{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:51:18Z","timestamp":1775191878619,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federation of Industrial Research Associations (AiF) as part of the Industrial Collective Research program (IGF)","award":["19881 N"],"award-info":[{"award-number":["19881 N"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.<\/jats:p>","DOI":"10.3390\/s21217147","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"7147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3585-8042","authenticated-orcid":false,"given":"Matthias","family":"D\u00f6rr","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenz","family":"Ott","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sven","family":"Matthiesen","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5827-9575","authenticated-orcid":false,"given":"Thomas","family":"Gwosch","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Das, J., Bales, G.L., Kong, Z., and Linke, B. (2018). Integrating Operator Information for Manual Grinding and Characterization of Process Performance Based on Operator Profile. J. Manuf. Sci. Eng., 140.","DOI":"10.1115\/1.4040266"},{"key":"ref_2","unstructured":"Malkin, S., and Guo, C. (2008). Grinding technology: Theory & Application of Machining with Abrasives, Industrial Press. [2nd ed.]."},{"key":"ref_3","unstructured":"Walls, L., Revie, M., and Bedford, T. (2016). Reliability engineering in face of shorten product life cycles: Challenges, technique trends and method approaches to ensure product reliability. Risk, Reliability and Safety: Innovating Theory and Practice, Proceedings of the ESREL 2016, Glasgow, Scotland, 25\u201329 September 2016, CRC Press."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lim, A., Kim, J., and Zechmann, E. (2013, January 15\u201321). Development of an Experimental Method to Estimate the Operating Force of a Hand-Held Power Tool Utilizing Measured Transfer Functions. Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition. Volume 4A: Dynamics, Vibration and Control, San Diego, CA, USA.","DOI":"10.1115\/IMECE2013-65801"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Phan, G., Kana, S., and Campolo, D. (2017, January 19\u201321). Instrumentation of a grinding tool for capturing dynamic interactions with the workpiece. Proceedings of the 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE International Conferences on CIS & RAM, Ningbo, China.","DOI":"10.1109\/ICCIS.2017.8274836"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.cirp.2013.05.006","article-title":"Recent advances in modelling of metal machining processes","volume":"62","author":"Arrazola","year":"2013","journal-title":"CIRP Ann."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kunto\u011flu, M., Aslan, A., Pimenov, D.Y., Usca, \u00dc.A., Salur, E., Gupta, M.K., Mikolajczyk, T., Giasin, K., Kap\u0142onek, W., and Sharma, S. (2021). A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends. Sensors, 21.","DOI":"10.3390\/s21010108"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sousa, V.F.C., Silva, F.J.G., Fecheira, J.S., Lopes, H.M., Martinho, R.P., Casais, R.B., and Ferreira, L.P. (2020). Cutting Forces Assessment in CNC Machining Processes: A Critical Review. Sensors, 20.","DOI":"10.3390\/s20164536"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1299\/jsmec.49.307","article-title":"Monitoring Method of Cutting Force by Using Additional Spindle Sensors","volume":"49","author":"Sarhan","year":"2006","journal-title":"JSME Int. J. Ser. C"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, C.-S., and Ou, Y.