Process parameter optimisation method based on data-driven prediction model and multi-objective optimisation for the laser metal deposition manufacturing process monitoring
•A data-driven prediction model based on random forest was developed. The model takes into account internal defects porosity and cracks, and establishes a non-explicit function between process parameters and quality as the objective optimisation function.•A multi-objective optimisation algorithm for...
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| Veröffentlicht in: | Computers & industrial engineering Jg. 204; S. 111108 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.06.2025
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| ISSN: | 0360-8352 |
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| Abstract | •A data-driven prediction model based on random forest was developed. The model takes into account internal defects porosity and cracks, and establishes a non-explicit function between process parameters and quality as the objective optimisation function.•A multi-objective optimisation algorithm for process parameters suitable for manufacturing processes is constructed. The algorithm combines random forest and NSGA-II for the first time, eliminates the need for expensive and time-consuming multiple testing experiments, and improves the reliability and applicability of the optimisation model.•The optimal solution search strategy is established. The strategy enables the optimisation algorithm can automatically search the best process parameters from the set of Pareto solutions and obtain the optimal solution, which meets the actual requirements of industrial manufacturing.
Process parameter optimisation is essential for laser metal deposition manufacturing process monitoring, which can minimize internal defects and enhance component quality. However, existing process parameter optimisation mainly focuses on experimental design and curve fitting, which are time-consuming, labour-intensive, and expensive to test, thus, they are not able to effectively ensure accuracy. In addition, the dynamic changes in quality under the same process parameters in the manufacturing processes make it more difficult to analyse the optimal process parameter combination through experimentation. In this regard, a process parameter optimisation method based on a data-driven prediction model and the multi-objective optimisation algorithm is proposed in this paper to obtain the optimal process parameter combination. This method carries out multi-process parameter deposition experiments to count the number of each quality level based on the established quality evaluation standard. Then, a data-driven prediction model by random forest was used to automatically develop a non-explicit prediction function, which establishes the relationship between process parameters and different quality levels. Subsequently, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective optimisation algorithm was utilised to generate the optimal set of Pareto solutions for the process parameters. Finally, the optimal process parameter combinations are automatically searched based on the proposed search strategy. Experimental results show that the components under the optimal process parameters have the least internal defects and the best quality, which indicates that the proposed method can provide effective guidance for the manufacturing process monitoring. |
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| AbstractList | •A data-driven prediction model based on random forest was developed. The model takes into account internal defects porosity and cracks, and establishes a non-explicit function between process parameters and quality as the objective optimisation function.•A multi-objective optimisation algorithm for process parameters suitable for manufacturing processes is constructed. The algorithm combines random forest and NSGA-II for the first time, eliminates the need for expensive and time-consuming multiple testing experiments, and improves the reliability and applicability of the optimisation model.•The optimal solution search strategy is established. The strategy enables the optimisation algorithm can automatically search the best process parameters from the set of Pareto solutions and obtain the optimal solution, which meets the actual requirements of industrial manufacturing.
