{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:44:17Z","timestamp":1776177857019,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004442","name":"Narodowym Centrum Nauki","doi-asserted-by":"publisher","award":["UMO-2020\/37\/K\/ST8\/02795"],"award-info":[{"award-number":["UMO-2020\/37\/K\/ST8\/02795"]}],"id":[{"id":"10.13039\/501100004442","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004382","name":"Polska Akademia Nauk","doi-asserted-by":"publisher","award":["PPN\/ULM\/2020\/1\/00121"],"award-info":[{"award-number":["PPN\/ULM\/2020\/1\/00121"]}],"id":[{"id":"10.13039\/501100004382","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.<\/jats:p>","DOI":"10.1007\/s10845-023-02074-8","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T17:03:26Z","timestamp":1673543006000},"page":"757-775","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4016-8970","authenticated-orcid":false,"given":"Nimel Sworna","family":"Ross","sequence":"first","affiliation":[]},{"given":"Paul T.","family":"Sheeba","sequence":"additional","affiliation":[]},{"given":"C. Sherin","family":"Shibi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0777-1559","authenticated-orcid":false,"given":"Munish Kumar","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Mehmet Erdi","family":"Korkmaz","sequence":"additional","affiliation":[]},{"given":"Vishal S","family":"Sharma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"issue":"September","key":"2074_CR1","doi-asserted-by":"publisher","first-page":"108649","DOI":"10.1016\/j.measurement.2020.108649","volume":"173","author":"P Abhishek Dhananjay","year":"2021","unstructured":"Abhishek Dhananjay, P., & Jegadeeshwaran, R. (2021). A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC). Measurement: Journal of the International Measurement Confederation, 173(September), 108649. https:\/\/doi.org\/10.1016\/j.measurement.2020.108649.","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"issue":"9\u201312","key":"2074_CR2","doi-asserted-by":"publisher","first-page":"3217","DOI":"10.1007\/s00170-018-2420-0","volume":"98","author":"F Aghazadeh","year":"2018","unstructured":"Aghazadeh, F., Tahan, A., & Thomas, M. (2018). Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. International Journal of Advanced Manufacturing Technology, 98(9\u201312), 3217\u20133227. https:\/\/doi.org\/10.1007\/s00170-018-2420-0.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2074_CR3","doi-asserted-by":"publisher","first-page":"105659","DOI":"10.1109\/ACCESS.2020.2998808","volume":"8","author":"R Ashraf","year":"2020","unstructured":"Ashraf, R., Habib, M. A., Akram, M., Latif, M. A., Malik, M. S. A., Awais, M., et al. (2020). Deep convolution neural network for Big Data Medical Image classification. Ieee Access : Practical Innovations, Open Solutions, 8, 105659\u2013105670. https:\/\/doi.org\/10.1109\/ACCESS.2020.2998808.","journal-title":"Ieee Access : Practical Innovations, Open Solutions"},{"issue":"1","key":"2074_CR4","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/S0952-1976(02)00004-0","volume":"15","author":"M Balazinski","year":"2002","unstructured":"Balazinski, M., Czogala, E., Jemielniak, K., & Leski, J. (2002). Tool condition monitoring using artificial intelligence methods. Engineering Applications of Artificial Intelligence, 15(1), 73\u201380.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"2074_CR5","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s10845-013-0774-6","volume":"26","author":"T Benkedjouh","year":"2015","unstructured":"Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26(2), 213\u2013223. https:\/\/doi.org\/10.1007\/s10845-013-0774-6.