Robotical automation in CNC machine tools: a review

• Autorzy: Soori Mohsen , Jough Fooad Karimi Ghaleh , Dastres Roza , Arezoo Behrooz

Identyfikatory

DOI

10.2478/ama-2024-0048

• Języki publikacji: EN

Abstrakty

EN

Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

Słowa kluczowe

EN

robotics; automation; CNC machine tools; automated material handling; automatic tool changin

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2024
• Tom: Vol. 18, no. 3
• Strony: 434--450
• Opis fizyczny: Bibliogr. 184 poz., rys.

Twórcy

autor

Soori Mohsen

• Department of Civil Engineering, Final International University, AS128, Kyrenia, North Cyprus, Via Mersin 10, Turkey

• https://orcid.org/0000-0002-4358-7513

autor

Jough Fooad Karimi Ghaleh

• Department of Civil Engineering, Final International University, AS128, Kyrenia, North Cyprus, Via Mersin 10, Turkey

• https://orcid.org/0000-0003-0697-516X

autor

Dastres Roza

• Department of Computer Engineering, Cyprus International University, North Cyprus, Via Mersin 10, Turkey

• https://orcid.org/0000-0002-0278-5003

autor

Arezoo Behrooz

• CAD/CAPP/CAM Research Center, Department of Mechanical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, Tehran 15875-4413, Iran

