{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:19:56Z","timestamp":1778858396550,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018AAA0101802"],"award-info":[{"award-number":["2018AAA0101802"]}]},{"name":"Science and Technology Major Project of Shaanxi Province","award":["2019ZDLGY01-05HZ"],"award-info":[{"award-number":["2019ZDLGY01-05HZ"]}]},{"DOI":"10.13039\/501100001459","name":"Singapore Ministry of Education","doi-asserted-by":"crossref","award":["MOE2018-T2-1-140"],"award-info":[{"award-number":["MOE2018-T2-1-140"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001459","name":"Singapore Ministry of Education","doi-asserted-by":"crossref","award":["MOE-T2EP50120-0010"],"award-info":[{"award-number":["MOE-T2EP50120-0010"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s10845-022-02054-4","type":"journal-article","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T17:04:18Z","timestamp":1668272658000},"page":"343-353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Interpolation-based virtual sample generation for surface roughness prediction"],"prefix":"10.1007","volume":"35","author":[{"given":"Wenwen","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobing","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuesong","family":"Mei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangde","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0733-608X","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"issue":"8","key":"2054_CR1","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/S0890-6955(03)00059-2","volume":"43","author":"P Benardos","year":"2003","unstructured":"Benardos, P., & Vosniakos, G.-C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833\u2013844.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2054_CR2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ijmachtools.2018.05.009","volume":"133","author":"A Chaudhari","year":"2018","unstructured":"Chaudhari, A., Soh, Z. Y., Wang, H., & Kumar, A. S. (2018). Rehbinder effect in ultraprecision machining of ductile materials. International Journal of Machine Tools and Manufacture, 133, 47\u201360.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"1","key":"2054_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","volume":"29","author":"CP Chen","year":"2017","unstructured":"Chen, C. P., & Liu, Z. (2017). Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 29(1), 10\u201324.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2054_CR4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.measurement.2016.11.027","volume":"98","author":"Y Chen","year":"2017","unstructured":"Chen, Y., Sun, R., Gao, Y., & Leopold, J. (2017). A nested-ann prediction model for surface roughness considering the effects of cutting forces and tool vibrations. Measurement, 98, 25\u201334.","journal-title":"Measurement"},{"key":"2054_CR5","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.measurement.2015.03.004","volume":"69","author":"J Chen","year":"2015","unstructured":"Chen, J., & Zhao, Q. (2015). A model for predicting surface roughness in single-point diamond turning. Measurement, 69, 20\u201330.","journal-title":"Measurement"},{"issue":"3","key":"2054_CR6","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.apm.2010.07.048","volume":"35","author":"M Dong","year":"2011","unstructured":"Dong, M., & Wang, N. (2011). Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Applied Mathematical Modelling, 35(3), 1024\u20131035.","journal-title":"Applied Mathematical Modelling"},{"key":"2054_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3061094","author":"X Gong","year":"2021","unstructured":"Gong, X., Zhang, T., Chen, C. P., & Liu, Z. (2021). Research review for broad learning system: Algorithms, theory, and applications. IEEE Transactions on Cybernetics. https:\/\/doi.org\/10.1109\/TCYB.2021.3061094.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"5","key":"2054_CR8","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1016\/j.asoc.2012.03.070","volume":"13","author":"M Grzenda","year":"2013","unstructured":"Grzenda, M., & Bustillo, A. (2013). The evolutionary development of roughness prediction models. Applied Soft Computing, 13(5), 2913\u20132922.","journal-title":"Applied Soft Computing"},{"key":"2054_CR9","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.precisioneng.2018.03.004","volume":"53","author":"J Guo","year":"2018","unstructured":"Guo, J., Zhang, J., Wang, H., Liu, K., & Kumar, A. S. (2018). Surface quality characterisation of diamond cut v-groove structures made of rapidly solidified aluminium rsa-905. Precision Engineering, 53, 120\u2013133.","journal-title":"Precision Engineering"},{"key":"2054_CR10","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.isatra.2021.07.033","volume":"126","author":"Y-L He","year":"2022","unstructured":"He, Y.-L., Hua, Q., Zhu, Q.-X., & Lu, S. (2022). Enhanced virtual sample generation based on manifold features: Applications to developing soft sensor using small data. ISA Transactions, 126, 398\u2013406.","journal-title":"ISA Transactions"},{"issue":"2","key":"2054_CR11","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1016\/j.eswa.2008.01.051","volume":"36","author":"W-H Ho","year":"2009","unstructured":"Ho, W.-H., Tsai, J.-T., Lin, B.-T., & Chou, J.-H. (2009). Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid taguchi-genetic learning algorithm. Expert Systems with applications, 36(2), 3216\u20133222.","journal-title":"Expert Systems with applications"},{"issue":"4","key":"2054_CR12","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1007\/s10845-017-1361-z","volume":"30","author":"PB Huang","year":"2019","unstructured":"Huang, P. B., Zhang, H.-J., & Lin, Y.-C. (2019). Development of a grey online modeling surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing, 30(4), 1923\u20131936.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"10","key":"2054_CR13","doi-asserted-by":"publisher","first-page":"7683","DOI":"10.1109\/TIM.2020.2980599","volume":"69","author":"K Kannadasan","year":"2020","unstructured":"Kannadasan, K., Edla, D. R., Yadav, M. H., & Bablani, A. (2020). Intelligent-anfis model for predicting measurement of surface roughness and geometric tolerances in three-axis cnc milling. IEEE Transactions on Instrumentation and Measurement , 69(10), 7683\u20137694.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2054_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106770","volume":"142","author":"D Kong","year":"2020","unstructured":"Kong, D., Zhu, J., Duan, C., Lu, L., & Chen, D. (2020). Bayesian linear regression for surface roughness prediction. Mechanical Systems and Signal Processing, 142, 106770.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2054_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107474","volume":"152","author":"D Kong","year":"2021","unstructured":"Kong, D., Zhu, J., Duan, C., Lu, L., & Chen, D. (2021). Surface roughness prediction using kernel locality preserving projection and bayesian linear regression. Mechanical Systems and Signal Processing, 152, 107474.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2054_CR16","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.ins.2021.09.014","volume":"581","author":"L Li","year":"2021","unstructured":"Li, L., Damarla, S. K., Wang, Y., & Huang, B. (2021). A gaussian mixture model based virtual sample generation approach for small datasets in industrial processes. Information Sciences, 581, 262\u2013277.","journal-title":"Information Sciences"},{"issue":"3","key":"2054_CR17","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/S0924-0136(00)00835-9","volume":"108","author":"W Lin","year":"2001","unstructured":"Lin, W., Lee, B., & Wu, C. (2001). Modeling the surface roughness and cutting force for turning. Journal of Materials Processing Technology, 108(3), 286\u2013293.","journal-title":"Journal of Materials Processing Technology"},{"key":"2054_CR18","doi-asserted-by":"publisher","first-page":"80006","DOI":"10.1109\/ACCESS.2021.3084617","volume":"9","author":"B Li","year":"2021","unstructured":"Li, B., & Tian, X. (2021). An effective pso-lssvm-based approach for surface roughness prediction in high-speed precision milling. Ieee Access, 9, 80006\u201380014.","journal-title":"Ieee Access"},{"key":"2054_CR19","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ijmecsci.2016.09.002","volume":"118","author":"N Liu","year":"2016","unstructured":"Liu, N., Wang, S., Zhang, Y., & Lu, W. (2016). A novel approach to predicting surface roughness based on specific cutting energy consumption when slot milling al-7075. International Journal of Mechanical Sciences, 118, 13\u201320.","journal-title":"International Journal of Mechanical Sciences"},{"key":"2054_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101470","volume":"51","author":"L Liu","year":"2022","unstructured":"Liu, L., Zhang, X., Wan, X., Zhou, S., & Gao, Z. (2022). Digital twin-driven surface roughness prediction and process parameter adaptive optimization. Advanced Engineering Informatics, 51, 101470.","journal-title":"Advanced Engineering Informatics"},{"issue":"3","key":"2054_CR21","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1016\/S0924-0136(03)00687-3","volume":"142","author":"S-P Lo","year":"2003","unstructured":"Lo, S.-P. (2003). An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 142(3), 665\u2013675.","journal-title":"Journal of Materials Processing Technology"},{"issue":"1\u20133","key":"2054_CR22","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.jmatprotec.2007.11.270","volume":"205","author":"C Lu","year":"2008","unstructured":"Lu, C. (2008). Study on prediction of surface quality in machining process. Journal of Materials Processing Technology, 205(1\u20133), 439\u2013450.","journal-title":"Journal of Materials Processing Technology"},{"issue":"1","key":"2054_CR23","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s00170-014-6425-z","volume":"77","author":"G Mahesh","year":"2015","unstructured":"Mahesh, G., Muthu, S., & Devadasan, S. (2015). Prediction of surface roughness of end milling operation using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 77(1), 369\u2013381.