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2074_CR6","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.promfg.2020.05.134","volume":"48","author":"T Bergs","year":"2020","unstructured":"Bergs, T., Holst, C., Gupta, P., & Augspurger, T. (2020). Digital image processing with deep learning for automated cutting tool wear detection. Procedia Manufacturing, 48, 947\u2013958. https:\/\/doi.org\/10.1016\/j.promfg.2020.05.134.","journal-title":"Procedia Manufacturing"},{"key":"2074_CR7","doi-asserted-by":"publisher","unstructured":"Bustillo, A., Urbikain, G., Perez, J. M., Pereira, O. M., Lopez, L. N., & Lacalle, D. (2018). Smart optimization of a friction-drilling process based on boosting ensembles. Journal of Manufacturing Systems, (November 2017), 1\u201314. https:\/\/doi.org\/10.1016\/j.jmsy.2018.06.004","DOI":"10.1016\/j.jmsy.2018.06.004"},{"issue":"3","key":"2074_CR8","doi-asserted-by":"publisher","first-page":"47","DOI":"10.52795\/mateca.1020081","volume":"2","author":"A \u00c7ak\u0131r \u015eencan","year":"2021","unstructured":"\u00c7ak\u0131r \u015eencan, A., \u00c7elik, M., & Selayet Sara\u00e7, E. N. (2021). The Effect of Nanofluids used in the MQL technique Applied in turning process on Machining performance: a review on eco-friendly machining. Manufacturing Technologies and Applications, 2(3), 47\u201366. https:\/\/doi.org\/10.52795\/mateca.1020081.","journal-title":"Manufacturing Technologies and Applications"},{"issue":"8","key":"2074_CR9","doi-asserted-by":"publisher","first-page":"8055","DOI":"10.1007\/s13369-021-05626-3","volume":"46","author":"R \u00c7ak\u0131ro\u011flu","year":"2021","unstructured":"\u00c7ak\u0131ro\u011flu, R. (2021). Machinability Analysis of Inconel 718 Superalloy with AlTiN-Coated Carbide Tool under different cutting environments. Arabian Journal for Science and Engineering, 46(8), 8055\u20138073. https:\/\/doi.org\/10.1007\/s13369-021-05626-3.","journal-title":"Arabian Journal for Science and Engineering"},{"key":"2074_CR10","doi-asserted-by":"publisher","unstructured":"Cao, X., Chen, B., Yao, B., & Zhuang, S. (2019). An intelligent milling toolwear monitoring methodology based on convolutional neural network with derived wavelet frames coefficient. Applied Sciences (Switzerland), 9(18), https:\/\/doi.org\/10.3390\/app9183912.","DOI":"10.3390\/app9183912"},{"issue":"9\u201310","key":"2074_CR11","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1016\/j.wear.2004.07.005","volume":"257","author":"I Ciftci","year":"2004","unstructured":"Ciftci, I., Turker, M., & Seker, U. (2004). CBN cutting tool wear during machining of particulate reinforced MMCs. Wear, 257(9\u201310), 1041\u20131046. https:\/\/doi.org\/10.1016\/j.wear.2004.07.005.","journal-title":"Wear"},{"issue":"1","key":"2074_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.52795\/mateca.1058771","volume":"3","author":"R Demirs\u00f6z","year":"2022","unstructured":"Demirs\u00f6z, R., & Boy, M. (2022). Measurement and evaluation of machinability characteristics in turning of train wheel steel via CVD Coated-RCMX Carbide Tool. Manufacturing Technologies and Applications, 3(1), 1\u201313. https:\/\/doi.org\/10.52795\/mateca.1058771.","journal-title":"Manufacturing Technologies and Applications"},{"key":"2074_CR13","doi-asserted-by":"publisher","first-page":"106311","DOI":"10.1016\/j.asoc.2020.106311","volume":"93","author":"N Dong","year":"2020","unstructured":"Dong, N., Zhao, L., Wu, C. H., & Chang, J. F. (2020). Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing, 93, 106311. https:\/\/doi.org\/10.1016\/j.asoc.2020.106311.","journal-title":"Applied Soft Computing"},{"key":"2074_CR14","doi-asserted-by":"publisher","unstructured":"Duc, T. M., Long, T. T., & Tuan, N. M. (2021). Performance investigation of mql parameters using nano cutting fluids in hard milling. Fluids, 6(7), https:\/\/doi.org\/10.3390\/fluids6070248.","DOI":"10.3390\/fluids6070248"},{"key":"2074_CR15","doi-asserted-by":"publisher","unstructured":"Dutta, S., Pal, S. K., & Sen, R. (2016). Tool condition monitoring in turning by applying machine vision. Journal of Manufacturing Science and Engineering Transactions of the ASME, 138(5), https:\/\/doi.org\/10.1115\/1.4031770.","DOI":"10.1115\/1.4031770"},{"key":"2074_CR16","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.measurement.2015.03.037","volume":"70","author":"A Gok","year":"2015","unstructured":"Gok, A. (2015). A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement: Journal of the International Measurement Confederation, 70, 100\u2013109. https:\/\/doi.org\/10.1016\/j.measurement.2015.03.037.","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"issue":"5\u20138","key":"2074_CR17","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s00170-013-5075-x","volume":"69","author":"A Gok","year":"2013","unstructured":"Gok, A., Gologlu, C., & Demirci, H. I. (2013). Cutting parameter and tool path style effects on cutting force and tool deflection in machining of convex and concave inclined surfaces. International Journal of Advanced Manufacturing Technology, 69(5\u20138), 1063\u20131078. https:\/\/doi.org\/10.1007\/s00170-013-5075-x.