• https://orcid.org/0000-0002-3054-4696

Bibliografia

• 1. Bloss R. Machine tools become much more than just a lathe or milling machine. Assembly Automation. 2007;27(1):9-11.
• 2. Ribeiro J, Lima R, Eckhardt T, Paiva S. Robotic process automa-tion and artificial intelligence in industry 4.0–a literature review. Procedia Computer Science. 2021;181:51-8.
• 3. Bârsan A. A Brief Review of Robotic Machining. Acta Universitatis Cibiniensis Technical Series. 2019;71(1):9-13.
• 4. Yuwen S, Jinjie J, Jinting X, Mansen C, Jinbo N. Path, feedrate and trajectory planning for free-form surface machining: A state-of-the-art review. Chinese Journal of Aeronautics. 2022;35(8):12-29.
• 5. Bartoš M, Bulej V, Bohušík M, Stanček J, Ivanov V, Macek P. An overview of robot applications in automotive industry. Transporta-tion Research Procedia. 2021;55:837-44.
• 6. Martinova LI, Kozak NV, Kovalev IA, Ljubimov AB. Creation of CNC system’s components for monitoring machine tool health. The International Journal of Advanced Manufacturing Technology. 2021;117(7-8):2341-8.
• 7. Nasir V, Sassani F. A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges. The Inter-national Journal of Advanced Manufacturing Technology. 2021;115(9-10):2683-709.
• 8. Liu C, Zheng P, Xu X. Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review. International journal of production research. 2021:1-33.
• 9. Nguyen V, Johnson J, Melkote S. Active vibration suppression in robotic milling using optimal control. International Journal of Ma-chine Tools and Manufacture. 2020;152:103541.
• 10. Gienke O, Pan Z, Yuan L, Lepper T, Van Duin S. Mode coupling chatter prediction and avoidance in robotic machining process. The International Journal of Advanced Manufacturing Technology. 2019;104:2103-16.
• 11. Hao D, Wang W, Liu Z, Yun C. Experimental study of stability prediction for high-speed robotic milling of aluminum. Journal of Vibration and Control. 2020;26(7-8):387-98.
• 12. Javaid M, Haleem A, Singh RP, Suman R. Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics. 2021;1:58-75.
• 13. Onstein IF, Semeniuta O, Bjerkeng M, editors. Deburring using robot manipulators: A review. 2020 3rd International Symposium on Small-scale Intelligent Manufacturing Systems; 2020: IEEE.
• 14. Zerun Z, Xiaowei T, Chen C, Fangyu P, Rong Y, Lin Z, et al. High precision and efficiency robotic milling of complex parts: Challeng-es, approaches and trends. Chinese Journal of Aeronautics. 2022;35(2):22-46.
• 15. Zheng H, Lin J, editors. A deep learning approach for high speed machining tool wear monitoring. 2019 3rd international conference on robotics and automation sciences (ICRAS); 2019: IEEE.
• 16. Luo W, Hu T, Ye Y, Zhang C, Wei Y. A hybrid predictive mainte-nance approach for CNC machine tool driven by Digital Twin. Ro-botics and Computer-Integrated Manufacturing. 2020;65:101974.
• 17. Zhu D, Feng X, Xu X, Yang Z, Li W, Yan S, et al. Robotic grinding of complex components: a step towards efficient and intelligent machining–challenges, solutions, and applications. Robotics and Computer-Integrated Manufacturing. 2020;65:101908.
• 18. Iglesias Sánchez I, Ares JE, González Gaya C, Rosales Prieto V. A new approach to the consideration and analysis of critical factors in robotic machining. Applied Sciences. 2020;10(24):8885.
• 19. Johansen K, Ashourpour M, Rao S. Positioning sustainable auto-mation in production of customized products. Procedia Manufactur-ing. 2021;55:358-64.
• 20. Evjemo LD, Gjerstad T, Grøtli EI, Sziebig G. Trends in smart manufacturing: Role of humans and industrial robots in smart fac-tories. Current Robotics Reports. 2020;1:35-41.
• 21. Chen Q, Zhang C, Hu T, Zhou Y, Ni H, Xue X. Posture optimization in robotic machining based on comprehensive deformation index considering spindle weight and cutting force. Robotics and Com-puter-Integrated Manufacturing. 2022;74:102290.
• 22. Pantazis D, Pease SG, Goodall P, West A, Conway P. A design of experiments Cyber–Physical System for energy modelling and op-timisation in end-milling machining. Robotics and Computer-Integrated Manufacturing. 2023;80:102469.
• 23. Soori M, Arezoo B, Habibi M. Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machin-ing system. International Journal of Computer Applications in Technology. 2017;55(4):308-21.
• 24. Soori M, Arezoo B, Habibi M. Virtual machining considering dimen-sional, geometrical and tool deflection errors in three-axis CNC milling machines. Journal of Manufacturing Systems. 2014;33(4):498-507.
• 25. Soori M, Arezoo B, Habibi M. Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system. Computer-Aided Design. 2013;45(11):1306-13.
• 26. Soori M, Arezoo B, Habibi M. Tool deflection error of three-axis computer numerical control milling machines, monitoring and min-imizing by a virtual machining system. Journal of Manufacturing Science and Engineering. 2016;138(8):081005.
• 27. Soori M, Asmael M, Solyalı D. Recent Development in Friction Stir Welding Process: A Review. SAE International Journal of Materials and Manufacturing. 2020(5):18.
