{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T20:30:34Z","timestamp":1779395434539,"version":"3.53.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National High-tech R&amp;D Program of China","award":["2015AA043002"],"award-info":[{"award-number":["2015AA043002"]}]},{"name":"National High-tech R&amp;D Program of China","award":["LQ22E050017"],"award-info":[{"award-number":["LQ22E050017"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["2015AA043002"],"award-info":[{"award-number":["2015AA043002"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["LQ22E050017"],"award-info":[{"award-number":["LQ22E050017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a new method for predicting rotation error based on improved grey wolf\u2013optimized support vector regression (IGWO-SVR), because the existing rotation error research methods cannot meet the production beat and product quality requirements of enterprises, because of the disadvantages of its being time-consuming and having poor calculation accuracy. First, the grey wolf algorithm is improved based on the optimal Latin hypercube sampling initialization, nonlinear convergence factor, and dynamic weights to improve its accuracy in optimizing the parameters of the support vector regression (SVR) model. Then, the IGWO-SVR prediction model between the manufacturing error of critical parts and the rotation error is established with the RV-40E reducer as a case. The results show that the improved grey wolf algorithm shows better parameter optimization performance, and the IGWO-SVR method shows better prediction performance than the existing back propagation (BP) neural network and BP neural network optimized by the sparrow search algorithm rotation error prediction methods, as well as the SVR models optimized by particle swarm algorithm and grey wolf algorithm. The mean squared error of IGWO-SVR model is 0.026, the running time is 7.843 s, and the maximum relative error is 13.5%, which can meet the requirements of production beat and product quality. Therefore, the IGWO-SVR method can be well applied to the rotate vector (RV) reducer parts-matching model to improve product quality and reduce rework rate and cost.<\/jats:p>","DOI":"10.3390\/s23010366","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:19:46Z","timestamp":1672370386000},"page":"366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Study on an Assembly Prediction Method of RV Reducer Based on IGWO Algorithm and SVR Model"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5120-6402","authenticated-orcid":false,"given":"Shousong","family":"Jin","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyi","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiancheng","family":"Qian","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaliang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","unstructured":"Yin, Y. (2021). Research on On-line Monitoring and Rating of Transmission Accuracy of Industrial Robot RV Reducer. [Master\u2019s Thesis, China University of Mining and Technology]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, F., Li, P., Zhu, P., Yang, X., Jiang, W., Xavior, A., Cai, J., and You, L. (2017). Analysis on Dynamic Transmission Accuracy for RV Reducer. MATEC Web of Conferences, EDP Sciences.","DOI":"10.1051\/matecconf\/201710001003"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1115\/1.3259004","article-title":"Cycloid drives with machining tolerances","volume":"111","author":"Blanche","year":"1989","journal-title":"Mech. Transm. Autom. Des."},{"key":"ref_4","first-page":"278","article-title":"A study on opening the turning error of the K-H-V star congratulations device using the Cycloid congratulations vehicle (article 2, effects of various processing and assembly errors on turning error)","volume":"60","author":"Ishida","year":"1994","journal-title":"Trans. Jpn. Soc. Mech. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"226","DOI":"10.4028\/www.scientific.net\/AMM.789-790.226","article-title":"Virtual Prototype Simulation and Transmission Error Analysis for RV Reducer","volume":"789\u2013790","author":"Zhang","year":"2015","journal-title":"Appl. Mech. Mater."},{"key":"ref_6","unstructured":"Tong, X.T. (2019). Research on Dynamic Transmission Error of RV Reducer based on Virtual Prototype technology. [Master\u2019s Thesis, Zhejiang University of Technology]."},{"key":"ref_7","first-page":"149","article-title":"the RV reducer transmission error prediction based on SSA-BP","volume":"46","author":"Sun","year":"2022","journal-title":"J. Mech. Transm."},{"key":"ref_8","first-page":"2331","article-title":"A support vector machine milling cutter wear prediction model based on deep learning and feature post-processing","volume":"26","author":"Dai","year":"2022","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.gsf.2020.10.009","article-title":"Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms","volume":"12","author":"Balogun","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107297","DOI":"10.1016\/j.knosys.2021.107297","article-title":"Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads","volume":"228","author":"Zhang","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103716","DOI":"10.1016\/j.jngse.2020.103716","article-title":"A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline","volume":"85","author":"Peng","year":"2021","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Choi, Y., Bui, X.N., and Nguyen-Thoi, T. (2019). Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms. Sensors, 20.","DOI":"10.3390\/s20010132"},{"key":"ref_13","unstructured":"Zhang, B., Li, K., Hu, Y., Ji, K., and Han, B. (2022). Prediction of Backfill Strength Based on Support Vector Regression Improved by Grey Wolf Optimization. J. Shanghai Jiaotong Univ. (Sci.), 1\u20139. Available online: https:\/\/link.springer.com\/article\/10.1007\/s12204-022-2408-7."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/JAS.2021.1004129","article-title":"A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends","volume":"8","author":"Tang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121407","DOI":"10.1016\/j.energy.2021.121407","article-title":"Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach","volume":"235","author":"Liu","year":"2021","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107504","DOI":"10.1016\/j.asoc.2021.107504","article-title":"Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems","volume":"108","author":"Li","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113873","DOI":"10.1016\/j.eswa.2020.113873","article-title":"Dynamic salp swarm algorithm for feature selection","volume":"164","author":"Tubishat","year":"2021","journal-title":"Expert Syst Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, T., Ye, X., Heidari, A.A., Liang, G., Chen, H., and Pan, Z. (2022). Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Eng. Comput. Germany., 1\u201335.","DOI":"10.1007\/s00366-021-01545-x"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11831-019-09343-x","article-title":"A comparative study of recent non-traditional methods for mechanical design optimization","volume":"27","author":"Yildiz","year":"2020","journal-title":"Arch. Comput. Method E."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1515\/mt-2020-0075","article-title":"A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system","volume":"63","author":"Abderazek","year":"2021","journal-title":"Mater Test."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1007\/s00366-021-01311-z","article-title":"Cellular differential evolutionary algorithm with double-stage external population-leading and its application","volume":"38","author":"Wang","year":"2022","journal-title":"Eng. Comput. Germany"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1007\/s40313-020-00584-x","article-title":"Intelligent Sliding Mode Adaptive Controller Design for Wind Turbine Pitch Control System Using PSO-SVM in Presence of Disturbance","volume":"31","author":"Kamarzarrin","year":"2020","journal-title":"J. Control Autom. Electr. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3126366","article-title":"Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Mixture of Ensemble Empirical Mode Decomposition and GWO-SVR Model","volume":"70","author":"Yang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_25","first-page":"30","article-title":"A Survey of Gray Wolf Optimization Algorithms","volume":"46","author":"Zhang","year":"2022","journal-title":"Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, X., Ren, S., Quan, H., and Gao, Q. (2020). Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer. Sensors, 20.","DOI":"10.3390\/s20030820"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"545","DOI":"10.20965\/ijat.2019.p0545","article-title":"Influencing Factors on Rotate Vector Reducer Dynamic Transmission Error","volume":"13","author":"Jin","year":"2019","journal-title":"Int. J. Autom. Technol"},{"key":"ref_28","first-page":"215","article-title":"Transmission ERROR Modeling and Optimization of Robot Reducer","volume":"37","author":"Liu","year":"2022","journal-title":"Control Theory Appl."},{"key":"ref_29","first-page":"14","article-title":"Job Scheduling for Cross-layer Shuttle Vehicle Storage System with FJSP Problem","volume":"1","author":"Lei","year":"2022","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/0378-3758(90)90122-B","article-title":"Minimax and maximin distance designs","volume":"26","author":"Johnson","year":"1990","journal-title":"J. Stat. Plan. Infer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/0378-3758(94)00035-T","article-title":"Exploratory designs for computational experiments","volume":"43","author":"Morris","year":"1995","journal-title":"J. Stat. Plan. Infer."},{"key":"ref_32","first-page":"487","article-title":"Online Public opinion Prediction based on improved Grey Wolf Optimized Support Vector Regression","volume":"42","author":"Lin","year":"2022","journal-title":"Syst. Eng. Theory Pract."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hou, Y., Gao, H., Wang, Z., and Du, C. (2022). Improved Grey Wolf Optimization Algorithm and Application. Sensors, 22.","DOI":"10.3390\/s22103810"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/JSEE.2015.00037","article-title":"Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC","volume":"26","author":"Zhu","year":"2015","journal-title":"J. Syst. Eng. Electr."},{"key":"ref_35","first-page":"1523","article-title":"Residual life prediction of high power semiconductor lasers based on cluster sampling and support vector regression","volume":"32","author":"Yan","year":"2022","journal-title":"Model. China Mech. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/366\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:55:20Z","timestamp":1760147720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010366"],"URL":"https:\/\/doi.org\/10.3390\/s23010366","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,29]]}}}