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2074_CR18","doi-asserted-by":"crossref","unstructured":"Gupta, M. K., Demirs\u00f6z, R., Korkmaz, M. E., & Ross, N. S. (2023). Wear and Friction Mechanism of Stainless Steel 420 Under Various Lubrication Conditions: A Tribological Assessment With Ball on Flat Test.Journal of Tribology, 145(4).","DOI":"10.1115\/1.4056423"},{"issue":"2","key":"2074_CR19","first-page":"323","volume":"35","author":"YAKA Harun","year":"2017","unstructured":"Harun, Y. A. K. A., & Halil, D. E. M. R., A. G (2017). Optimization of the cutting parameters affecting the Surface Roughness on Free Form Surfaces. Sigma Journal of Engineering and Natural Sciences, 35(2), 323\u2013331.","journal-title":"Sigma Journal of Engineering and Natural Sciences"},{"key":"2074_CR20","unstructured":"Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T. (2017). MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications."},{"issue":"1","key":"2074_CR21","doi-asserted-by":"publisher","first-page":"48","DOI":"10.5604\/01.3001.0013.0448","volume":"19","author":"K Jemielniak","year":"2019","unstructured":"Jemielniak, K. (2019). Contemporary challenges in tool condition monitoring. Journal of Machine Engineering, 19(1), 48\u201361. https:\/\/doi.org\/10.5604\/01.3001.0013.0448.","journal-title":"Journal of Machine Engineering"},{"issue":"1\u20133","key":"2074_CR22","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jmatprotec.2007.05.030","volume":"196","author":"AA Khan","year":"2008","unstructured":"Khan, A. A., & Ahmed, M. I. (2008). Improving tool life using cryogenic cooling. Journal of Materials Processing Technology, 196(1\u20133), 149\u2013154. https:\/\/doi.org\/10.1016\/j.jmatprotec.2007.05.030.","journal-title":"Journal of Materials Processing Technology"},{"key":"2074_CR23","doi-asserted-by":"publisher","unstructured":"Kim, J. S., Kim, J. W., Kim, Y. C., & Lee, S. W. (2016, June). Experimental Study on Environmentally-Friendly Micro End-Milling Process of Ti-6Al-4V Using Nanofluid Minimum Quantity Lubrication With Chilly Gas. Virginia, USA, ASME 2016 11th International Manufacturing Science and Engineering Conference. Virginia, USA. https:\/\/doi.org\/10.1115\/MSEC2016-8748","DOI":"10.1115\/MSEC2016-8748"},{"key":"2074_CR24","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1016\/j.promfg.2019.06.096","volume":"34","author":"A Kothuru","year":"2019","unstructured":"Kothuru, A., Nooka, S. P., & Liu, R. (2019). Application of deep visualization in CNN-based tool condition monitoring for end milling. Procedia Manufacturing, 34, 995\u20131004. https:\/\/doi.org\/10.1016\/j.promfg.2019.06.096.","journal-title":"Procedia Manufacturing"},{"key":"2074_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.02.017","author":"GM Krolczyk","year":"2019","unstructured":"Krolczyk, G. M., Maruda, R. W., Krolczyk, J. B., Wojciechowski, S., Mia, M., Nieslony, P., & Budzik, G. (2019). Ecological trends in machining as a key factor in sustainable production \u2013 a review. Journal of Cleaner Production. https:\/\/doi.org\/10.1016\/j.jclepro.2019.02.017.","journal-title":"Journal of Cleaner Production"},{"key":"2074_CR26","doi-asserted-by":"publisher","unstructured":"Kumar, M. P., Dutta, S., & Murmu, N. C. (2021). Tool wear classification based on machined surface images using convolution neural networks. Sadhana - Academy Proceedings in Engineering Sciences, 46(3). https:\/\/doi.org\/10.1007\/s12046-021-01654-9","DOI":"10.1007\/s12046-021-01654-9"},{"key":"2074_CR27","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.measurement.2015.10.029","volume":"79","author":"L Li","year":"2016","unstructured":"Li, L., & An, Q. (2016). An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis. Measurement: Journal of the International Measurement Confederation, 79, 44\u201352. https:\/\/doi.org\/10.1016\/j.measurement.2015.10.029.","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"key":"2074_CR28","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ymssp.2019.06.021","volume":"131","author":"W Li","year":"2019","unstructured":"Li, W., & Liu, T. (2019). Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling. Mechanical Systems and Signal Processing, 131, 689\u2013702. https:\/\/doi.org\/10.1016\/j.ymssp.2019.06.021.","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"3","key":"2074_CR29","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3311\/PPtr.11480","volume":"47","author":"C Lin","year":"2019","unstructured":"Lin, C., Li, L., Luo, W., Wang, K. C. P., & Guo, J. (2019). Transfer learning based traffic sign recognition using inception-v3 model. Periodica Polytechnica Transportation Engineering, 47(3), 242\u2013250. https:\/\/doi.org\/10.3311\/PPtr.11480.","journal-title":"Periodica Polytechnica Transportation Engineering"},{"issue":"2","key":"2074_CR30","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s40436-013-0025-2","volume":"1","author":"WC Lu","year":"2013","unstructured":"Lu, W. C., Ji, X. B., Li, M. J., Liu, L., Yue, B. H., & Zhang, L. M. (2013). Using support vector machine for materials design. Advances in Manufacturing, 1(2), 151\u2013159. https:\/\/doi.org\/10.1007\/s40436-013-0025-2.","journal-title":"Advances in Manufacturing"},{"key":"2074_CR31","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1007\/s13198-017-0637-1","volume":"8","author":"CK Madhusudana","year":"2017","unstructured":"Madhusudana, C. K., Kumar, H., & Narendranath, S. (2017). Face milling tool condition monitoring using sound signal. International Journal of System Assurance Engineering and Management, 8, 1643\u20131653. https:\/\/doi.org\/10.1007\/s13198-017-0637-1.","journal-title":"International Journal of System Assurance Engineering and Management"},{"key":"2074_CR32","doi-asserted-by":"publisher","unstructured":"Madhusudana, C. K., Kumar, H., & Narendranath, S. (2018). Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal. Materials Today: Proceedings, 5(5), 12035\u201312044. https:\/\/doi.org\/10.1016\/j.matpr.2018.02.178","DOI":"10.1016\/j.matpr.2018.02.178"},{"key":"2074_CR33","doi-asserted-by":"publisher","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., & Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sensing, 10(7), https:\/\/doi.org\/10.3390\/rs10071119.","DOI":"10.3390\/rs10071119"},{"issue":"March","key":"2074_CR34","doi-asserted-by":"publisher","first-page":"102145","DOI":"10.1016\/j.rcim.2021.102145","volume":"71","author":"M Marei","year":"2021","unstructured":"Marei, M., Zaatari, S., El, & Li, W. (2021). Transfer learning enabled convolutional neural networks for estimating health state of cutting tools. Robotics and Computer-Integrated Manufacturing, 71(March), 102145. https:\/\/doi.org\/10.1016\/j.rcim.2021.102145.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"9\u201312","key":"2074_CR35","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.1007\/s00170-019-04090-6","volume":"104","author":"G Mart\u00ednez-Arellano","year":"2019","unstructured":"Mart\u00ednez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104(9\u201312), 3647\u20133662.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2074_CR36","doi-asserted-by":"publisher","first-page":"108847","DOI":"10.1016\/j.measurement.2020.108847","volume":"171","author":"RW Maruda","year":"2021","unstructured":"Maruda, R. W., Wojciechowski, S., Szczotkarz, N., Legutko, S., Mia, M., Gupta, M. K., et al. (2021). Metrological analysis of surface quality aspects in minimum quantity cooling lubrication. Measurement, 171, 108847. https:\/\/doi.org\/10.1016\/j.measurement.2020.108847.","journal-title":"Measurement"},{"issue":"2","key":"2074_CR37","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s13369-015-1726-6","volume":"41","author":"RW