• 28. Soori M, Asmael M. Virtual Minimization of Residual Stress and Deflection Error in Five-Axis Milling of Turbine Blades. Strojniski Vestnik/Journal of Mechanical Engineering. 2021;67(5):235-44.
• 29. Soori M, Asmael M. Cutting temperatures in milling operations of difficult-to-cut materials. Journal of New Technology and Materials. 2021;11(1):47-56.
• 30. Soori M, Asmael M, Khan A, Farouk N. Minimization of surface roughness in 5-axis milling of turbine blades. Mechanics Based Design of Structures and Machines. 2021;51(9):1-18.
• 31. Soori M, Asmael M. MINIMIZATION OF DEFLECTION ERROR IN FIVE AXIS MILLING OF IMPELLER BLADES. Facta Universitatis, series: Mechanical Engineering. 2021;21(2):175-90.
• 32. Soori M. Virtual product development: GRIN Verlag; 2019.
• 33. Soori M, Asmael M. A Review of the Recent Development in Machining Parameter Optimization. Jordan Journal of Mechanical & Industrial Engineering. 2022;16(2):205-23.
• 34. Dastres R, Soori M, Asmael M. RADIO FREQUENCY IDENTIFI-CATION (RFID) BASED WIRELESS MANUFACTURING SYS-TEMS, A REVIEW. Independent Journal of Management & Pro-duction. 2022;13(1):258-90.
• 35. Soori M, Arezoo B, Dastres R. Machine Learning and Artificial Intelligence in CNC Machine Tools, A Review. Sustainable Manu-facturing and Service Economics. 2023:100009.
• 36. Soori M, Arezoo B. A Review in Machining-Induced Residual Stress. Journal of New Technology and Materials. 2022;12(1):64-83.
• 37. Soori M, Arezoo B. Minimization of Surface Roughness and Resid-ual Stress in Grinding Operations of Inconel 718. Journal of Mate-rials Engineering and Performance. 2022:1-10.
• 38. Soori M, Arezoo B. Cutting Tool Wear Prediction in Machining Operations, A Review. Journal of New Technology and Materials. 2022;12(2):15-26.
• 39. Soori M, Asmael M. Classification of research and applications of the computer aided process planning in manufacturing systems. Independent Journal of Management & Production. 2021;12(5):1250-81.
• 40. Dastres R, Soori M. Advances in web-based decision support systems. International Journal of Engineering and Future Technol-ogy. 2021;19(1):1-15.
• 41. Dastres R, Soori M. Artificial Neural Network Systems. Internation-al Journal of Imaging and Robotics (IJIR). 2021;21(2):13-25.
• 42. Dastres R, Soori M. The Role of Information and Communication Technology (ICT) in Environmental Protection. International Jour-nal of Tomography and Simulation. 2021;35(1):24-37.
• 43. Dastres R, Soori M. Secure Socket Layer in the Network and Web Security. International Journal of Computer and Information Engi-neering. 2020;14(10):330-3.
• 44. Dastres R, Soori M. Advances in Web-Based Decision Support Systems. International Journal of Engineering and Future Technol-ogy. 2021.
• 45. Dastres R, Soori M. A review in recent development of network threats and security measures. International Journal of Information Sciences and Computer Engineering. 2021.
• 46. Dastres R, Soori M. Advanced image processing systems. Interna-tional Journal of Imagining and Robotics. 2021;21(1):27-44.
• 47. Soori M, Arezoo B. Dimensional, geometrical, thermal and tool deflection errors compensation in 5-Axis CNC milling operations. Australian Journal of Mechanical Engineering. 2023:1-15.
• 48. Soori M, Arezoo B, Dastres R. Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review. Cognitive Robotics. 2023;3:54-70.
• 49. Soori M, Arezoo B. Effect of cutting parameters on tool life and cutting temperature in milling of AISI 1038 carbon steel. Journal of New Technology and Materials. 2023.
• 50. Soori M, Arezoo B. The effects of coolant on the cutting tempera-ture, surface roughness and tool wear in turning operations of Ti6Al4V alloy. Mechanics Based Design of Structures and Ma-chines. 2023:1-23.
• 51. Soori M. Advanced Composite Materials and Structures. Journal of Materials and Engineering Structures. 2023.
• 52. Soori M, Arezoo B, Dastres R. Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems. 2023.
• 53. Soori M, Arezoo B. Cutting tool wear minimization in drilling opera-tions of titanium alloy Ti-6Al-4V. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology. 2023:13506501231158259.
• 54. Soori M, Arezoo B. Minimization of surface roughness and residual stress in abrasive water jet cutting of titanium alloy Ti6Al4V. Pro-ceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 2023:09544089231157972.
• 55. Soori M. Deformation error compensation in 5-Axis milling opera-tions of turbine blades. Journal of the Brazilian Society of Mechani-cal Sciences and Engineering. 2023;45(6):289.
• 56. Soori M, Arezoo B. Modification of CNC Machine Tool Operations and Structures Using Finite Element Methods, A Review. Jordan Journal of Mechanical and Industrial Engineering. 2023.
• 57. Soori M, Arezoo B, Dastres R. Optimization of Energy Consump-tion in Industrial Robots, A Review. Cognitive Robotics. 2023.