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"12","key":"2054_CR24","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3390\/machines9120369","volume":"9","author":"K Manjunath","year":"2021","unstructured":"Manjunath, K., Tewary, S., Khatri, N., & Cheng, K. (2021). Monitoring and predicting the surface generation and surface roughness in ultraprecision machining: A critical review. Machines, 9(12), 369.","journal-title":"Machines"},{"issue":"4\u20135","key":"2054_CR25","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.ijmachtools.2004.09.007","volume":"45","author":"T \u00d6zel","year":"2005","unstructured":"\u00d6zel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4\u20135), 467\u2013479.","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"2054_CR26","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10845-020-01669-9","volume":"33","author":"Y Pan","year":"2020","unstructured":"Pan, Y., Kang, R., Dong, Z., Du, W., Yin, S., & Bao, Y. (2020). On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning. Journal of Intelligent Manufacturing, 33, 675\u2013685.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2054_CR27","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.ymssp.2017.05.006","volume":"98","author":"EG Plaza","year":"2018","unstructured":"Plaza, E. G., & L\u00f3pez, P. N. (2018). Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in cnc turning. Mechanical Systems and Signal Processing, 98, 634\u2013651.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2054_CR28","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.ymssp.2017.05.028","volume":"98","author":"EG Plaza","year":"2018","unstructured":"Plaza, E. G., & L\u00f3pez, P. N. (2018). Application of the wavelet packet transform to vibration signals for surface roughness monitoring in cnc turning operations. Mechanical Systems and Signal Processing, 98, 902\u2013919.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2054_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107860","volume":"161","author":"AP Rifai","year":"2020","unstructured":"Rifai, A. P., Aoyama, H., Tho, N. H., Dawal, S. Z. M., & Masruroh, N. A. (2020). Evaluation of turned and milled surfaces roughness using convolutional neural network. Measurement, 161, 107860.","journal-title":"Measurement"},{"issue":"1","key":"2054_CR30","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/s00170-008-1698-8","volume":"43","author":"DR Salgado","year":"2009","unstructured":"Salgado, D. R., Alonso, F., Cambero, I., & Marcelo, A. (2009). In-process surface roughness prediction system using cutting vibrations in turning. The International Journal of Advanced Manufacturing Technology, 43(1), 40\u201351.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"2054_CR31","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1080\/09511920802287138","volume":"22","author":"B Samanta","year":"2009","unstructured":"Samanta, B. (2009). Surface roughness prediction in machining using soft computing. International Journal of Computer Integrated Manufacturing, 22(3), 257\u2013266.","journal-title":"International Journal of Computer Integrated Manufacturing"},{"key":"2054_CR32","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.neucom.2015.03.060","volume":"166","author":"A Sarkheyli","year":"2015","unstructured":"Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). Robust optimization of anfis based on a new modified ga. Neurocomputing, 166, 357\u2013366.","journal-title":"Neurocomputing"},{"issue":"1","key":"2054_CR33","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s13042-013-0155-7","volume":"5","author":"AB Sharkawy","year":"2014","unstructured":"Sharkawy, A. B., El-Sharief, M. A., & Soliman, M.-E.S. (2014). Surface roughness prediction in end milling process using intelligent systems. International Journal of Machine Learning and Cybernetics, 5(1), 135\u2013150.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"2054_CR34","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.asoc.2016.10.010","volume":"52","author":"I Svalina","year":"2017","unstructured":"Svalina, I., \u0160imunovi\u0107, G., \u0160ari\u0107, T., & Luji\u0107, R. (2017). Evolutionary neuro-fuzzy system for surface roughness evaluation. Applied Soft Computing, 52, 593\u2013604.","journal-title":"Applied Soft Computing"},{"key":"2054_CR35","first-page":"1","volume":"71","author":"W Tian","year":"2022","unstructured":"Tian, W., Zhao, F., Min, C., Feng, X., Liu, R., Mei, X., & Chen, G. (2022). Broad learning system based on binary grey wolf optimization for surface roughness prediction in slot milling. IEEE Transactions on Instrumentation and Measurement, 71, 1\u201310.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2054_CR36","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.jmsy.2022.07.012","volume":"64","author":"W Tian","year":"2022","unstructured":"Tian, W., Zhao, F., Sun, Z., Zhang, J., Gong, C., Mei, X., et al. (2022). Prediction of surface roughness using fuzzy broad learning system based on feature selection. Journal of Manufacturing Systems, 64, 508\u2013517.","journal-title":"Journal of Manufacturing Systems"},{"issue":"4","key":"2054_CR37","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/S0890-6955(98)00053-4","volume":"39","author":"Y-H Tsai","year":"1999","unstructured":"Tsai, Y.-H., Chen, J. C., & Lou, S.-J. (1999). An in-process surface recognition system based on neural networks in end milling cutting operations. International Journal of Machine Tools and Manufacture, 39(4), 583\u2013605.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"1","key":"2054_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jcde.2015.04.002","volume":"3","author":"T-L Tseng","year":"2016","unstructured":"Tseng, T.