Grinding is a process that still today largely depends on the skill and experience of the operator. For grinding, no generalized computer-aided manufacturing tool exists as for milling, and the grinding machine manufacturers have their proprietary process planning tools for the path planning. The success of a grinding process furthermore depends on a suitable conditioning of the grinding wheel and not only on the appropriate selection of the process parameters. Skilled operators are, on the one hand, capable of setting up the process in shorter time than their less skilled colleagues and with immediate success. Artificial intelligence, driven by sufficiently increased computational performance, is increasingly capable of handling manufacturing processes, particularly as these processes become more complicated and experience-based. Therefore, extending the machine’s ability towards what today is the task of the operator, namely process planning feeding the vision of “operator integrated” is a breakthrough in zero defect manufacturing and first part right for grinding processes. The paper conceptualizes an intelligent grinding machine that uses ontologies, applies rule-based planning tools, makes use of physical as well as autonomous modeling, and is capable of learning. Moreover, the processes of grinding and dressing are fully monitored so that a self-learning ability is provided. Learning is fed from different sources, from monitoring, from other machines requiring filters built on physical models, and from the operator with the ability to deal with incomplete, unstructured, and unreliable data. From this the way, how such a machine communicates with operators must be completely different than today. Research results and literature are provided to discuss the different aspects like machine state monitoring, process monitoring, and parameter selection for an optimized grinding process.

1. [1] E. Brinksmeier, J. C. Aurich, E. Govekar, C. Heinzel, H. W. Hoffmeister, F. Klocke, J. Peters, R. Rentsch, D. J. Stephenson, E. Uhlmann, K. Weinert, and M. Wittmann, “Advances in modeling and simulation of grinding processes,” CIRP Annals, Vol.55, No.2, pp. 667-696, 2006. https://doi.org/10.1016/j.cirp.2006.10.003
2. [2] K. Wegener, F. Bleicher, P. Krajnik, H. W. Hoffmeister, and C. Brecher, “Recent developments in grinding machines,” CIRP Annals, Vol.66, No.2, pp. 779-802, 2017. https://doi.org/10.1016/j.cirp.2017.05.006
3. [3] B. Azarhoushang and H. Kitzig-Frank, “Der Grinding Expert – Zukunft der Schleiftechnologie,” H. W. Hoffmeister and B. Denkena (Eds.), “Jahrbuch Schleifen, Honen, Läppen und Polieren: Verfahren und Maschinen,” Vol.70, pp. 96-104, Vulkan Verlag, 2023 (in German).
4. [4] P. Krajnik, R.Drazumeric, J. Badger, and F. Hashimoto, “Cycle optimization in cam-lobe grinding for high productivity,” CIRP Annals, Vol.63, No.1, pp. 333-336, 2014. https://doi.org/10.1016/j.cirp.2014.03.036
5. [5] S. Venk, R. Govind, and M. E. Merchant, “An expert system approach to optimization of the centerless grinding process,” CIRP Annals, Vol.39, No.1, pp. 489-492, 1990. https://doi.org/10.1016/S0007-8506(07)61103-2
6. [6] J. Wang, Y. Tian, X. Hu, Z. Fan, J. Han, and Y. Liu, “Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: Experimental approach,” J. of Intelligent Manufacturing, Vol.35, Issue 3, pp. 1013-1035, 2023. https://doi.org/10.1007/s10845-023-02089-1
7. [7] R. J. Sternberg, “Development of intelligence,” Encyclopaedia Britannica, 2025. https://www.britannica.com/science/human-intelligence-psychology/Development-of-intelligence [Accessed June 21, 2023]
8. [8] G. N. Saridis, “Hierarchically intelligent machines,” World Scientific Publishing, 2001.