Process parameter optimisation is essential for laser metal deposition manufacturing process monitoring, which can minimize internal defects and enhance component quality. However, existing process parameter optimisation mainly focuses on experimental design and curve fitting, which are time-consuming, labour-intensive, and expensive to test, thus, they are not able to effectively ensure accuracy. In addition, the dynamic changes in quality under the same process parameters in the manufacturing processes make it more difficult to analyse the optimal process parameter combination through experimentation. In this regard, a process parameter optimisation method based on a data-driven prediction model and the multi-objective optimisation algorithm is proposed in this paper to obtain the optimal process parameter combination. This method carries out multi-process parameter deposition experiments to count the number of each quality level based on the established quality evaluation standard. Then, a data-driven prediction model by random forest was used to automatically develop a non-explicit prediction function, which establishes the relationship between process parameters and different quality levels. Subsequently, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective optimisation algorithm was utilised to generate the optimal set of Pareto solutions for the process parameters. Finally, the optimal process parameter combinations are automatically searched based on the proposed search strategy. Experimental results show that the components under the optimal process parameters have the least internal defects and the best quality, which indicates that the proposed method can provide effective guidance for the manufacturing process monitoring. |
| ArticleNumber | 111108 |
| Author | Li, Cheng Xu, Zhenying Zhang, Chao Han, Bangguo Wang, Zeyi Fan, Wei Wu, Ziqian |
| Author_xml | – sequence: 1 givenname: Ziqian orcidid: 0000-0002-2133-0637 surname: Wu fullname: Wu, Ziqian organization: School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China – sequence: 2 givenname: Cheng surname: Li fullname: Li, Cheng email: licheng@czust.edu.cn organization: School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China – sequence: 3 givenname: Chao surname: Zhang fullname: Zhang, Chao organization: Personnel Department, Jiangsu Urban and Rural Construction Vocational College, Changzhou 213147, China – sequence: 4 givenname: Bangguo surname: Han fullname: Han, Bangguo organization: School of Public Utilities, Jiangsu Urban and Rural Construction Vocational College, Changzhou 213147, China – sequence: 5 givenname: Zeyi surname: Wang fullname: Wang, Zeyi organization: School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China – sequence: 6 givenname: Wei surname: Fan fullname: Fan, Wei organization: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China – sequence: 7 givenname: Zhenying orcidid: 0000-0002-4319-5077 surname: Xu fullname: Xu, Zhenying email: xuzhenying@ujs.edu.cn organization: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China |
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| Cites_doi | 10.1016/j.addma.2022.102643 10.1016/j.jmsy.2022.05.016 10.1016/j.jmsy.2024.01.011 10.1002/adem.202200279 10.1057/s41278-023-00271-z 10.1109/TEVC.2007.892759 10.1109/TEVC.2016.2519378 10.1109/TNNLS.2022.3155478 10.1016/j.measurement.2024.115883 10.1016/j.jmapro.2023.10.021 10.1109/TNNLS.2012.2199516 10.1016/j.swevo.2019.05.011 10.1007/s11665-020-04847-1 10.1016/j.measurement.2021.110232 10.1007/s00170-018-2373-3 10.1016/j.jmapro.2021.07.064 10.17849/insm-47-01-31-39.1 10.1016/j.addma.2016.05.009 10.1007/s10994-012-5286-7 10.1016/j.trpro.2019.07.024 10.1016/j.cirpj.2020.05.009 10.3390/ma17050971 10.1007/s00170-014-6012-3 10.1007/s00170-020-06047-6 10.1007/s11277-017-5224-x 10.1016/j.ejor.2006.08.008 10.1080/25725084.2020.1784530 10.1016/j.addma.2015.07.002 10.1016/j.jmapro.2022.02.027 10.1016/j.addma.2024.104208 10.1016/j.asoc.2023.110472 10.1108/RPJ-04-2016-0059 10.1002/qre.3513 10.1016/j.jmapro.2022.02.053 |
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| Keywords | Manufacturing process monitoring Data-driven prediction model Process parameter optimisation Optimal process parameter combinations Multi-objective optimisation algorithm |
