{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T01:13:35Z","timestamp":1771895615096,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,11,30]],"date-time":"2017-11-30T00:00:00Z","timestamp":1512000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Labor, Family and Social Protection, Romania, co-financed by the European Social Fund \u2013 \u201cInvesting in people\u201d","award":["POSDRU 107\/1.5\/S\/77265 (2010)"],"award-info":[{"award-number":["POSDRU 107\/1.5\/S\/77265 (2010)"]}]},{"name":"R &amp; D center \u201cCercetare Dezvoltare Agora, Oradea\u201d.","award":["1\/03.11.2017"],"award-info":[{"award-number":["1\/03.11.2017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Flexibility of manufacturing systems is an essential factor in maintaining the competitiveness of industrial production. Flexibility can be defined in several ways and according to several factors, but in order to obtain adequate results in implementing a flexible manufacturing system able to compete on the market, a high level of autonomy (free of human intervention) of the manufacturing system must be achieved. There are many factors that can disturb the production process and reduce the autonomy of the system, because of the need of human intervention to overcome these disturbances. One of these factors is tool wear. The aim of this paper is to present an experimental study on the possibility to determine the state of tool wear in a flexible manufacturing cell environment, using image acquisition and processing methods.<\/jats:p>","DOI":"10.3390\/sym9120296","type":"journal-article","created":{"date-parts":[[2017,11,30]],"date-time":"2017-11-30T12:02:51Z","timestamp":1512043371000},"page":"296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Tool-Wear Analysis Using Image Processing of the Tool Flank"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1523-4718","authenticated-orcid":false,"given":"Ovidiu","family":"Moldovan","sequence":"first","affiliation":[{"name":"Department of Mechatronics, University of Oradea, 410087 Oradea, Romania"}]},{"given":"Simona","family":"Dzitac","sequence":"additional","affiliation":[{"name":"Department of Energy Engineering, University of Oradea, 410087 Oradea, Romania"}]},{"given":"Ioan","family":"Moga","sequence":"additional","affiliation":[{"name":"Department of Mechatronics, University of Oradea, 410087 Oradea, Romania"}]},{"given":"Tiberiu","family":"Vesselenyi","sequence":"additional","affiliation":[{"name":"Department of Mechatronics, University of Oradea, 410087 Oradea, Romania"}]},{"given":"Ioan","family":"Dzitac","sequence":"additional","affiliation":[{"name":"Department of Mathematics\u2014Computer Science, Aurel Vlaicu University of Arad, 310025 Arad, Romania"},{"name":"Department of Social Sciences, Agora University, 410526 Oradea, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s00170-014-6013-2","article-title":"Condition-based tool management for small batch production","volume":"74","author":"Denkena","year":"2014","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"\u00c7elik, Y.H., Kilickap, E., and G\u00fcney, M.J. (2016). Investigation of cutting parameters affecting on tool wear and surface roughness in dry turning of Ti-6Al-4V using CVD and PVD coated tools. J. Braz. Soc. Mech. Sci. Eng., 1\u20139.","DOI":"10.1007\/s40430-016-0607-6"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jain, V., and Raj, T. (2016). Tool life management of unmanned production system based on surface roughness by ANFIS. Int. J. Syst. Assur. Eng. Manag., 1\u201310.","DOI":"10.1007\/s13198-016-0450-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1007\/s00170-015-7441-3","article-title":"Tool condition monitoring by SVM classification of machined surface images in turning","volume":"83","author":"Bhat","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s10845-014-0907-6","article-title":"An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations","volume":"27","author":"Huang","year":"2016","journal-title":"J. Intell. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, N., Chen, Y., and Kong, D. (2016). Force-based tool condition monitoring for turning process using v-support vector regression. Int. J. Adv. Manuf. Technol., 1\u201311.","DOI":"10.1007\/s00170-016-9735-5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.cirpj.2016.06.003","article-title":"Tool wear and surface quality assessment of CFRP trimming using fractal analyses of the cutting force signals","volume":"16","author":"Rimpault","year":"2017","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s12206-013-0982-1","article-title":"Tool wear prediction considering uncovered data based on partial least square regression","volume":"28","author":"Wang","year":"2014","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.1007\/s12206-016-1005-9","article-title":"Milling tool wear diagnosis by feed motor current signal using an artificial neural network","volume":"30","author":"Khajavi","year":"2016","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1007\/s13369-015-1802-y","article-title":"Online prediction of tool wear in the milling of the AISI P20 steel through electric power of the main motor","volume":"40","author":"Arruda","year":"2015","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3103\/S1068798X15120163","article-title":"Influence of machine-tool dynamics on the tool wear","volume":"35","author":"Postnov","year":"2015","journal-title":"Russ. Eng. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00170-015-7317-6","article-title":"Tool wear predictability estimation in milling based on multi-sensorial data","volume":"82","author":"Stavropoulos","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.ijmecsci.2016.09.013","article-title":"Evaluation for tool flank wear and its influences on surface roughness in ultra-precision raster fly cutting","volume":"118","author":"Zhang","year":"2016","journal-title":"Int. J. Mech. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1007\/s12206-015-0834-2","article-title":"3D cutting tool-wear monitoring in the process","volume":"29","author":"Cerce","year":"2015","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_15","unstructured":"Garcia-Ord\u00e1s, M.T., Alegre, E., and Gonz\u00e1lez-Castro, V. (2016). A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. Int. J. Adv. Manuf. Technol., 1\u201315."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Javed, K., Gouriveau, R., and Li, X. (2016). Tool wear monitoring and prognostics challenges: A comparison of connectionist methods toward an adaptive ensemble model. J. Intell. Manuf., 1\u201318.","DOI":"10.1007\/s10845-016-1221-2"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kong, D., Chen, Y., and Li, N. (2016). Tool wear monitoring based on kernel principal component analysis and v-support vector regression. Int. J. Adv. Manuf. Technol., 1\u201316.","DOI":"10.1007\/s00170-016-9070-x"},{"key":"ref_18","first-page":"279","article-title":"Study of tool wear mechanisms and mathematical modeling of flank wear during machining of Ti alloy (Ti6Al4V)","volume":"96","author":"Chetan","year":"2015","journal-title":"J. Inst. Eng. (India)"},{"key":"ref_19","first-page":"517","article-title":"Modeling of principal flank wear: An empirical approach combining the effect of tool","volume":"97","author":"Mia","year":"2016","journal-title":"Environ. Workpiece Hardness J. Inst. Eng. (India)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10845-013-0867-2","article-title":"Prediction of drill flank wear using ensemble of co-evolutionary particle swarm optimization based-selective neural network ensembles","volume":"27","author":"Yang","year":"2016","journal-title":"J. Intell. Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"321","DOI":"10.3103\/S1068798X16040122","article-title":"Influence of rigid and frictional kinematic linkages in tool\u2013workpiece contact on the uniformity of tool wear","volume":"36","author":"Muratov","year":"2016","journal-title":"Russ. Eng. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s12541-015-0215-x","article-title":"Tool wear analysis on coated and uncoated carbide tools in inconel machining","volume":"16","author":"Park","year":"2015","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1007\/s11665-016-2438-0","article-title":"Influence of cutting parameters and tool wear on the surface integrity of cobalt-based stellite 6 alloy when machined under a dry cutting environment","volume":"26","author":"Yingfei","year":"2017","journal-title":"J. Mater. Eng. Perform."},{"key":"ref_24","unstructured":"Mathworks\u00ae (2017, November 04). MATLAB, Neural Network Toolbox, Image Processing Toolbox, R2016b, User\u2019s Guide. Available online: https:\/\/www.mathworks.com\/help\/."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006, January 4\u20137). Greedy layer-wise training of deep networks. Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.7551\/mitpress\/7503.003.0024"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/12\/296\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:52:01Z","timestamp":1760208721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/12\/296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,30]]},"references-count":26,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["sym9120296"],"URL":"https:\/\/doi.org\/10.3390\/sym9120296","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,30]]}}}