9. [9] S. L. Andresen, “John McCarthy: Father of AI,” IEEE Intelligent Systems, Vol.17, No.5, pp. 84-85, 2002. https://doi.org/10.1109/MIS.2002.1039837
10. [10] G. Alden, “Operation of grinding wheels in machine grinding,” Trans. of the ASME, Vol.36, pp. 451-460, 1914. https://doi.org/10.1115/1.4059851
11. [11] W. R. Backer and M. E. Merchant, “On the basic mechanics of the grinding process,” Trans. of the ASME, Vol.80, No.1, pp. 141-146, 1958. https://doi.org/10.1115/1.4012287
12. [12] J. Peklenik, “Contribution to the correlation theory for the grinding process,” J. of Engineering for Industry, Vol.86, No.2, pp. 85-94, 1964. https://doi.org/10.1115/1.3670496
13. [13] I. Grabec and E. Kuljanić, “Characterization of manufacturing processes based upon acoustic emission analysis by neural networks,” CIRP Annals, Vol.43, No.1, pp. 77-80, 1994. https://doi.org/10.1016/S0007-8506(07)62168-4
14. [14] W. B. Rowe, L. Yan, I. Inasaki, and S. Malkin, “Applications of artificial intelligence in grinding,” CIRP Annals, Vol.43, No.2, pp. 521-531, 1994. https://doi.org/10.1016/S0007-8506(07)60498-3
15. [15] F. Gaegauf, “Technologie schafft Wettbewerbsvorteile,” Schweizer Präzisionstechnik, pp. 26-28, 2011 (in German).
16. [16] Reishauer AG, “ARGUS Monitoring System – Broschüre,” Reishauer AG, 2023. https://www.reishauer.com/fileadmin/Dateiliste/Technologie/ARGUS/brochure_argus_de_web.pdf [Accessed September 24, 2024]
17. [17] J. Badger, H. Wyman, and F. Hashimoto, “A unified approach to traverse dressing with radiused diamond tools,” CIRP Annals, Vol.73, No.1, pp. 245-248, 2024. https://doi.org/10.1016/j.cirp.2024.04.029
18. [18] R. Drazumeric, J. Badger, R. Roininen, and P. Krajnik, “On geometry and kinematics of abrasive processes: The theory of aggressiveness,” Int. J. of Machine Tools and Manufacture, Vol.154, Article No.103567, 2020. https://doi.org/10.1016/j.ijmachtools.2020.103567
19. [19] G. Byrne, O. Damm, L. Monostori, R. Teti, F. van Houten, K. Wegener, R. Wertheim, and F. Sammler, “Towards high performance living manufacturing systems – A new convergence between biology and engineering,” CIRP J. of Manufacturing Science and Technology, Vol.34, pp. 6-21, 2021. https://doi.org/10.1016/j.cirpj.2020.10.009
20. [20] H. C. Möhring, P. Wiederkehr, K. Erkorkmaz, and Y. Kakinuma, “Self-optimizing machining systems,” CIRP Annals, Vol.69, No.2, pp. 740-763, 2020. https://doi.org/10.1016/j.cirp.2020.05.007
21. [21] L. Lv, Z. Deng, T. Liu, Z. Li, and W. Liu, “Intelligent technology in grinding process driven by data: A review,” J. of Manufacturing Processes, Vol.58, pp. 1039-1051, 2020. https://doi.org/10.1016/j.jmapro.2020.09.018
22. [22] V. Pandiyan, S. Shevchik, K. Wasmer, S. Castagne, and T. Tjahjowidodo, “Modeling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review,” J. of Manufacturing Processes, Vol.57, pp. 114-135, 2020. https://doi.org/10.1016/j.jmapro.2020.06.013
23. [23] T. Kaufmann, S. Sahay, P. Niemietz, D. Trauth, W. Maaß, and T. Bergs, “AI-based framework for deep learning applications in grinding,” 2020 IEEE 18th World Symp. on Applied Machine Intelligence and Informatics, pp. 195-200, 2020. https://doi.org/10.1109/SAMI48414.2020.9108743
24. [24] T. Gittler, “Data acquisition and data analytics or prognostics and health management in machine tools,” Ph.D. thesis, No.28940, ETH Zürich, 2023.