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| References | Wang, Yang, Liu (b0180) 2022; 77 Hentschel, Petersmann, Gonzalez-Gutierrez (b0075) 2023; 25 Patil, Nigam, Mohapatra (b0125) 2021; 69 Deshwal, Kumar, Chhabra (b0060) 2020; 31 Reif, Shafait, Dengel (b0140) 2012; 87 Beniak, Holdy, Križan (b0020) 2019; 40 Rigatti (b0145) 2017; 47 Shamsaei, Yadollahi, Bian (b0160) 2015; 8 Kramer, Kramer (b0085) 2013 Zheng, Liu, Doerr (b0210) 2022 Brøtan (b0030) 2014; 74 Cheng, Jin, Olhofer (b0035) 2016; 20 Sato, Sato, Miyakawa (b0150) 2019 Aggarwal, Urbanic, Saqib (b0010) 2018; 24 Mahmood, Popescu, Hapenciuc (b0105) 2020; 111 Cuong, Xu, Lee (b0050) 2020; 4 Soori, Asmael (b0170) 2022; 16 Nick, Campbell (b0115) 2007 Pandit, Sekhar, Shah (b0120) 2019; 8 DaSilva, Frostevarg, Kaplan (b0055) 2023; 107 Tapia, Elwany, Sang (b0175) 2016; 12 Kumar, Maji (b0090) 2020; 29 Li, Cao, Xu (b0095) 2022; 187 Kapil, Suga, Tanaka (b0080) 2022; 76 Song, Yang, Xu (b0165) 2022; 34 Beume, Naujoks, Emmerich (b0025) 2007; 181 Rahimi, Gandomi, Nikoo (b0130) 2023; 144 Zhang, Huang, Zhang (b0200) 2024; 86 Zhang, Li (b0205) 2007; 11 Engelhardt, Wegener, Niendorf (b0070) 2024; 17 Wu, Feng (b0185) 2018; 102 Ramani, He, Tsai (b0135) 2022; 52 Ding, Wang, Ma (b0065) 2024; 40 Wu, He, Li (b0190) 2022; 64 Abbas, Memon, Jamali (b0005) 2019; 19 Awad, Khanna, Awad (b0015) 2015 Wu, Zhang, Xu, Fan (b0195) 2025; 242 Cuong, Kim, Long (b0045) 2024; 26 Cui, Chang, Zhang (b0040) 2019; 49 Li, Zhao, Tang (b0100) 2024; 73 Serra, Chibane, Duchosal (b0155) 2018; 99 Moreno-Torres, Saez, Herrera (b0110) 2012; 23 Zhou, Li, Chen (b0215) 2023; 47 Zheng (10.1016/j.cie.2025.111108_b0210) 2022 Deshwal (10.1016/j.cie.2025.111108_b0060) 2020; 31 Wu (10.1016/j.cie.2025.111108_b0190) 2022; 64 Kramer (10.1016/j.cie.2025.111108_b0085) 2013 Zhang (10.1016/j.cie.2025.111108_b0205) 2007; 11 Beniak (10.1016/j.cie.2025.111108_b0020) 2019; 40 Rahimi (10.1016/j.cie.2025.111108_b0130) 2023; 144 Reif (10.1016/j.cie.2025.111108_b0140) 2012; 87 Rigatti (10.1016/j.cie.2025.111108_b0145) 2017; 47 Brøtan (10.1016/j.cie.2025.111108_b0030) 2014; 74 Kumar (10.1016/j.cie.2025.111108_b0090) 2020; 29 Zhou (10.1016/j.cie.2025.111108_b0215) 2023; 47 Ding (10.1016/j.cie.2025.111108_b0065) 2024; 40 Cheng (10.1016/j.cie.2025.111108_b0035) 2016; 20 Nick (10.1016/j.cie.2025.111108_b0115) 2007 Moreno-Torres (10.1016/j.cie.2025.111108_b0110) 2012; 23 Kapil (10.1016/j.cie.2025.111108_b0080) 2022; 76 Sato (10.1016/j.cie.2025.111108_b0150) 2019 Soori (10.1016/j.cie.2025.111108_b0170) 2022; 16 Mahmood (10.1016/j.cie.2025.111108_b0105) 2020; 111 Wu (10.1016/j.cie.2025.111108_b0185) 2018; 102 Song (10.1016/j.cie.2025.111108_b0165) 2022; 34 Abbas (10.1016/j.cie.2025.111108_b0005) 2019; 19 Cui (10.1016/j.cie.2025.111108_b0040) 2019; 49 Wang (10.1016/j.cie.2025.111108_b0180) 2022; 77 Li (10.1016/j.cie.2025.111108_b0095) 2022; 187 Shamsaei (10.1016/j.cie.2025.111108_b0160) 2015; 8 Wu (10.1016/j.cie.2025.111108_b0195) 2025; 242 Serra (10.1016/j.cie.2025.111108_b0155) 2018; 99 Engelhardt (10.1016/j.cie.2025.111108_b0070) 2024; 17 Hentschel (10.1016/j.cie.2025.111108_b0075) 2023; 25 Zhang (10.1016/j.cie.2025.111108_b0200) 2024; 86 Cuong (10.1016/j.cie.2025.111108_b0050) 2020; 4 Li (10.1016/j.cie.2025.111108_b0100) 2024; 73 Awad (10.1016/j.cie.2025.111108_b0015) 2015 DaSilva (10.1016/j.cie.2025.111108_b0055) 2023; 107 Beume (10.1016/j.cie.2025.111108_b0025) 2007; 181 Cuong (10.1016/j.cie.2025.111108_b0045) 2024; 26 Tapia (10.1016/j.cie.2025.111108_b0175) 2016; 12 Pandit (10.1016/j.cie.2025.111108_b0120) 2019; 8 Patil (10.1016/j.cie.2025.111108_b0125) 2021; 69 Ramani (10.1016/j.cie.2025.111108_b0135) 2022; 52 Aggarwal (10.1016/j.cie.2025.111108_b0010) 2018; 24 |
| References_xml | – volume: 111 start-page: 77 year: 2020 end-page: 91 ident: b0105 article-title: Estimation of clad geometry and corresponding residual stress distribution in laser melting deposition: Analytical modeling and experimental correlations publication-title: The International Journal of Advanced Manufacturing Technology – volume: 17 start-page: 971 year: 2024 ident: b0070 article-title: Pathways toward the use of Non-destructive micromagnetic analysis for porosity assessment and process parameter optimization in additive manufacturing of 42CrMo4 (AISI 4140) publication-title: Materials – volume: 24 start-page: 214 year: 2018 end-page: 228 ident: b0010 article-title: Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry publication-title: Rapid Prototyping Journal – volume: 181 start-page: 1653 year: 2007 end-page: 1669 ident: b0025 article-title: SMS-EMOA: Multiobjective selection based on dominated hypervolume publication-title: European Journal of Operational Research – volume: 20 start-page: 773 year: 2016 end-page: 791 ident: b0035 article-title: A reference vector guided evolutionary algorithm for many-objective optimization publication-title: IEEE Transactions on Evolutionary Computation – start-page: 13 year: 2013 end-page: 23 ident: b0085 article-title: Dimensionality reduction with unsupervised nearest neighbors publication-title: K-nearest Neighbors – start-page: 3086 year: 2019 end-page: 