• 58. Soori M, Arezoo B, Dastres R. Advanced Virtual Manufacturing Systems, A Review. Journal of Advanced Manufacturing Science and Technology. 2023.
• 59. Jough FKG, Şensoy S. Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms. Earthquake Engineering and Engineering Vibration. 2016;15: 743-57.
• 60. Karimi Ghaleh Jough F, Şensoy S. Steel moment-resisting frame reliability via the interval analysis by FCM-PSO approach consider-ing various uncertainties. Journal of Earthquake Engineering. 2020;24(1):109-28.
• 61. Karimi Ghaleh Jough F, Golhashem M. Assessment of out-of-plane behavior of non-structural masonry walls using FE simulations. Bulletin of Earthquake Engineering. 2020;18(14):6405-27.
• 62. Taher GA, Yousuf Howlader MAR, Touqir FA. Automation of material handling with bucket elevator and belt conveyor. Interna-tional Journal of Scientific and Research Publications. 2014;4(3): 1-13.
• 63. Deal WF, Jones CE. Integrating CAD/CAM in automation and materials handling. Technology and Engineering Teacher. 2012;71(6):13.
• 64. Maheswari C, Priyanka E, Thangavel S, Parameswari P. Devel-opment of unmanned guided vehicle for material handling automa-tion for industry 4.0. Int J Recent Technol Eng. 2018;7(4):428-32.
• 65. Mahdavi I, Shirazi B, Sahebjamnia N. Development of a simula-tion-based optimisation for controlling operation allocation and ma-terial handling equipment selection in FMS. International Journal of Production Research. 2011;49(23):6981-7005.
• 66. Lee S, Kim Y, Kahng H, Lee S-K, Chung S, Cheong T, et al. Intelli-gent traffic control for autonomous vehicle systems based on ma-chine learning. Expert Systems with Applications. 2020;144:113074.
• 67. Kunduru AR. Cloud BPM Application (Appian) Robotic Process Automation Capabilities. Asian Journal of Research in Computer Science. 2023;16(3):267-80.
• 68. Muñoz-Benavent P, Solanes JE, Gracia L, Tornero J. Robust auto tool change for industrial robots using visual servoing. International Journal of Systems Science. 2019;50(2):432-49.
• 69. Soori M, Arezoo B, Dastres R. Artificial Neural Networks in Supply Chain Management, A Review. Journal of Economy and Technol-ogy. 2023;1:179-96.
• 70. Javaid M, Haleem A, Singh RP, Rab S, Suman R. Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International. 2022;3:100132.
• 71. Jordaan GD, Van Nieuwenhuizen RJ, editors. Machine Vision in an Automated Component-Handling System. 2006 IEEE International Conference on Mechatronics; 2006: IEEE.
• 72. Mudiyanselage SE, Nguyen PHD, Rajabi MS, Akhavian R. Auto-mated workers’ ergonomic risk assessment in manual material handling using sEMG wearable sensors and machine learning. Electronics. 2021;10(20):2558.
• 73. Guzzi J, Abbate G, Paolillo A, Giusti A, editors. Interacting with a conveyor belt in virtual reality using pointing gestures. 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI); 2022: IEEE.
• 74. Rahman HF, Nielsen I. Scheduling automated transport vehicles for material distribution systems. Applied Soft Computing. 2019;82:105552.
• 75. Tyagi AK, Fernandez TF, Mishra S, Kumari S, editors. Intelligent automation systems at the core of industry 4.0. International con-ference on intelligent systems design and applications; 2020: Springer.
• 76. Brecher C, Schröter B, Almeida C, editors. Development and programming of portable robot systems for material handling tasks. Proceedings of the CIRP International Conference on Reconfigu-rable Manufacturing; 2005: Citeseer.
• 77. Wan J, Tang S, Hua Q, Li D, Liu C, Lloret J. Context-aware cloud robotics for material handling in cognitive industrial Internet of Things. IEEE Internet of Things Journal. 2017;5(4):2272-81.
• 78. Xie C, Allen TT. Simulation and experimental design methods for job shop scheduling with material handling: a survey. The Interna-tional Journal of Advanced Manufacturing Technology. 2015;80(1-4):233-43.
• 79. Obreja C, Stan G, Andrioaia D, Funaru M, editors. Design of an automatic tool changer system for milling machining centers. Ap-plied Mechanics and Materials; 2013: Trans Tech Publ.
• 80. Verma K, Belokar R, Verma VK, Ntalianis K. Track-based analysis for profile generation on globoidal cam in automatic tool changer of CNC machining center. Assembly Automation. 2019;39(2):369-79.
• 81. Wu X, Liu Y, Zhou X, Mou A. Automatic identification of tool wear based on convolutional neural network in face milling process. Sensors. 2019;19(18):3817.
• 82. Lianzhong Z, Li W, editors. Machining center automatic ATC analysis and research. 2010 3rd International Conference on In-formation Management, Innovation Management and Industrial Engineering; 2010: IEEE.
• 83. Leng J, Liu Q, Ye S, Jing J, Wang Y, Zhang C, et al. Digital twin-driven rapid reconfiguration of the automated manufacturing sys-tem via an open architecture model. Robotics and Computer-Integrated Manufacturing. 2020;63:101895.
• 84. Dereli T, Filiz İH. Allocating optimal index positions on tool maga-zines using genetic algorithms. Robotics and Autonomous Sys-tems. 2000;33(2-3):155-67.