Maruda","year":"2016","unstructured":"Maruda, R. W., Feldshtein, E., Legutko, S., & Krolczyk, G. M. (2016). Analysis of contact phenomena and Heat Exchange in the cutting Zone under Minimum Quantity cooling lubrication conditions. Arabian Journal for Science and Engineering, 41(2), 661\u2013668. https:\/\/doi.org\/10.1007\/s13369-015-1726-6.","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"4","key":"2074_CR38","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1007\/s12206-018-0313-7","volume":"32","author":"RW Maruda","year":"2018","unstructured":"Maruda, R. W., Krolczyk, G. M., Wojciechowski, S., Zak, K., Habrat, W., & Nieslony, P. (2018). Effects of extreme pressure and anti-wear additives on surface topography and tool wear during MQCL turning of AISI 1045 steel. Journal of Mechanical Science and Technology, 32(4), 1585\u20131591. https:\/\/doi.org\/10.1007\/s12206-018-0313-7.","journal-title":"Journal of Mechanical Science and Technology"},{"issue":"August","key":"2074_CR39","doi-asserted-by":"publisher","first-page":"100406","DOI":"10.1016\/j.measen.2022.100406","volume":"23","author":"Y Methkal","year":"2022","unstructured":"Methkal, Y., Algani, A., Ritonga, M., Bala, B. K., Saleh, M., Ansari, A., et al. (2022). Measurement: Sensors Machine learning in health condition check-up : an approach using Breiman \u2019 s random forest algorithm. Measurement: Sensors, 23(August), 100406. https:\/\/doi.org\/10.1016\/j.measen.2022.100406.","journal-title":"Measurement: Sensors"},{"key":"2074_CR40","doi-asserted-by":"publisher","unstructured":"Molitor, D. A., Kubik, C., Becker, M., Hetfleisch, R. H., Lyu, F., & Groche, P. (2022). Towards high-performance deep learning models in tool wear classification with generative adversarial networks. Journal of Materials Processing Technology, 302(December 2021), 117484. https:\/\/doi.org\/10.1016\/j.jmatprotec.2021.117484","DOI":"10.1016\/j.jmatprotec.2021.117484"},{"issue":"4","key":"2074_CR41","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/s11740-022-01113-2","volume":"16","author":"DA Molitor","year":"2022","unstructured":"Molitor, D. A., Kubik, C., Hetfleisch, R. H., & Groche, P. (2022). Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks. Production Engineering, 16(4), 481\u2013492. https:\/\/doi.org\/10.1007\/s11740-022-01113-2.","journal-title":"Production Engineering"},{"key":"2074_CR42","doi-asserted-by":"publisher","unstructured":"Muhammad, U., Wang, W., Chattha, S. P., & Ali, S. (2018). Pre-trained VGGNet Architecture for Remote-Sensing Image Scene Classification. Proceedings - International Conference on Pattern Recognition, 2018-Augus(August), 1622\u20131627. https:\/\/doi.org\/10.1109\/ICPR.2018.8545591","DOI":"10.1109\/ICPR.2018.8545591"},{"key":"2074_CR43","doi-asserted-by":"publisher","unstructured":"Naveen Venkatesh, S., Arun Balaji, P., Elangovan, M., Annamalai, K., Indira, V., Sugumaran, V., & Mahamuni, V. S. (2022). Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool. Computational Intelligence and Neuroscience, 2022. https:\/\/doi.org\/10.1155\/2022\/3205960","DOI":"10.1155\/2022\/3205960"},{"issue":"3","key":"2074_CR44","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1007\/s11668-019-00667-1","volume":"19","author":"S Nimel","year":"2019","unstructured":"Nimel, S., Ross, K., & Ganesh, M. (2019). Performance analysis of Machining Ti\u20136Al\u20134V under cryogenic CO2 using PVD-TiN Coated Tool. Journal of Failure Analysis and Prevention, 19(3), 821\u2013831. https:\/\/doi.org\/10.1007\/s11668-019-00667-1.","journal-title":"Journal of Failure Analysis and Prevention"},{"key":"2074_CR45","doi-asserted-by":"publisher","first-page":"108153","DOI":"10.1016\/j.measurement.2020.108153","volume":"167","author":"J Ou","year":"2021","unstructured":"Ou, J., Li, H., Huang, G., & Yang, G. (2021). Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine. Measurement: Journal of the International Measurement Confederation, 167, 108153. https:\/\/doi.org\/10.1016\/j.measurement.2020.108153.","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"issue":"16","key":"2074_CR46","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1080\/10426914.2021.1926497","volume":"36","author":"H Pek\u015fen","year":"2021","unstructured":"Pek\u015fen, H., & Kalyon, A. (2021). Optimization and measurement of flank wear and surface roughness via Taguchi based grey relational analysis. Materials and Manufacturing Processes, 36(16), 1865\u20131874. https:\/\/doi.org\/10.1080\/10426914.2021.1926497.","journal-title":"Materials and Manufacturing Processes"},{"key":"2074_CR47","doi-asserted-by":"publisher","first-page":"125580","DOI":"10.1016\/j.jclepro.2020.125580","volume":"288","author":"A Race","year":"2021","unstructured":"Race, A., Zwierzak, I., Secker, J., Walsh, J., Carrell, J., Slatter, T., & Maurotto, A. (2021). Environmentally sustainable cooling strategies in milling of SA516: Effects on surface integrity of dry, flood and MQL machining. Journal of Cleaner Production, 288, 125580. https:\/\/doi.org\/10.1016\/j.jclepro.2020.125580.","journal-title":"Journal of Cleaner Production"},{"key":"2074_CR48","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.jmapro.2021.08.045","volume":"70","author":"M Ramoni","year":"2021","unstructured":"Ramoni, M., Shanmugam, R., Ross, N. S., & Gupta, M. K. (2021). An experimental investigation of hybrid manufactured SLM based Al-Si10-Mg alloy under mist cooling conditions. Journal of Manufacturing Processes, 70, 225\u2013235.","journal-title":"Journal of Manufacturing Processes"},{"issue":"3","key":"2074_CR49","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1080\/10426914.2021.2001510","volume":"37","author":"NS Ross","year":"2022","unstructured":"Ross, N. S., Sheeba, P. T., Jebaraj, M., & Stephen, H. (2022). Milling performance assessment of Ti-6Al-4V under CO2 cooling utilizing coated AlCrN\/TiAlN insert. Materials and Manufacturing Processes, 37(3), 327\u2013341. https:\/\/doi.org\/10.1080\/10426914.2021.2001510.","journal-title":"Materials and Manufacturing Processes"},{"key":"2074_CR50","doi-asserted-by":"publisher","unstructured":"Serin, G., Ugur Gudelek, M., Murat Ozbayoglu, A., & Unver, H. O. (2017). Estimation of parameters for the free-form machining with deep neural network. In Proceedings \u2013 2017 IEEE International Conference on Big Data, Big Data 2017. https:\/\/doi.org\/10.1109\/BigData.2017.8258158","DOI":"10.1109\/BigData.2017.8258158"},{"issue":"5","key":"2074_CR51","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/S0890-6955(02)00266-3","volume":"43","author":"M Sortino","year":"2003","unstructured":"Sortino, M. (2003). Application of statistical filtering for optical detection of tool wear. International Journal of Machine Tools and Manufacture, 43(5), 493\u2013497.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2074_CR52","doi-asserted-by":"publisher","first-page":"101924","DOI":"10.1016\/j.rcim.2019.101924","volume":"64","author":"H Sun","year":"2020","unstructured":"Sun, H., Zhang, J., Mo, R., & Zhang, X. (2020). In-process tool condition forecasting based on a deep learning method. Robotics and Computer-Integrated Manufacturing, 64, 101924.","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"2074_CR53","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jmrt.2022.02.093","volume":"18","author":"V Vakharia","year":"2022","unstructured":"Vakharia, V., Vora, J., Khanna, S., Chaudhari, R., Shah, M., Pimenov, D. Y., et al. (2022). Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model. Journal of Materials Research and Technology, 18, 325\u2013337. https:\/\/doi.org\/10.1016\/j.jmrt.2022.02.093.","journal-title":"Journal of Materials Research and Technology"},{"issue":"July","key":"2074_CR54","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/j.precisioneng.2021.08.010","volume":"72","author":"D Wang","year":"2021","unstructured":"Wang, D., Hong, R., & Lin, X. (2021). A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning. Precision Engineering, 72(July), 847\u2013857. https:\/\/doi.org\/10.1016\/j.precisioneng.2021.08.010.","journal-title":"Precision Engineering"},{"key":"2074_CR55","doi-asserted-by":"publisher","unstructured":"Wang, Y., Wang, Y., Zheng, L., & Zhou, J. (2022). Online