25. [25] P. Renggli and T. Waefler, “Why do or don’t you provide your knowledge to an AI?,” Artificial Intelligence and Social Computing, Vol.122, pp. 1-9, 2024. https://doi.org/10.54941/ahfe1004636
26. [26] W. X. Ng, H. K. Chan, W. K. Teo, and I. M. Chen, “Capturing the tacit knowledge of the skilled operator to program tool paths and tool orientations for robot belt grinding,” The Int. J. of Advanced Manufacturing Technology, Vol.91, No.5, pp. 1599-1618, 2017. https://doi.org/10.1007/s00170-016-9813-8
27. [27] P. Krajnik, K. Wegener, T. Bergs, and A. Shih, “Advances in modeling of fixed-abrasive processes,” CIRP Annals, Vol.73, No.2, pp. 589-614, 2024. https://doi.org/10.1016/j.cirp.2024.05.001
28. [28] S. Fattahi, B. Azarhoushang, and H. Kitzig-Frank, “Knowledge-based adaptive design of experiments (KADoE) for grinding process optimization using an expert system in the context of Industry 4.0,” J. of Manufacturing and Materials Processing, Vol.9, No.2, Article No.6, 2025. https://doi.org/10.3390/jmmp9020062
29. [29] H. Hong, Y. Yin, and X. Chen, “Ontological modeling of knowledge management for human–machine integrated design of ultra-precision grinding machine,” Enterprise Information Systems, Vol.10, No.9, pp. 970-981, 2016. https://doi.org/10.1080/17517575.2015.1071433
30. [30] M. A. Musen, “The Protégé Project: A look back and a look forward,” AI Matters, Vol.1, No.4, pp. 4-12, 2015. https://doi.org/10.1145/2757001.2757003
31. [31] M. Weiten, “OntoSTUDIO® as a ontology engineering environment,” J. Davies, M. Grobelnik, and D. Mladenić (Eds.), “Semantic Knowledge Management: Integrating Ontology Management, Knowledge Discovery, and Human Language Technologies,” pp. 51-60, Springer, 2009. https://doi.org/10.1007/978-3-540-88845-1_5
32. [32] H. Hong and Y. Yin, “Ontology-based human–machine integrated design method for ultra-precision grinding machine spindle,” J. of Industrial Information Integration, Vol.2, pp. 1-10, 2016. https://doi.org/10.1016/j.jii.2016.04.003
33. [33] Q. Wang, X. Chen, Y. Yin, and J. Lu, “Ontology-based coupled optimization design method using state-space analysis for the spindle box system of large ultra-precision optical grinding machine,” Enterprise Information Systems, Vol.11, No.7, pp. 1105-1118, 2017. https://doi.org/10.1080/17517575.2015.1062920
34. [34] C. Huang, H. Cai, L. Xu, B. Xu, Y. Gu, and L. Jiang, “Data-driven ontology generation and evolution towards intelligent service in manufacturing systems,” Future Generation Computer Systems, Vol.101, pp. 197-207, 2019. https://doi.org/10.1016/j.future.2019.05.075
35. [35] Q. Cao, F. Giustozzi, C. Zanni-Merk, F. de Bertrand de Beuvron, and C. Reich, “Smart condition monitoring for industry 4.0 manufacturing processes: An ontology-based approach,” Cybernetics and Systems, Vol.50, No.2, pp. 82-96, 2019. https://doi.org/10.1080/01969722.2019.1565118
36. [36] N. Fathallah, A. Das, S. De Giorgis, A. Poltronieri, P. Haase, and L. Kovriguina, “NeOn-GPT: A large language model-powered pipeline for ontology learning,” Extended Semantic Web Conf., 2024.
37. [37] C. Brecher, P. Hirsch, and M. Weck, “Compensation of thermo-elastic machine tool deformation based on control internal data,” CIRP Annals, Vol.53, No.1, pp. 299-304, 2004.