• 85. Gong QS, Luo M, Ren AH, Chang ZB. Research on dynamic characteristics of globoidal cam employed in automatic tool chang-er (ATC) of machine center. Applied Mechanics and Materials. 2014;456:133-6.
• 86. Chen S-L, Su C-F, Cheng Y-T. A novel framework for diagnosing automatic tool changer and tool life based on cloud computing. Ad-vances in Mechanical Engineering. 2016;8(3):1687814016637319.
• 87. Oliveira TLL, Zitoune R, Ancelotti Jr AC, da Cunha Jr SS. Smart machining: Monitoring of CFRP milling using AE and IR. Compo-site Structures. 2020;249:112611.
• 88. Van HP, Thuy DN. Influence of relative humidity and air tempera-ture on the stopping position of the automatic tool changer in a CNC machine when using a pneumatic cylinder. International Journal of Modern Physics B. 2021;35(14n16):2140013.
• 89. Lu XH, Han PZ, Wu WY, Jie W, editors. Reliability evaluation of circular tool magazine and automatic tool changer. Advanced Ma-terials Research; 2013: Trans Tech Publ.
• 90. Nakamoto K, Shirase K, Wakamatsu H, Tsumaya A, Arai E. Auto-matic production planning system to achieve flexible direct machin-ing. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing. 2004;47(1):136-43.
• 91. Tian H, Yang Z, Li G, Chen C. Study on failure warning of tool magazine and automatic tool changer based on tool-pulling force. Journal of Mechanical Science and Technology. 2019;33:4371-81.
• 92. Zhou L, Li F, Wang Y, Wang L, Wang G. A new empirical standby power and auxiliary power model of CNC machine tools. The Inter-national Journal of Advanced Manufacturing Technology. 2022;120(5-6):3995-4010.
• 93. Chen C, Tian H, Zhang J, Shi X, Chen L, Bao J, et al. Study on failure warning of tool magazine and automatic tool changer. Jour-nal of Vibroengineering. 2016;18(2):883-99.
• 94. Barbosa M, Silva F, Pimentel C, Gouveia RM. A novel concept of CNC machining center automatic feeder. Procedia Manufacturing. 2018;17:952-9.
• 95. Živanović S, Slavković N, editors. Programming of machine tools and robots for machining using STEP-NC in the era of Industry 4.0. Proceedings of the 15th International Conference on Accomplish-ments in Mechanical and Industrial Engineering DEMI 2021; 2021: University of Banjaluka, Faculty of Mechanical Engineering.
• 96. Leali F, Pellicciari M, Pini F, Vergnano A, Berselli G, editors. A calibration method for the integrated design of finishing robotic workcells in the aerospace industry. Robotics in Smart Manufactur-ing: International Workshop, WRSM 2013, Co-located with FAIM 2013, Porto, Portugal, June 26-28, 2013 Proceedings; 2013: Springer.
• 97. Chen Y, Dong F. Robot machining: recent development and future research issues. The International Journal of Advanced Manufac-turing Technology. 2013;66:1489-97.
• 98. Gultekin H, Akturk MS, Karasan OE. Bicriteria robotic operation allocation in a flexible manufacturing cell. Computers & operations research. 2010;37(4):779-89.
• 99. Vaher K, Kangru T, Otto T, Riives J. THE MOBILITY OF ROBOT-ISED WORK CELLS IN MANUFACTURING. Annals of DAAAM & Proceedings. 2019;30.
• 100. Schneider U, Drust M, Ansaloni M, Lehmann C, Pellicciari M, Leali F, et al. Improving robotic machining accuracy through experi-mental error investigation and modular compensation. The Interna-tional Journal of Advanced Manufacturing Technology. 2016;85: 3-15.
• 101. Buerkle A, Eaton W, Lohse N, Bamber T, Ferreira P. EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration. Robotics and Computer-Integrated Manufacturing. 2021;70:102137.
• 102. Soori M, Arezoo B, Dastres R. Virtual manufacturing in industry 4.0: A review. Data Science and Management. 2023.
• 103. Bedaka AK, Vidal J, Lin C-Y. Automatic robot path integration using three-dimensional vision and offline programming. The Inter-national Journal of Advanced Manufacturing Technology. 2019;102:1935-50.
• 104. Devine K, Reifschneider L, editors. Agile robotic work cells for teaching manufacturing engineering. Proceedings of ASEE; 2009.
• 105. Lee NK. Total automation: The possibility of lights-out manufactur-ing in the near future. Missouri S&T’s Peer to Peer. 2018;2(1):4.
• 106. Søndergaard A, Feringa J, Stan F, Maier D. Robotic abrasive wire cutting of polymerized styrene formwork systems for cost-effective realization of topology-optimized concrete structures. Construction Robotics. 2018;2(1-4):81-92.
• 107. Zonta T, Da Costa CA, da Rosa Righi R, de Lima MJ, da Trindade ES, Li GP. Predictive maintenance in the Industry 4.0: A systemat-ic literature review. Computers & Industrial Engineering. 2020;150:106889.
• 108. Liu Y, Candell R, Kashef M, Montgomery K, editors. A collaborative work cell testbed for industrial wireless communications—the base-line design. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE); 2019: IEEE.
• 109. KUKA milling robot [Available from: https://www.kuka.com/en-my/products/process-technologies/milling.
• 110. Soori M, Arezoo B, Dastres R. Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems. 2023;3:192-204.