3093 ident: b0150 article-title: Distributed NSGA-II sharing extreme non-dominated solutions for improving accuracy and achieving speed-up publication-title: 2019 IEEE Congress on Evolutionary Computation (CEC) – volume: 29 start-page: 3334 year: 2020 end-page: 3352 ident: b0090 article-title: Selection of process parameters for near-net shape deposition in wire arc additive manufacturing by genetic algorithm publication-title: Journal of Materials Engineering and Performance – volume: 76 start-page: 457 year: 2022 end-page: 474 ident: b0080 article-title: Towards hybrid laser-arc based directed energy deposition: Understanding bead formation through mathematical modeling for additive manufacturing publication-title: Journal of Manufacturing Processes – volume: 87 start-page: 357 year: 2012 end-page: 380 ident: b0140 article-title: Meta-learning for evolutionary parameter optimization of classifiers publication-title: Machine Learning – start-page: 273 year: 2007 end-page: 301 ident: b0115 article-title: Topics in biostatistics publication-title: Logistic Regression – volume: 23 start-page: 1304 year: 2012 end-page: 1312 ident: b0110 article-title: Study on the impact of partition-induced dataset shift on k-fold cross-validation publication-title: IEEE Transaction on Neural Networks and Learning Systems – volume: 47 start-page: 31 year: 2017 end-page: 39 ident: b0145 article-title: Random forest publication-title: Journal of Insurance Medicine – volume: 73 start-page: 170 year: 2024 end-page: 191 ident: b0100 article-title: Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation publication-title: Journal of Manufacturing Systems – volume: 8 start-page: 3405 year: 2019 end-page: 3410 ident: b0120 article-title: Simulation based process optimization for additive manufacturing publication-title: International Journal of Innovative Technology and Exploring Engineering (IJITEE) – volume: 187 year: 2022 ident: b0095 article-title: In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing publication-title: Measurement – volume: 52 year: 2022 ident: b0135 article-title: SmartScan: An intelligent scanning approach for uniform thermal distribution, reduced residual stresses and deformations in PBF additive manufacturing publication-title: Additive Manufacturing – volume: 47 start-page: 469 year: 2023 end-page: 479 ident: b0215 article-title: Research progress in modeling the optimization of process parameters of laser additive manufacturing publication-title: Laser Technology – volume: 26 start-page: 212 year: 2024 end-page: 240 ident: b0045 article-title: Seaport profit analysis and efficient management strategies under stochastic disruptions publication-title: Maritime Economics & Logistics – volume: 77 start-page: 13 year: 2022 end-page: 31 ident: b0180 article-title: Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions publication-title: Journal of Manufacturing Processes – volume: 86 year: 2024 ident: b0200 article-title: Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach publication-title: Additive Manufacturing – volume: 74 start-page: 1187 year: 2014 end-page: 1195 ident: b0030 article-title: A new method for determining and improving the accuracy of a powder bed additive manufacturing machine publication-title: The International Journal of Advanced Manufacturing Technology – volume: 8 start-page: 12 year: 2015 end-page: 35 ident: b0160 article-title: An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control publication-title: Additive Manufacturing – volume: 16 start-page: 205 year: 2022 end-page: 223 ident: b0170 article-title: A review of the recent development in machining parameter optimization publication-title: Jordan Journal of Mechanical and Industrial Engineering – volume: 49 start-page: 23 year: 2019 end-page: 33 ident: b0040 article-title: Improved NSGA-III with selection-and-elimination operator publication-title: Swarm and Evolutionary Computation – volume: 4 start-page: 48 year: 2020 end-page: 55 ident: b0050 article-title: Dynamic analysis and management optimization for maritime supply chains using nonlinear control theory publication-title: Journal of International Maritime Safety, Environmental Affairs, and Shipping – volume: 69 start-page: 630 year: 2021 end-page: 647 ident: b0125 article-title: Image processing approach to automate feature measuring and process parameter optimizing of laser additive manufacturing process publication-title: Journal of Manufacturing Processes – start-page: 67 year: 2015 end-page: 80 ident: b0015 article-title: Efficient learning machines: Theories, concepts, and applications for