38. [38] M. Mareš, O. Horejš, and L. Havlík, “Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece,” Precision Engineering, Vol.66, pp. 21-30, 2020. https://doi.org/10.1016/j.precisioneng.2020.06.010
39. [39] J. Mayr, P. Blaser, A. Ryser, and P. Hernandez-Becerro, “An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates,” CIRP Annals, Vol.67, No.1, pp. 551-554, 2018. https://doi.org/10.1016/j.cirp.2018.04.001
40. [40] N. Zimmermann, T. Büchi, J. Mayr, and K. Wegener, “Self-optimizing thermal error compensation models with adaptive inputs using Group-LASSO for ARX-models,” J. of Manufacturing Systems, Vol.64, pp. 615-625, 2022. https://doi.org/10.1016/j.jmsy.2022.04.015
41. [41] B. Botcha, V. Rajagopal, and S. Bukkapatnam, “Process-machine interactions and a multi-sensor fusion approach to predict surface roughness in cylindrical plunge grinding process,” Procedia Manufacturing, Vol.26, pp. 700-711, 2018. https://doi.org/10.1016/j.promfg.2018.07.080
42. [42] M. Barandas, D. Folgado, L. Fernandes, S. Santos, M. Abreu, P. Bota, H. Liu, T. Schultz, and H. Gamboa, “TSFEL: Time series feature extraction library,” SoftwareX, Vol.11, Article No.100456, 2020. https://doi.org/10.1016/j.softx.2020.100456
43. [43] E. Sauter, “Detection and avoidance of thermal damage for high-performance metal grinding processes using hybrid machine learning models,” Ph.D. thesis, No.29055, ETH Zürich, 2023.
44. [44] E. Sauter, E. Sarikaya, M. Winter, and K. Wegener, “In-process detection of grinding burn using machine learning,” The Int. J. of Advanced Manufacturing Technology, Vol.115, No.7, pp. 2281-2297, 2021. https://doi.org/10.1007/s00170-021-06896-9
45. [45] S. Mahata, P. Shakya, and N. Babu, “A robust condition monitoring methodology for grinding wheel wear iIdentification using Hilbert-Huang Transform,” Precision Engineering, Vol.70, pp. 77-91, 2021. https://doi.org/10.1016/j.precisioneng.2021.01.009
46. [46] P. Sachin Krishnan and K. Rameshkumar, “Grinding wheel condition prediction with discrete hidden Markov model using acoustic emission signature,” Materials Today: Proc., Vol.46, pp. 9168-9175, 2021. https://doi.org/10.1016/j.matpr.2019.12.428
47. [47] R. Kuppuswamy, F. Jani, S. Naidoo, and Q. De Jongh, “A study on intelligent grinding systems with industrial perspective,” Int. J. of Advanced Manufacturing Technology, Vol.115, pp. 3811-3827, 2021. https://doi.org/10.1007/s00170-021-07315-9
48. [48] H. Hübner, M. Duarte, and R. Da Silva, “Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks,” Int. J. of Advanced Manufacturing Technology, Vol.110, pp. 1833-1849, 2020. https://doi.org/10.1007/s00170-020-05902-w
49. [49] J. S. Kwak and M. K. Ha, “Detection of dressing time using the grinding force signal based on the discrete wavelet decomposition,” Int. J. of Advanced Manufacturing Technology, Vol.23, Nos.1-2, pp. 87-92, 2004. https://doi.org/10.1007/s00170-003-1556-7
50. [50] R. Neto, M. Marchi, C. Martins, P. R. Aguiar, and E. Bianchi, “Monitoring of grinding burn by AE and vibration signals,” Proc. of 6th Int. Conf. on Agents and Artificial Intelligence, pp. 272-279, 2014.
51. [51] W. Saxler, “Erkennung von Schleifbrand durch Schallemissionsanalyse,” VDI-Verlag, 1997 (in German).