• 111. Soori M, Arezoo B, Dastres R. Digital Twin for Smart Manufactur-ing, A Review. Sustainable Manufacturing and Service Economics. 2023;2:100017.
• 112. Ashima R, Haleem A, Bahl S, Javaid M, Mahla SK, Singh S. Auto-mation and manufacturing of smart materials in Additive Manufac-turing technologies using Internet of Things towards the adoption of Industry 4.0. Materials Today: Proceedings. 2021;45:5081-8.
• 113. Oyekan J, Farnsworth M, Hutabarat W, Miller D, Tiwari A. Applying a 6 DoF robotic arm and digital twin to automate fan-blade recondi-tioning for aerospace maintenance, repair, and overhaul. Sensors. 2020;20(16):4637.
• 114. Lotti N, Xiloyannis M, Durandau G, Galofaro E, Sanguineti V, Masia L, et al. Adaptive model-based myoelectric control for a soft wearable arm exosuit: A new generation of wearable robot control. IEEE Robotics & Automation Magazine. 2020;27(1):43-53.
• 115. Carlucho I, De Paula M, Acosta GG. An adaptive deep reinforce-ment learning approach for MIMO PID control of mobile robots. ISA transactions. 2020;102:280-94.
• 116. Wang L, Orban P, Cunningham A, Lang S. Remote real-time CNC machining for web-based manufacturing. Robotics and Computer-Integrated Manufacturing. 2004;20(6):563-71.
• 117. Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using ma-chine learning approaches for multi-omics data analysis: A review. Biotechnology Advances. 2021;49:107739.
• 118. Enthrakandi Narasimhan G, Bettyjane J. Implementation and study of a novel approach to control adaptive cooperative robot using fuzzy rules. International Journal of Information Technology. 2021;13:2287-94.
• 119. Sheridan TB. Adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control: Distinctions and modes of adaptation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans. 2011;41(4):662-7.
• 120. Jezernik S, Colombo G, Morari M. Automatic gait-pattern adapta-tion algorithms for rehabilitation with a 4-DOF robotic orthosis. IEEE Transactions on Robotics and Automation. 2004;20(3):574-82.
• 121. Zou H, Chen C-L, Li M, Yang J, Zhou Y, Xie L, et al. Adversarial learning-enabled automatic WiFi indoor radio map construction and adaptation with mobile robot. IEEE Internet of Things Journal. 2020;7(8):6946-54.
• 122. Weingarten JD, Lopes GA, Buehler M, Groff RE, Koditschek DE, editors. Automated gait adaptation for legged robots. IEEE Interna-tional Conference on Robotics and Automation, 2004 Proceedings ICRA'04 2004; 2004: IEEE.
• 123. Neythalath N, Søndergaard A, Bærentzen JA. Adaptive robotic manufacturing using higher order knowledge systems. Automation in Construction. 2021;127:103702.
• 124. Sahil D, Sarabjeet SS, editors. REDUCTION OF SCRAP AND INSPECTION EFFORT: AN APPROACH INCORPORATING IN-DUSTRY 4.0. Electrophysical machining in modern industry; 2021.
• 125. Xiao R, Xu Y, Hou Z, Chen C, Chen S. An adaptive feature extrac-tion algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding. Sensors and Actuators A: Physical. 2019;297:111533.
• 126. Lv Y, Peng Z, Qu C, Zhu D. An adaptive trajectory planning algo-rithm for robotic belt grinding of blade leading and trailing edges based on material removal profile model. Robotics and Computer-Integrated Manufacturing. 2020;66:101987.
• 127. Perzylo A, Rickert M, Kahl B, Somani N, Lehmann C, Kuss A, et al. SMErobotics: Smart robots for flexible manufacturing. IEEE Robot-ics & Automation Magazine. 2019;26(1):78-90.
• 128. Jia F, Tzintzun J, Ahmad R, editors. An improved robot path plan-ning algorithm for a novel self-adapting intelligent machine tending robotic system. Industrial and Robotic Systems: LASIRS 2019; 2020: Springer.
• 129. Annem V, Rajendran P, Thakar S, Gupta SK, editors. Towards remote teleoperation of a semi-autonomous mobile manipulator system in machine tending tasks. International Manufacturing Sci-ence and Engineering Conference; 2019: American Society of Me-chanical Engineers.
• 130. Park Y-K, Park J, Kim S, Lee D, Lee S, Um J. Development of Digital twin for Plug-and-Produce of a Machine tending system through ISO 21919 interface. International Journal of Computer In-tegrated Manufacturing. 2023:1-16.
• 131. Steele JW, Wysk RA, Ferreira JC. A resource-oriented tolerance representation scheme for the planning of robotic machine tending operations in automated manufacturing systems. The International Journal of Advanced Manufacturing Technology. 2008;38:741-56.
• 132. Bahrin MAK, Othman MF, Azli NHN, Talib MF. Industry 4.0: A review on industrial automation and robotic. Jurnal teknologi. 2016;78(6-13).
• 133. Jia F, Jebelli A, Ma Y, Ahmad R. An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition. Applied Sciences. 2022;12(11):5697.
• 134. Schneider C, Klos M, Bdiwi M, Putz M. Machine-To-Machine (M2M) Communication of Robotic Platform in Machine Tending Applications.
• 135. Landscheidt S, Kans M, Winroth M. Opportunities for robotic automation in wood product industries: the supplier and system in-tegrators’ perspective. Procedia Manufacturing. 2017;11:233-40.
