{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:28:07Z","timestamp":1773800887760,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2014,11,14]],"date-time":"2014-11-14T00:00:00Z","timestamp":1415923200000},"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>Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.<\/jats:p>","DOI":"10.3390\/s141121588","type":"journal-article","created":{"date-parts":[[2014,11,17]],"date-time":"2014-11-17T03:15:21Z","timestamp":1416194121000},"page":"21588-21602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Guofeng","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,  Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinwei","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,  Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhimeng","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,  Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1016\/j.ijmachtools.2004.04.003","article-title":"Multiclassification of tool wear with support vector machine by manufacturing loss consideration","volume":"44","author":"Sun","year":"2004","journal-title":"Int. J. Mach. Tool. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1016\/j.ymssp.2006.07.016","article-title":"Tool wear predictive model based on least squares support vector machines","volume":"21","author":"Shi","year":"2007","journal-title":"Mech. Syst. Sign. Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0890-6955(01)00103-1","article-title":"Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools","volume":"42","author":"Nadgir","year":"2002","journal-title":"Int. J. Mach. Tool. Manuf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0893-6080(98)00137-3","article-title":"Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network","volume":"12","author":"Kuo","year":"1999","journal-title":"Neural Netw."},{"key":"ref_5","first-page":"355","article-title":"On-line monitoring of tool wear in turning using a neural network","volume":"12","author":"Choudhury","year":"1999","journal-title":"Int. J. Mach. Tool. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1006\/mssp.1997.0123","article-title":"Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors","volume":"12","author":"Silva","year":"1998","journal-title":"Mech. Syst. Sign. Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.engappai.2012.10.015","article-title":"Tool wear state recognition based on linear chain conditional random field model","volume":"26","author":"Wang","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","unstructured":"Atlas, L., Ostendorf, M., and Bernard, G.D. (2000, January 5\u20139). Hidden Markov models for monitoring machining tool-wear. Seattle, WA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1115\/1.1475320","article-title":"Hidden Markov model-based tool wear monitoring in turning","volume":"124","author":"Wang","year":"2002","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.aca.2007.05.030","article-title":"How to avoid over-fitting in multivariate calibration\u2014the conventional validation approach and an alternative","volume":"595","author":"Faber","year":"2007","journal-title":"Anal. Chim. Acta"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1016\/j.ymssp.2007.01.004","article-title":"Cutting force based real-time estimation of tool wear in face milling using a combination of signal processing techniques","volume":"21","author":"Bhattacharyya","year":"2007","journal-title":"Mech. Syst. Sign. Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.advengsoft.2010.12.002","article-title":"Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks","volume":"42","author":"Kaya","year":"2011","journal-title":"Adv. Eng. Softw."},{"key":"ref_13","unstructured":"Cui, Y.J. (2008). Tool Wear Monitoring for Milling by Tracking Cutting Force Model Coefficients, University of New Hampshire."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1016\/S0890-6955(99)00020-6","article-title":"On-line monitoring of flank wear in turning with multilayered feed-forward neural network","volume":"39","author":"Liu","year":"1999","journal-title":"Int. J. Mach. Tool. Manuf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-based classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Aryif. Intell. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.eswa.2010.06.048","article-title":"A comparative assessment of ensemble learning for credit scoring","volume":"38","author":"Wang","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_17","first-page":"103","article-title":"On diversity and accuracy of homogeneous and heterogeneous ensembles","volume":"4","author":"Bian","year":"2007","journal-title":"Int. J. Intell. Syst."},{"key":"ref_18","first-page":"1751","article-title":"Combining information extraction systems using voting and stacked generalization","volume":"6","author":"Sigletos","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (2000). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1016\/j.ijmachtools.2007.04.013","article-title":"An approach based on current and sound signals for in-process tool wear monitoring","volume":"47","author":"Salgado","year":"2007","journal-title":"Int. J. Mach. Tool. Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4728","DOI":"10.1016\/j.jmatprotec.2008.11.038","article-title":"Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion","volume":"209","author":"Binsaeid","year":"2009","journal-title":"J. Mater. Process. Technol."},{"key":"ref_22","unstructured":"Powell, M.J.D. (1987). Radial Basis Functions for Multivariable Interpolation: A Review, Clarendon Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/59.867165","article-title":"Radial basis function (RBF) network adaptive power system stabilizer","volume":"15","author":"Segal","year":"2000","journal-title":"IEEE. Trans. Power. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A tutorial on hidden Markov models and selected applications in speech recognition","volume":"77","author":"Rabiner","year":"1989","journal-title":"Proc. IEEE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_26","unstructured":"Yu, S.X. (2003). Feature Selection and Classifier Ensembles: A Study on Hyperspectral Remote Sensing Data, Scientific Literature Digital Library and Search Engine."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.sna.2013.09.023","article-title":"Hybrid learning based Gaussian ARTMAP network for tool condition monitoring using selected force harmonic features","volume":"203","author":"Wang","year":"2013","journal-title":"Sens. Actuators A Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE. Trans. Pattern Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1103\/PhysRevE.55.811","article-title":"Statistical mechanics of ensemble learning","volume":"40","author":"Krogh","year":"1997","journal-title":"Phys. Rev. E"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble Methods in Machine Learning, Multiple Classifier Systems, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Oliveira, J.F.L., and Ludermir, T.B. (2011, January 7\u20139). Homogeneous Ensemble Selection through Hierarchical Clustering with a Modified Artificial Fish Swarm Algorithm. Boca Raton, FL, USA.","DOI":"10.1109\/ICTAI.2011.34"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/11\/21588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:09:24Z","timestamp":1760216964000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/11\/21588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,14]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2014,11]]}},"alternative-id":["s141121588"],"URL":"https:\/\/doi.org\/10.3390\/s141121588","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,11,14]]}}}