling element bearing fault detection in industrial environments based on a K-means clustering approach , 2011, Expert Syst. Appl..

[31]  S Bergmann,et al.  Emulation of control strategies through machine learning in manufacturing simulations , 2017, J. Simulation.

[32]  K. Chiang,et al.  Optimization of the WEDM process of particle-reinforced material with multiple performance characteristics using grey relational analysis , 2006 .

[33]  James Tannock,et al.  The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling , 2005, Neural Computing & Applications.

[34]  Henning Trsek,et al.  Isochronous Wireless Network for Real-time Communication in Industrial Automation , 2016 .

[35]  Mustafa Demetgul,et al.  Fault diagnosis on production systems with support vector machine and decision trees algorithms , 2013 .

[36]  Tao Yu,et al.  Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression , 2008 .

[37]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[38]  Mohamed Elforjani,et al.  Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning , 2018, IEEE Transactions on Industrial Electronics.

[39]  Masahiko Mori,et al.  Study on Quality Improvement of Machine Tools , 2017 .

[40]  Javier García,et al.  A comprehensive survey on safe reinforcement learning , 2015, J. Mach. Learn. Res..

[41]  Dongxu Zhang,et al.  Online monitoring of precision optics grinding using acoustic emission based on support vector machine , 2015 .

[42]  Kincho H. Law,et al.  An intelligent machine monitoring system for energy prediction using a Gaussian Process regression , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[43]  T. Warren Liao,et al.  A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine , 2015 .

[44]  Enming Miao,et al.  Robustness of thermal error compensation modeling models of CNC machine tools , 2013, The International Journal of Advanced Manufacturing Technology.

[45]  Shahaboddin Shamshirband,et al.  Surface roughness prediction by extreme learning machine constructed with abrasive water jet , 2016 .

[46]  Tuğrul Özel,et al.  Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy , 2015 .

[47]  Sangkee Min,et al.  From design for manufacturing (DFM) to manufacturing for design (MFD) via hybrid manufacturing and smart factory: A review and perspective of paradigm shift , 2016 .

[48]  Gang Chen,et al.  Swarm intelligence in mechanical engineering , 2016 .

[49]  Connor Jennings,et al.  A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests , 2017 .

[50]  Nikolaos Tapoglou,et al.  Online on-board Optimization of Cutting Parameter for Energy Efficient CNC Milling☆ , 2016 .

[51]  Klaus-Dieter Thoben,et al.  An approach to monitoring quality in manufacturing using supervised machine learning on product state data , 2013, Journal of Intelligent Manufacturing.

[52]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[53]  Sung-Hoon Ahn,et al.  Hybrid manufacturing in micro/nano scale: A Review , 2014 .

[54]  Miran Brezocnik,et al.  A comparison of machine learning methods for cutting parameters prediction in high speed turning process , 2016, Journal of Intelligent Manufacturing.

[55]  Pınar Tüfekci,et al.  Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods , 2014 .

[56]  Jiann-Fuh Chen,et al.  Classification of partial discharge events in GILBS using probabilistic neural networks and the fuzzy c-means clustering approach , 2014 .

[57]  A. M. M. Sharif Ullah,et al.  Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing , 2015, Journal of Intelligent Manufacturing.

[58]  Pedro Paulo Balestrassi,et al.  Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays , 2012, Expert Syst. Appl..

[59]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[60]  Bing Xue,et al.  Sustainability aspects of a digitalized industry – A comparative study from China and Germany , 2017 .

[61]  Sudarsan Rachuri,et al.  A Generalized Data-Driven Energy Prediction Model With Uncertainty for a Milling Machine Tool Using Gaussian Process , 2015 .

[62]  Oguz Findik,et al.  A directed artificial bee colony algorithm , 2015, Appl. Soft Comput..

[63]  Sang Do Noh,et al.  Smart manufacturing: Past research, present findings, and future directions , 2016, International Journal of Precision Engineering and Manufacturing-Green Technology.

[64]  Sohyung Cho,et al.  Tool breakage detection using support vector machine learning in a milling process , 2005 .

[65]  Noureddine Zerhouni,et al.  Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..

[66]  Samir Mekid,et al.  Beyond intelligent manufacturing: A new generation of flexible intelligent NC machines , 2009 .