52. [52] E. P. Frigieri, P. H. S. Campos, A. P. Paiva, P. P. Balestrassi, J. R. Ferreira, and C. A. Ynoguti, “A mel-frequency cepstral coefficient-based approach for surface roughness diagnosis in hard turning using acoustic signals and gaussian mixture models,” Applied Acoustics, Vol.113, pp. 230-237, 2016. https://doi.org/10.1016/j.apacoust.2016.06.027
53. [53] Z. Yang, Z. Yu, H. Wu, and D. Chang, “Laser-induced thermal damage detection in metallic materials via acoustic emission and ensemble empirical mode decomposition,” J. of Materials Processing Technology, Vol.214, pp. 1617-1626, 2014. https://doi.org/10.1016/j.jmatprotec.2014.03.009
54. [54] B. Botcha, A. Iquebal, and S. Bukkapatnam, “Efficient manufacturing processes and performance qualification via active learning: Application to a cylindrical plunge grinding platform,” Procedia Manufacturing, Vol.53, pp. 716-725, 2021. https://doi.org/10.1016/j.promfg.2021.06.070
55. [55] Y. Zhang, G. Xiao, J. Ma, H. Gao, B. Zhu, and Y. Huang, “A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding,” Int. J. of Advanced Manufacturing Technology, Vol.130, pp. 311-328, 2024. https://doi.org/10.1080/00207543.2021.2010144
56. [56] A. Heng, S. Zhang, A. C. C. Tan, and J. Mathew, “Rotating machinery prognostics: State of the art, challenges and opportunities,” Mechanical Systems and Signal Processing, Vol.23, No.3, pp. 724-739, 2009. https://doi.org/10.1016/j.ymssp.2008.06.009
57. [57] A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, Vol.20, No.7, pp. 1483-1510, 2006. https://doi.org/10.1016/j.ymssp.2005.09.012
58. [58] K. F. Martin, “A review by discussion of condition monitoring and fault diagnosis in machine tools,” Int. J. of Machine Tools and Manufacture, Vol.34, No.4, pp. 527-551, 1994. https://doi.org/10.1016/0890-6955(94)90083-3
59. [59] K. Wegener, H. W. Hoffmeister, B. Karpuschewski, F. Kuster, W. C. Hahmann, and M. Rabiey, “Conditioning and monitoring of grinding wheels,” CIRP Annals, Vol.60, No.2, pp. 757-777, 2011. https://doi.org/10.1016/j.cirp.2011.05.003
60. [60] J. Leukel, J. González, and M. Riekert, “Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review,” J. of Manufacturing Systems, Vol.61, pp. 87-96, 2021. https://doi.org/10.1016/j.jmsy.2021.08.012
61. [61] H. N. Teixeira, I. Lopes, and A. C. Braga, “Condition-based maintenance implementation: A literature review,” Procedia Manufacturing, Vol.51, pp. 228-235, 2020. https://doi.org/10.1016/j.promfg.2020.10.033
62. [62] M. Ahmer, P. Marklund, M. Gustafsson, and K. Berglund, “An implementation framework for condition-based maintenance in a bearing ring grinder,” Procedia CIRP, Vol.107, pp. 746-751, 2022. https://doi.org/10.1016/j.procir.2022.05.056
63. [63] M. Ahmer, F. Sandin, P. Marklund, M. Gustafsson, and K. Berglund, “A unified approach towards performance monitoring and condition-based maintenance in grinding machines,” Procedia CIRP, Vol.93, pp. 1388-1393, 2020. https://doi.org/10.1016/j.procir.2020.04.094
64. [64] M. Ahmer, F. Sandin, P. Marklund, M. Gustafsson, and K. Berglund, “Failure mode classification for condition-based maintenance in a bearing ring grinding machine,” The Int. J. of Advanced Manufacturing Technology, Vol.122, No.3, pp. 1479-1495, 2022. https://doi.org/10.1007/s00170-022-09930-6