-J. (2020). Grinding Wheel Loading Evaluation by Using Acoustic Emission Signals and Digital Image Processing. Sensors, 20.","DOI":"10.3390\/s20154092"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kaufmann, T., Sahay, S., Niemietz, P., Trauth, D., Maas, W., and Bergs, T. (2020). AI-based Framework for Deep Learning Applications in Grinding. IEEE 18th World Symposium on Applied Machine Intelligence and Informatics, Proceedings of the SAMI, Herlany, Slovakia, 23\u201325 January 2020, IEEE.","DOI":"10.1109\/SAMI48414.2020.9108743"},{"key":"ref_12","unstructured":"Dornfeld, D. (2014, January 23\u201325). Sustainability Analysis of Grinding with Power Tools. Proceedings of the 6th CIRP International Conference on High Performance Cutting: Procedia CIRP Volume 14. HPC2014, Berkeley, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Davies, M.A., and M\u2019Saoubi, R. (2016, January 10). Effect of Manual Grinding Operations on Surface Integrity. Proceedings of the 3rd CIRP Conference on Surface Integrity (CSI 2016): Procedia CIRP Volume 45. CSI 2016, Charlotte, NC, USA.","DOI":"10.1016\/j.procir.2016.02.091"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bales, G.L., Das, J., Tsugawa, J., Linke, B., and Kong, Z. (2017). Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance. J. Manuf. Sci. Eng., 139.","DOI":"10.1115\/1.4037615"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1520\/SSMS20190045","article-title":"Enabling Advanced Process Control for Manual Grinding Operations","volume":"4","author":"Kamath","year":"2020","journal-title":"Smart Sustain. Manuf. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Matthiesen, S., Gwosch, T., and Bruchmueller, T. (2017, January 10\u201311). Experimentelle Identifikation von Schwingungsursachen in Antriebsstr\u00e4ngen von Power-Tools: VDI Fachtagung Schwingungen 2017: Berechnung, \u00dcberwachung, Anwendung. Proceedings of the VDI Fachtagung Schwingungen 2017: Berechnung, \u00dcberwachung, Anwendung. VDI-Fachtagung Schwingungen 2017, N\u00fcrtingen, Germany.","DOI":"10.51202\/9783181022955-311"},{"key":"ref_17","unstructured":"Shih, A., and Wang, L. (July, January 27). Recognizing Gaze-Motor Behavioral Patterns in Manual Grinding Tasks. Proceedings of the 44th North American Manufacturing Research Conference. NAMRC, Blacksburg, VA, USA."},{"key":"ref_18","unstructured":"Krause, D., Paetzold, K., and Wartzack, S. (2018). Testfallgenerierung\u2014Vorgehen zur Lastkollektivermittlung durch Data Mining am Winkelschleifer. Design for X: Proceedings of the 29th Symposium 2018. DfX, TuTech Verlag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10010-016-0203-z","article-title":"Experimental identification of component loads in a power tool power-train through indirect measurement in realistically applications as an element of subsystem validation","volume":"80","author":"Matthiesen","year":"2016","journal-title":"Forsch. im Ing.-Eng. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76","DOI":"10.37544\/0720-5953-2017-07-08-76","article-title":"Realit\u00e4tsnahe Komponententests zur Unterst\u00fctzung der Produktentwicklung bei der Validierung von Power-Tools","volume":"69","author":"Matthiesen","year":"2017","journal-title":"Konstruktion"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dong, R.G., Welcome, D.E., Xu, X.S., and McDowell, T.W. (2020). Identification of effective engineering methods for controlling handheld workpiece vibration in grinding processes. Int. J. Ind. Ergon., 77.","DOI":"10.1016\/j.ergon.2020.102946"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s00170-020-06066-3","article-title":"Design and characterization of an instrumented hand-held power tool to capture dynamic interaction with the workpiece during manual operations","volume":"111","author":"Phan","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Giarmatzis, G., Zacharaki, E.I., and Moustakas, K. (2020). Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning. Sensors, 20.","DOI":"10.3390\/s20236933"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Stetter, B.J., Ringhof, S., Krafft, F.C., Sell, S., and Stein, T. (2019). Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning. Sensors, 19.","DOI":"10.3390\/s19173690"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lim, H., Kim, B., and Park, S. (2019). Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning. Sensors, 20.","DOI":"10.3390\/s20010130"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, H., Schomaker, L.R., and Carloni, R. (2020\u201324, January 24). IMU-based Deep Neural Networks for Locomotor Intention Prediction. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341649"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Choi, A., Jung, H., and Mun, J.H. (2019). Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle during Walking. Sensors, 19.","DOI":"10.3390\/s19132974"},{"key":"ref_28","first-page":"9372","article-title":"IMU sensors beneath walking surface for ground reaction force prediction in gait","volume":"20","author":"Wu","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Voet, H., Altenhof, M., Ellerich, M., Schmitt, R.H., and Linke, B. (2019). A Framework for the Capture and Analysis of Product Usage Data for Continuous Product Improvement. J. Manuf. Sci. Eng., 141.","DOI":"10.1115\/1.4041948"},{"key":"ref_30","unstructured":"Ahram, T., Taiar, R., Langlois, K., and Choplin, A. (2021). Data-Driven Analysis of Human-Machine Systems\u2014A Data Logger and Possible Use Cases for Field Studies with Cordless Power Tools. Human Interaction, Emerging Technologies and Future Applications III, Springer International Publishing."},{"key":"ref_31","unstructured":"ISO\u2014International Organization for Standardization (2007). Mechanical Vibration and Shock\u2014Coupling Forces at the Man\u2014Machine Interface for Hand-Transmitted Vibration, Beuth Verlag. ISO 15230:2007."},{"key":"ref_32","unstructured":"Ranganathan, S., Gribskov, M., Nakai, K., and Sch\u00f6nbach, C. (2019). Regression Analysis. Encyclopedia of Bioinformatics and Computational Biology, Academic Press."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, The MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_34","unstructured":"Kern, T.A., Matysek, M., Meckel, O., Rausch, J., Rettig, A., Roese, A., and Sindlinger, S. (2009). Entwicklung Haptischer Ger\u00e4te: Ein Einstieg f\u00fcr Ingenieure, Springer International Publishing."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1007\/s00170-018-2026-6","article-title":"Prediction of surface roughness and cutting forces using RSM, ANN, and NSGA-II in finish turning of AISI 4140 hardened steel with mixed ceramic tool","volume":"97","author":"Meddour","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s10845-008-0097-1","article-title":"Estimation of cutting forces and surface roughness for hard turning using neural networks","volume":"19","author":"Sharma","year":"2008","journal-title":"J. Intell. Manuf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jmatprotec.2007.02.045","article-title":"Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network","volume":"190","author":"Aykut","year":"2007","journal-title":"J. Mater. Process. Technol."},{"key":"ref_38","unstructured":"D\u00f6rr, M., Zimprich, S., D\u00fcrkopp, A., Bruchm\u00fcller, T., Gittel, H.-J., Matthiesen, S., Pelshenke, C., and D\u00fcltgen, P. (2019, January 12\u201316). Experimental Abrasive Contact Analysis\u2014Dynamic Forces between Grinding Discs and Steel for common Angle Grinder Applications. Proceedings of the 11th TOOLING 2019 conference & exhibition, TOOLING, Aachen, Germany."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sanhudo, L., Calvetti, D., Martins, J.P., Ramos, N.M., M\u00eada, P., Gon\u00e7alves, M.C., and Sousa, H. (2021). Activity classification using accelerometers and machine learning for complex construction worker activities. J. Build. Eng., 35.","DOI":"10.1016\/j.jobe.2020.102001"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/08956308.2018.1495964","article-title":"Improving Usage Metrics for Pay-per-Use Pricing with IoT Technology and Machine Learning","volume":"61","author":"Heinis","year":"2018","journal-title":"Res. Technol. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Putnik, G.D. (2019, January 8\u201310). Recognizing Product Application based on Integrated Consumer Grade Sensors: A Case Study with Handheld Power Tools: Procedia 29th CIRP Design Conference. Proceedings of the 29th CIRP Design Conference: Procedia CIRP Volume 84, CIRP Design, P\u00f3voa de Varzim, Portgal.","DOI":"10.1016\/j.procir.2019.04.317"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:21:53Z","timestamp":1760167313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":41,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217147"],"URL":"https:\/\/doi.org\/10.3390\/s21217147","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]}}}