• 136. Rooks B. Robots make a show at the UK automation and machine tool exhibitions. Industrial Robot: An International Journal. 2002.
• 137. Chen Q, Heydari B, Moghaddam M. Leveraging task modularity in reinforcement learning for adaptable industry 4.0 automation. Journal of Mechanical Design. 2021;143(7).
• 138. Giles DM, Sinyuk A, Sorokin MG, Schafer JS, Smirnov A, Slutsker I, et al. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database–automated near-real-time quality control algo-rithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques. 2019;12(1):169-209.
• 139. Plaza EG, López PN, González EB. Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining. Journal of Manufacturing Processes. 2019;44:145-57.
• 140. Dev Anand M, Selveraj T, Kumanan S, Ajith Bosco Raj T. Robotics in online inspection and quality control using moment algorithm. Advances in Production Engineering & Management. 2012;7(1): 27-38.
• 141. Montironi M, Castellini P, Stroppa L, Paone N. Adaptive autono-mous positioning of a robot vision system: Application to quality control on production lines. Robotics and Computer-Integrated Manufacturing. 2014;30(5):489-98.
• 142. Tripathi S, Shukla S, Attrey S, Agrawal A, Bhadoria VS. Smart industrial packaging and sorting system. Strategic system assur-ance and business analytics. 2020:245-54.
• 143. Navon R. Process and quality control with a video camera, for a floor-tilling robot. Automation in construction. 2000;10(1):113-25.
• 144. Gupta M, Khan MA, Butola R, Singari RM. Advances in applica-tions of Non-Destructive Testing (NDT): A review. Advances in Ma-terials and Processing Technologies. 2022;8(2):2286-307.
• 145. Azamfirei V, Granlund A, Lagrosen Y. Multi-layer quality inspection system framework for industry 4.0. International journal of automa-tion technology. 2021;15(5):641-50.
• 146. Brito T, Queiroz J, Piardi L, Fernandes LA, Lima J, Leitão P. A machine learning approach for collaborative robot smart manufac-turing inspection for quality control systems. Procedia Manufactur-ing. 2020;51:11-8.
• 147. Sun Y, Lu L, Wu F, Xiao S, Sha J, Zhang L, editors. Error Analysis of a Coordinate Measuring Machine with a 6-DOF Industrial Robot Holding the Probe. Actuators; 2023: MDPI.
• 148. Saez M, Maturana FP, Barton K, Tilbury DM. Real-time manufac-turing machine and system performance monitoring using internet of things. IEEE Transactions on Automation Science and Engineer-ing. 2018;15(4):1735-48.
• 149. My CA. The role of big data analytics and AI in smart manufactur-ing: An overview. Research in Intelligent and Computing in Engi-neering: Select Proceedings of RICE 2020. 2021:911-21.
• 150. Chen S-L, Jen Y. Data fusion neural network for tool condition monitoring in CNC milling machining. International journal of ma-chine tools and manufacture. 2000;40(3):381-400.
• 151. Ridwan F, Xu X. Advanced CNC system with in-process feed-rate optimisation. Robotics and Computer-Integrated Manufacturing. 2013;29(3):12-20.
• 152. Li H, Li R, Zhang J, Zhang P. Development of a pipeline inspection robot for the standard oil pipeline of China national petroleum cor-poration. Applied Sciences. 2020;10(8):2853.
• 153. Bhatia P, Liu Y, Nagaraj S, Achanta V, Pulaparthi B, Diaz-Elsayed N, editors. Data-Driven Multi-Criteria Decision-Making for Smart and Sustainable Machining. ASME International Mechanical Engi-neering Congress and Exposition; 2021: American Society of Me-chanical Engineers.
• 154. Raval MB, Joshi H. Categorical framework for implementation of industry 4.0 techniques in medium-scale bearing manufacturing in-dustries. Materials Today: Proceedings. 2022;65:3531-7.
• 155. Lu Y, Xu X. Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robotics and Computer-Integrated Manufacturing. 2019;57:92-102.
• 156. Duro JA, Padget JA, Bowen CR, Kim HA, Nassehi A. Multi-sensor data fusion framework for CNC machining monitoring. Mechanical systems and signal processing. 2016;66:505-20.
• 157. Pieskä S, Kaarela J, Mäkelä J, editors. Simulation and program-ming experiences of collaborative robots for small-scale manufac-turing. 2018 2nd International Symposium on Small-scale Intelli-gent Manufacturing Systems (SIMS); 2018: IEEE.
• 158. Ronzoni M, Accorsi R, Botti L, Manzini R. A support-design frame-work for Cooperative Robots systems in labor-intensive manufac-turing processes. Journal of Manufacturing Systems. 2021;61:646-57.
• 159. Lima F, De Carvalho CN, Acardi MB, Dos Santos EG, De Miranda GB, Maia RF, et al. Digital manufacturing tools in the simulation of collaborative robots: Towards industry 4.0. Brazilian Journal of Op-erations & Production Management. 2019;16(2):261-80.
• 160. Hashemi-Petroodi SE, Thevenin S, Kovalev S, Dolgui A. Opera-tions management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey. Annual Re-views in Control. 2020;49:264-76.
• 161. Norman AR, Schönberg A, Gorlach IA, Schmitt R. Validation of iGPS as an external measurement system for cooperative robot positioning. The International Journal of Advanced Manufacturing Technology. 2013;64:427-46.