[67]  Matthias Finkbeiner,et al.  Addressing Sustainability and Flexibility in Manufacturing Via Smart Modular Machine Tool Frames to Support Sustainable Value Creation , 2015 .

[68]  Chaoyong Zhang,et al.  Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations , 2015 .

[69]  Meng-Shiun Tsai,et al.  Integration of an Empirical Mode Decomposition Algorithm With Iterative Learning Control for High-Precision Machining , 2013, IEEE/ASME Transactions on Mechatronics.

[70]  Pedro Paulo Balestrassi,et al.  Artificial neural networks for machining processes surface roughness modeling , 2010 .

[71]  Miran Brezocnik,et al.  Intelligent CAD/CAM System for Programming of CNC Machine Tools , 2016 .

[72]  Pieter Abbeel,et al.  Constrained Policy Optimization , 2017, ICML.

[73]  Blaine Nelson,et al.  The security of machine learning , 2010, Machine Learning.

[74]  R V Rao,et al.  Parameters optimization of advanced machining processes using TLBO algorithm , 2011 .

[75]  Rory Coulter,et al.  Intelligent agents defending for an IoT world: A review , 2018, Comput. Secur..

[76]  A. Paulo Moreira,et al.  Object recognition using laser range finder and machine learning techniques , 2013 .

[77]  Alexander Brosius,et al.  New Approaches for the Determination of Specific Values for Process Models in Machining Using Artificial Neural Networks , 2017 .

[78]  Oliver Niggemann,et al.  System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[79]  Jaime F. Fisac,et al.  Reachability-based safe learning with Gaussian processes , 2014, 53rd IEEE Conference on Decision and Control.

[80]  Hong-Seok Park,et al.  Development of a smart machining system using self-optimizing control , 2014, The International Journal of Advanced Manufacturing Technology.

[81]  Joaquim Ciurana,et al.  Modeling pulsed laser micromachining of micro geometries using machine-learning techniques , 2015, J. Intell. Manuf..

[82]  Shang Gao,et al.  Recent advances in micro- and nano-machining technologies , 2017 .

[83]  Yue Meng,et al.  Big-data-driven based intelligent prognostics scheme in industry 4.0 environment , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[84]  Roberto Teti,et al.  Online Prediction of Cutting Tool Life in Turning via Cognitive Decision Making , 2016 .

[85]  Frank L. Lewis,et al.  Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems , 2013 .

[86]  Jose Mathew,et al.  Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms , 2010 .

[87]  Edward J. Williams,et al.  Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic , 2016, J. Intell. Manuf..

[88]  Hong-Seok Park,et al.  Development of smart machining system for optimizing feedrates to minimize machining time , 2018, J. Comput. Des. Eng..

[89]  N. R. Sakthivel,et al.  Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning , 2015 .

[90]  Jeehyun Jung,et al.  Modeling and parameter optimization for cutting energy reduction in MQL milling process , 2016 .

[91]  Ananthram Swami,et al.  The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).

[92]  V. Sugumaran,et al.  Tool condition monitoring using K-star algorithm , 2014, Expert Syst. Appl..

[93]  Marzuki Khalid,et al.  Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[94]  Han Ding,et al.  Bayesian Learning-Based Model-Predictive Vibration Control for Thin-Walled Workpiece Machining Processes , 2017, IEEE/ASME Transactions on Mechatronics.

[95]  Kemal Yaman,et al.  Determination of cutting parameters in end milling operation based on the optical surface roughness measurement , 2016 .

[96]  K. I. Ramachandran,et al.  Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy , 2015 .

[97]  J. Jędrzejewski,et al.  Artificial Intelligence Tools in Diagnostics of Machine Tool Drives , 1996 .

[98]  Maite García-Ordás Wear characterization of the cutting tool in milling processes using shape and texture descriptors , 2017 .

[99]  Sadegh Rahmati,et al.  Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality , 2016, International Journal of Precision Engineering and Manufacturing.

[100]  Arindam Majumder Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters , 2015 .

[101]  Bibhuti Bhusan Biswal,et al.  A general regression neural network approach for the evaluation of compressive strength of FDM prototypes , 2014, Neural Computing and Applications.