-L., Konada, U., & Kwon, Y. (2016). A novel approach to predict surface roughness in machining operations using fuzzy set theory. Journal of Computational Design and Engineering, 3(1), 1\u201313.","journal-title":"Journal of Computational Design and Engineering"},{"key":"2054_CR39","first-page":"1409","volume":"11198","author":"X Wang","year":"2022","unstructured":"Wang, X., Liu, H., Li, L., & Zhang, Y. (2022). Dual adversarial learning-based virtual sample generation method for data expansion of soft senors. Measurement, 11198, 1409.","journal-title":"Measurement"},{"key":"2054_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3137172","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Niu, M., Liu, K., Shen, M., Qin, B., & Wang, H. (2022). A novel data augmentation method based on coralgan for prediction of part surface roughness. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2021.3137172.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2054_CR41","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.jmapro.2020.10.070","volume":"60","author":"S Wang","year":"2020","unstructured":"Wang, S., Xia, S., Wang, H., Yin, Z., & Sun, Z. (2020). Prediction of surface roughness in diamond turning of al6061 with precipitation effect. Journal of Manufacturing Processes, 60, 292\u2013298.","journal-title":"Journal of Manufacturing Processes"},{"issue":"9","key":"2054_CR42","doi-asserted-by":"publisher","first-page":"2523","DOI":"10.1007\/s00170-015-7884-6","volume":"84","author":"L Wen","year":"2016","unstructured":"Wen, L., Li, X., Gao, L., & Yi, W. (2016). Surface roughness prediction in end milling by using predicted point oriented local linear estimation method. The International Journal of Advanced Manufacturing Technology, 84(9), 2523\u20132535.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2054_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115397","volume":"183","author":"W Yu","year":"2021","unstructured":"Yu, W., Lu, Y., & Wang, J. (2021). Application of small sample virtual expansion and spherical mapping model in wind turbine fault diagnosis. Expert Systems with Applications, 183, 115397.","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"2054_CR44","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.1016\/j.eswa.2009.07.033","volume":"37","author":"AM Zain","year":"2010","unstructured":"Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using artificial neural network. Expert Systems with Applications, 37(2), 1755\u20131768.","journal-title":"Expert Systems with Applications"},{"key":"2054_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2022.103851","volume":"174","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Lee, Y. J., Chang, S., Chen, Y., Bai, Y., Zhang, J., & Wang, H. (2022). Microstructural modulation of tial alloys for controlling ultra-precision machinability. International Journal of Machine Tools and Manufacture, 174, 103851.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"4","key":"2054_CR46","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1007\/s40684-020-00260-0","volume":"8","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Lee, Y. J., & Wang, H. (2021). Surface texture transformation in micro-cutting of aa6061-t6 with the rehbinder effect. International Journal of Precision Engineering and Manufacturing-Green Technology, 8(4), 1151\u20131162.","journal-title":"International Journal of Precision Engineering and Manufacturing-Green Technology"},{"key":"2054_CR47","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ijmachtools.2015.02.001","volume":"91","author":"S Zhang","year":"2015","unstructured":"Zhang, S., To, S., Wang, S., & Zhu, Z. (2015). A review of surface roughness generation in ultra-precision machining. International Journal of Machine Tools and Manufacture, 91, 76\u201395.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"9","key":"2054_CR48","doi-asserted-by":"publisher","first-page":"6889","DOI":"10.1007\/s00500-019-04326-3","volume":"24","author":"Q-X Zhu","year":"2020","unstructured":"Zhu, Q.-X., Chen, Z.-S., Zhang, X.-H., Rajabifard, A., Xu, Y., & Chen, Y.-Q. (2020). Dealing with small sample size problems in process industry using virtual sample generation: A kriging-based approach. Soft Computing, 24(9), 6889\u20136902.","journal-title":"Soft Computing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-02054-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-022-02054-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-02054-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,14]],"date-time":"2024-01-14T22:02:35Z","timestamp":1705269755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-022-02054-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["2054"],"URL":"https:\/\/doi.org\/10.1007\/s10845-022-02054-4","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,12]]},"assertion":[{"value":"31 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest in the implementation of this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors consent that the work entitled as \u201cInterpolation-based virtual sample generation for surface roughness prediction\u201d for possible publication in Journal of Intelligent Manufacturing. The authors certify that this manuscript is original and has not been published in whole or in part nor is it being considered for publication elsewhere.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}