65. [65] M. Ahmer, F. Sandin, P. Marklund, M. Gustafsson, and K. Berglund, “Using multivariate quality statistic for maintenance decision support in a bearing ring grinder,” Machines, Vol.10, No.9, Article No.794, 2022. https://doi.org/10.3390/machines10090794
66. [66] K. Kannan and N. Arunachalam, “A digital twin for grinding wheel: An information sharing platform for sustainable grinding process,” J. of Manufacturing Science and Engineering, Vol.141, No.2, Article No.021015, 2018. https://doi.org/10.1115/1.4042076
67. [67] C. H. Lee, J. S. Jwo, H. Y. Hsieh, and C. S. Lin, “An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning,” IEEE Access, Vol.8, pp. 58279-58289, 2020. https://doi.org/10.1109/ACCESS.2020.2982800
68. [68] M. Maier, A. Rupenyan, C. Bobst, and K. Wegener, “Self-optimizing grinding machines using Gaussian process models and constrained Bayesian optimization,” The Int. J. of Advanced Manufacturing Technology, Vol.108, No.1, pp. 539-552, 2020. https://doi.org/10.1007/s00170-020-05369-9
69. [69] C. Nametala, A. Souza, B. Pereira Júnior, and E. Da Silva, “A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels,” CIRP J. of Manufacturing Science and Technology, Vol.30, pp. 157-173, 2020. https://doi.org/10.1016/j.cirpj.2020.05.004
70. [70] M. Maier, H. Kunstmann, R. Zwicker, A. Rupenyan, and K. Wegener, “Autonomous and data-efficient optimization of turning processes using expert knowledge and transfer learning,” J. of Materials Processing Technology, Vol.303, Article No.117540, 2022. https://doi.org/10.1016/j.jmatprotec.2022.117540
71. [71] C. E. Rasmussen and C. K. I. Williams, “Gaussian processes for machine learning,” MIT Press, 2006.
72. [72] M. Maier, “Autonomous parameter selection of manufacturing processes with applications in grinding and turning,” Ph.D. thesis, No.28002, ETH Zürich, 2022.
73. [73] B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas, “Taking the human out of the loop: A review of Bayesian optimization,” Proc. of the IEEE, Vol.104, No.1, pp. 148-175, 2016. https://doi.org/10.1109/JPROC.2015.2494218
74. [74] M.-A. Dittrich, “Autonome Werkzeugmaschinen – Definition, Elemente und technische Integration,” TEWISS Verlag, 2021 (in German).
75. [75] B. Denkena, M. A. Dittrich, M. Lindauer, J. Mainka, and L. Stürenburg, “Using AutoML to optimize shape error prediction in milling processes,” Proc. of the Machining Innovations Conf., pp. 160-165, 2020. https://doi.org/10.2139/ssrn.3724234
76. [76] J. Krüger, J. Fleischer, J. Franke, and P. Groche, “WGP Standpunkt: KI in der Produktion,” Wissenschaftliche Gesellschaft für Produktionstechnik e.V., 2019 (in German).
77. [77] A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean absolute percentage error for regression models,” Neurocomputing, Vol.192, pp. 38-48, 2016. https://doi.org/10.1016/j.neucom.2015.12.114
78. [78] B. Denkena, M. A. Dittrich, V. Böß, M. Wichmann, and S. Friebe, “Self-optimizing process planning for helical flute grinding,” Production Engineering, Vol.13, No.5, pp. 599-606, 2019. https://doi.org/10.1007/s11740-019-00908-0
79. [79] M.-A. Dittrich and F. Uhlich, “Self-optimizing compensation of surface deviations in 5-axis ball-end milling based on an enhanced description of cutting conditions,” CIRP J. of Manufacturing Science and Technology, Vol.31, pp. 224-232, 2020. https://doi.org/10.1016/j.cirpj.2020.05.013