surface roughness prediction for Assembly Interfaces of Vertical tail integrating ToolWear under Variable cutting parameters. Sensors (Basel, Switzerland), 22(5), https:\/\/doi.org\/10.3390\/s22051991.","DOI":"10.3390\/s22051991"},{"issue":"5A","key":"2074_CR56","doi-asserted-by":"publisher","first-page":"49","DOI":"10.5923\/c.jmea.201601.09","volume":"6","author":"P Waydande","year":"2016","unstructured":"Waydande, P., Ambhore, N., & Chinchanikar, S. (2016). A review on Tool wear monitoring system. Journal of Mechanical Engineering and Automation, 6(5A), 49\u201353. https:\/\/doi.org\/10.5923\/c.jmea.201601.09.","journal-title":"Journal of Mechanical Engineering and Automation"},{"issue":"1","key":"2074_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-14372-x","volume":"12","author":"X Wei","year":"2022","unstructured":"Wei, X., Hossain, M. Z., & Ahmed, K. A. (2022). A ResNet attention model for classifying mosquitoes from wing-beating sounds. Scientific Reports, 12(1), 1\u201311. https:\/\/doi.org\/10.1038\/s41598-022-14372-x.","journal-title":"Scientific Reports"},{"issue":"18","key":"2074_CR58","doi-asserted-by":"publisher","first-page":"3817","DOI":"10.3390\/s19183817","volume":"19","author":"X Wu","year":"2019","unstructured":"Wu, X., Liu, Y., Zhou, X., & Mou, A. (2019). Automatic identification of Tool wear based on convolutional neural network in Face Milling process. Sensors (Basel, Switzerland), 19(18), 3817.","journal-title":"Sensors (Basel, Switzerland)"},{"key":"2074_CR59","doi-asserted-by":"publisher","unstructured":"Xia, X., Xu, C., & Nan, B. (2017). Inception-v3 for flower classification. 2017 2nd International Conference on Image, Vision and Computing, ICIVC 2017, 783\u2013787. https:\/\/doi.org\/10.1109\/ICIVC.2017.7984661","DOI":"10.1109\/ICIVC.2017.7984661"},{"issue":"7","key":"2074_CR60","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1080\/02664763.2013.872233","volume":"41","author":"ZS Ye","year":"2014","unstructured":"Ye, Z. S., Li, J. G., & Zhang, M. (2014). Application of ridge regression and factor analysis in design and production of alloy wheels. Journal of Applied Statistics, 41(7), 1436\u20131452. https:\/\/doi.org\/10.1080\/02664763.2013.872233.","journal-title":"Journal of Applied Statistics"},{"issue":"2","key":"2074_CR61","doi-asserted-by":"publisher","first-page":"10","DOI":"10.52795\/mateca.940261","volume":"2","author":"H Yurtkuran","year":"2021","unstructured":"Yurtkuran, H. (2021). An evaluation on machinability characteristics of Titanium and Nickel Based Superalloys used in Aerospace Industry. Manufacturing Technologies and Applications, 2(2), 10\u201329. https:\/\/doi.org\/10.52795\/mateca.940261.","journal-title":"Manufacturing Technologies and Applications"},{"issue":"19","key":"2074_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app10196916","volume":"10","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Han, C., Luo, M., & Zhang, D. (2020). Tool wear monitoring for complex part milling based on deep learning. Applied Sciences (Switzerland), 10(19), 1\u201320. https:\/\/doi.org\/10.3390\/app10196916.","journal-title":"Applied Sciences (Switzerland)"},{"issue":"2","key":"2074_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s17020273","volume":"17","author":"R Zhao","year":"2017","unstructured":"Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors (Switzerland), 17(2), 1\u201318. https:\/\/doi.org\/10.3390\/s17020273.","journal-title":"Sensors (Switzerland)"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02074-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02074-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02074-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:12:26Z","timestamp":1706692346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02074-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":63,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2074"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02074-8","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,12]]},"assertion":[{"value":"1 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}