{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:02:54Z","timestamp":1783699374267,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green, yellow and red. A series of signals were collected as training data for the classification algorithms. Measurements were saved in separate data sets with corresponding time windows. A total of ten methods were evaluated in terms of overall accuracy and the number of misclassification errors. Three solutions obtained an acceptable accuracy rate above 85%. Algorithms were able to assign states without the most undesirable red-green and green-red errors. The best results were achieved by the Extreme Gradient Boosting algorithm. This approach achieved an overall accuracy of 93.33%, and the only misclassification was the yellow sample assigned as green. The presented solution achieves good results and can be applied in industry applications related to tool condition monitoring.<\/jats:p>","DOI":"10.3390\/s23010448","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:08:59Z","timestamp":1672628939000},"page":"448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improved Drill State Recognition during Milling Process Using Artificial Intelligence"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2789-4732","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Kurek","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8583-9873","authenticated-orcid":false,"given":"Artur","family":"Krupa","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3157-3381","authenticated-orcid":false,"given":"Izabella","family":"Antoniuk","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9496-1712","authenticated-orcid":false,"given":"Arlan","family":"Akhmet","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4785-0817","authenticated-orcid":false,"given":"Ulan","family":"Abdiomar","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1567-879X","authenticated-orcid":false,"given":"Micha\u0142","family":"Bukowski","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0383-0897","authenticated-orcid":false,"given":"Karol","family":"Szymanowski","sequence":"additional","affiliation":[{"name":"Department of Mechanical Processing of Wood, Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s001700050123","article-title":"Commercial tool condition monitoring systems","volume":"15","author":"Jemielniak","year":"1999","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ijrmhm.2004.01.007","article-title":"Study on wear mechanisms and grain effects of PCD tool in machining laminated flooring","volume":"22","author":"Bai","year":"2004","journal-title":"Int. J. Refract. Met. Hard Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3379","DOI":"10.15376\/biores.14.2.3379-3388","article-title":"Use of cutting force and vibro-acoustic signals in tool wear monitoring based on multiple regression technique for compreg milling","volume":"14","author":"Szymanowski","year":"2019","journal-title":"Bioresources"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/S0890-6955(99)00084-X","article-title":"On-line metal cutting tool condition monitoring.: I: Force and vibration analyses","volume":"40","author":"Dimla","year":"2000","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1006\/mssp.1999.1286","article-title":"The adaptability of a tool wear monitoring system under changing cutting conditions","volume":"14","author":"Silva","year":"2000","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00170-011-3504-2","article-title":"Tool condition monitoring based on numerous signal features","volume":"59","author":"Jemielniak","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"425","DOI":"10.15376\/biores.3.2.425-436","article-title":"Drill wear during the boring of particle board: A multi-factor analysis including effects of mineral contaminants","volume":"3","author":"Porankiewicz","year":"2008","journal-title":"BioResources"},{"key":"ref_8","first-page":"1","article-title":"Tepienie sie ostrzy i jakosc przedmiotu obrabianego w skrawaniu plyt wiorowych","volume":"241","author":"Porankiewicz","year":"2003","journal-title":"Rocz. Akad. Rol. Pozn. Rozpr. Nauk."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9611","DOI":"10.15376\/biores.15.4.9611-9624","article-title":"Time-efficient approach to drill condition monitoring based on images of holes drilled in melamine faced chipboard","volume":"15","author":"Jegorowa","year":"2020","journal-title":"BioResources"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s00226-020-01245-7","article-title":"Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard","volume":"55","author":"Jegorowa","year":"2021","journal-title":"Wood Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Borz, S.A., Forkuo, G.O., Oprea-Sorescu, O., and Proto, A.R. (2022). Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines. Forests, 13.","DOI":"10.3390\/f13071115"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bedelean, B., Ispas, M., R\u0103c\u0103\u0219an, S., and Baba, M.N. (2022). Optimization of Wood Particleboard Drilling Operating Parameters by Means of the Artificial Neural Network Modeling Technique and Response Surface Methodology. Forests, 13.","DOI":"10.3390\/f13071045"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"G\u00f3rski, J. (2022). The Review of New Scientific Developments in Drilling in Wood-Based Panels with Particular Emphasis on the Latest Research Trends in Drill Condition Monitoring. Forests, 13.","DOI":"10.3390\/f13020242"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kurek, J., Wieczorek, G., Kruk, B.S.M., Jegorowa, A., and Osowski, S. (2017, January 1\u201313). Transfer learning in recognition of drill wear using convolutional neural network. Proceedings of the 2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE), Kutn\u00e1 Hora, Czech Republic.","DOI":"10.1109\/CPEE.2017.8093087"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kurek, J., Wieczorek, G., Swiderski, B., Kruk, M., Jegorowa, A., and Gorski, J. (2018, January 9\u201312). Automatic identification of drill condition during drilling process in standard laminated chipboard with the use of long short-term memory (LSTM). Proceedings of the 19th International Conference Computational Problems of Electrical Engineering, Banska Stiavnica, Slovak Republic.","DOI":"10.1109\/CPEE.2018.8506809"},{"key":"ref_16","first-page":"V003T04A050","article-title":"Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling","volume":"Volume 50749","author":"Kothuru","year":"2017","journal-title":"Proceedings of the International Manufacturing Science and Engineering Conference"},{"key":"ref_17","unstructured":"(2022, November 05). Classifier Implementing the k-Nearest Neighbors Vote. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.html."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"88","DOI":"10.18201\/ijisae.2019252786","article-title":"Performance analysis of ANN and Naive Bayes classification algorithm for data classification","volume":"7","author":"Saritas","year":"2019","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_20","unstructured":"(2022, November 05). Gaussian Naive Bayes Classifier. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.naive_bayes.GaussianNB.html."},{"key":"ref_21","unstructured":"(2022, November 05). Naive Bayes Classifier for Multinomial Models. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.naive_bayes.MultinomialNB.html?highlight=multinomialnb#sklearn.naive_bayes.MultinomialNB."},{"key":"ref_22","unstructured":"Chubarian, K., and Tur\u00e1n, G. (2020, January 6\u20138). Interpretability of Bayesian Network Classifiers: OBDD Approximation and Polynomial Threshold Functions. Proceedings of the ISAIM, Fort Lauderdale, FL, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ketkar, N. (2017). Stochastic gradient descent. Deep Learning with Python, Springer.","DOI":"10.1007\/978-1-4842-2766-4"},{"key":"ref_25","unstructured":"(2022, November 05). Linear Classifiers with SGD Training. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.SGDClassifier.html."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10462-011-9272-4","article-title":"Decision trees: A recent overview","volume":"39","author":"Kotsiantis","year":"2013","journal-title":"Artif. Intell. Rev."},{"key":"ref_27","unstructured":"(2022, November 05). A Decision Tree Classifier. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.tree.DecisionTreeClassifier.html."},{"key":"ref_28","unstructured":"(2022, November 05). A Random Forest Classifier. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A random forest guided tour","volume":"25","author":"Biau","year":"2016","journal-title":"Test"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_31","unstructured":"Breiman, L. (1997). Arcing the Edge, Statistics Department, University of California. Technical Report, Technical Report 486."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_33","unstructured":"(2022, November 05). Gradient Boosting for Classification. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.GradientBoostingClassifier.html."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors)","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_37","unstructured":"(2022, November 05). Python API Reference of Xgboost. Available online: https:\/\/xgboost.readthedocs.io\/en\/stable\/python\/python_api.html."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/mice.12564","article-title":"Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine","volume":"36","author":"Chun","year":"2021","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_39","unstructured":"(2022, November 05). LightGBM Classifier. Available online: https:\/\/lightgbm.readthedocs.io\/en\/latest\/pythonapi\/lightgbm.LGBMClassifier.html."},{"key":"ref_40","unstructured":"Yang, Y., Li, J., and Yang, Y. (2015, January 18\u201320). The research of the fast SVM classifier method. Proceedings of the 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"61","DOI":"10.7551\/mitpress\/1113.003.0008","article-title":"Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods","volume":"10","author":"Platt","year":"2000","journal-title":"Adv. Large Margin Classif."},{"key":"ref_42","unstructured":"(2022, November 05). C-Support Vector Classification. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.svm.SVC.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:55:13Z","timestamp":1760118913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010448"],"URL":"https:\/\/doi.org\/10.3390\/s23010448","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,1]]}}}