• 162. Glatt M, Sinnwell C, Yi L, Donohoe S, Ravani B, Aurich JC. Model-ing and implementation of a digital twin of material flows based on physics simulation. Journal of Manufacturing Systems. 2021;58:231-45.
• 163. Ammar M, Haleem A, Javaid M, Walia R, Bahl S. Improving mate-rial quality management and manufacturing organizations system through Industry 4.0 technologies. Materials Today: Proceedings. 2021;45:5089-96.
• 164. George P, Cheng C-T, Pang TY, Neville K. Task Complexity and the Skills Dilemma in the Programming and Control of Collabora-tive Robots for Manufacturing. Applied Sciences. 2023;13(7):4635.
• 165. Perez-Ubeda R, Gutierrez S, Zotovic R, Lluch-Cerezo J. Study of the application of a collaborative robot for machining tasks. Proce-dia Manufacturing. 2019;41:867-74.
• 166. Michalik P, Hatala M, Dobransky J, Macej J, Petrus M, Tirpak P, et al., editors. Design and Evaluation of Production of a Robotic An-gle Arm for Collaborative Robot Using the WorkNC CAM Applica-tion. 5th EAI International Conference on Management of Manufac-turing Systems; 2022: Springer.
• 167. Liu Y, Guo L, Gao H, You Z, Ye Y, Zhang B. Machine vision based condition monitoring and fault diagnosis of machine tools using in-formation from machined surface texture: A review. Mechanical Systems and Signal Processing. 2022;164:108068.
• 168. Kim H, Lim D-E, Lee S. Deep learning-based dynamic scheduling for semiconductor manufacturing with high uncertainty of automat-ed material handling system capability. IEEE Transactions on Semiconductor Manufacturing. 2020;33(1):13-22.
• 169. Wang KB, Dailami F, Matthews J. Towards collaborative robotic polishing of mould and die sets. Procedia Manufacturing. 2019;38:1499-507.
• 170. Miyake Y, Kondo Y. A study on new machining method applied to a collaborative robot for drilling. Robotics and Computer-Integrated Manufacturing. 2022;78:102409.
• 171. Borboni A, Reddy KVV, Elamvazuthi I, AL-Quraishi MS, Natarajan E, Azhar Ali SS. The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Re-view of Recent Works. Machines. 2023;11(1):111.
• 172. El Makrini I, Elprama SA, Van den Bergh J, Vanderborght B, Knevels A-J, Jewell CI, et al. Working with walt: How a cobot was developed and inserted on an auto assembly line. IEEE Robotics & Automation Magazine. 2018;25(2):51-8.
• 173. Touzani H, Hadj-Abdelkader H, Séguy N, Bouchafa S. Multi-robot task sequencing & automatic path planning for cycle time optimiza-tion: Application for car production line. IEEE Robotics and Auto-mation Letters. 2021;6(2):1335-42.
• 174. Goel R, Gupta P. Robotics and industry 4.0. A Roadmap to Indus-try 40: Smart Production, Sharp Business and Sustainable Devel-opment. 2020:157-69.
• 175. Tan M, Chen J, Radhakrishnan R, editors. Design of Control System of Automated Production Line Based on PLC and Robot. Tenth International Conference on Applications and Techniques in Cyber Intelligence (ICATCI 2022) Volume 2; 2023: Springer.
• 176. Rahman HF, Janardhanan MN, Nielsen P. An integrated approach for line balancing and AGV scheduling towards smart assembly systems. Assembly Automation. 2020;40(2):219-34.
• 177. Pedersen MR, Nalpantidis L, Andersen RS, Schou C, Bøgh S, Krüger V, et al. Robot skills for manufacturing: From concept to in-dustrial deployment. Robotics and Computer-Integrated Manufac-turing. 2016;37:282-91.
• 178. Susanti S, Sutopo W, Ngadiman N, editors. Equipment Replace-ment Analysis from Manual Line to Automatic Line in Palletizing Activities: A Case Study. IOP Conference Series: Materials Sci-ence and Engineering; 2021: IOP Publishing.
• 179. Mohammed A, Schmidt B, Wang L. Active collision avoidance for human–robot collaboration driven by vision sensors. International Journal of Computer Integrated Manufacturing. 2017;30(9):970-80.
• 180. Wang S, Jiang L, Meng J, Xie Y, Ding H. Training for smart manu-facturing using a mobile robot-based production line. Frontiers of Mechanical Engineering. 2021;16:249-70.
• 181. Malik AA, Brem A. Digital twins for collaborative robots: A case study in human-robot interaction. Robotics and Computer-Integrated Manufacturing. 2021;68:102092.
• 182. Kukartsev V, Boyko A, Mikhalev A, Tynchenko V, Rukosueva A, Korpacheva L, editors. Simulation-dynamic model of working time costs calculation for performance of operations on CNC machines. Journal of Physics: Conference Series; 2020: IOP Publishing.
• 183. Scholz S, Mueller T, Plasch M, Limbeck H, Adamietz R, Isering-hausen T, et al. A modular flexible scalable and reconfigurable sys-tem for manufacturing of microsystems based on additive manu-facturing and e-printing. Robotics and Computer-Integrated Manu-facturing. 2016;40:14-23.
• 184. Kousi N, Gkournelos C, Aivaliotis S, Lotsaris K, Bavelos AC, Baris P, et al. Digital twin for designing and reconfiguring human–robot collaborative assembly lines. Applied Sciences. 2021;11(10):4620.