engineers and system designers publication-title: Support Vector Regression – volume: 107 start-page: 126 year: 2023 end-page: 133 ident: b0055 article-title: Melt pool monitoring and process optimisation of directed energy deposition via coaxial thermal imaging publication-title: Journal of Manufacturing Processes – volume: 64 start-page: 40 year: 2022 end-page: 52 ident: b0190 article-title: Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS publication-title: Journal of Manufacturing Systems – volume: 40 start-page: 144 year: 2019 end-page: 149 ident: b0020 article-title: Research on parameters optimization for the additive manufacturing process publication-title: Transportation Research Procedia – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b0205 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation – volume: 242 year: 2025 ident: b0195 article-title: Multiconstraint quality–probability graph for quality monitoring of laser directed energy deposition manufacturing process publication-title: Measurement – start-page: 10408 year: 2022 end-page: 10416 ident: b0210 article-title: A first mathematical runtime analysis of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) publication-title: Proceedings of the AAAI conference on artificial intelligence – volume: 99 start-page: 2025 year: 2018 end-page: 2034 ident: b0155 article-title: Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel publication-title: The International Journal of Advanced Manufacturing Technology – volume: 12 start-page: 282 year: 2016 end-page: 290 ident: b0175 article-title: Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models publication-title: Additive Manufacturing – volume: 19 start-page: 62 year: 2019 ident: b0005 article-title: Multinomial Naive Bayes classification model for sentiment analysis publication-title: IJCSNS - International Journal of Computer Science and Network – volume: 31 start-page: 189 year: 2020 end-page: 199 ident: b0060 article-title: Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement publication-title: CIRP Journal of Manufacturing Science and Technology – volume: 144 year: 2023 ident: b0130 article-title: A comparative study on evolutionary multi-objective algorithms for next release problem publication-title: Applied Soft Computing – volume: 34 start-page: 8174 year: 2022 end-page: 8194 ident: b0165 article-title: Graph-based semi-supervised learning: A comprehensive review publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 25 year: 2023 ident: b0075 article-title: Parameter optimization of the ARBURG plastic freeforming process by means of a design of experiments approach publication-title: Advanced Engineering Materials – volume: 102 start-page: 1645 year: 2018 end-page: 1656 ident: b0185 article-title: Development and application of artificial neural network publication-title: Wireless Personal Communications – volume: 40 start-page: 2096 year: 2024 end-page: 2115 ident: b0065 article-title: Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method publication-title: Quality and Reliability Engineering International – start-page: 273 year: 2007 ident: 10.1016/j.cie.2025.111108_b0115 article-title: Topics in biostatistics publication-title: Logistic Regression – volume: 8 start-page: 3405 issue: 10 year: 2019 ident: 10.1016/j.cie.2025.111108_b0120 article-title: Simulation based process optimization for additive manufacturing publication-title: International Journal of Innovative Technology and Exploring Engineering (IJITEE) – volume: 52 year: 2022 ident: 10.1016/j.cie.2025.111108_b0135 article-title: SmartScan: An intelligent scanning approach for uniform thermal distribution, reduced residual stresses and deformations in PBF additive manufacturing publication-title: Additive Manufacturing doi: 10.1016/j.addma.2022.102643 – volume: 64 start-page: 40 year: 2022 ident: 10.1016/j.cie.2025.111108_b0190 article-title: Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2022.05.016 – start-page: 10408 year: 2022 ident: 10.1016/j.cie.2025.111108_b0210 article-title: A first mathematical runtime analysis of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) – volume: 73 start-page: 170 year: 2024 ident: 10.1016/j.cie.2025.111108_b0100 article-title: Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2024.01.011 – volume: 47 start-page: 469 issue: 4 year: 2023 ident: 10.1016/j.cie.2025.111108_b0215 article-title: Research progress in modeling the optimization of process parameters of laser additive manufacturing publication-title: Laser Technology – start-page: 3086 year: 2019 ident: 10.1016/j.cie.2025.111108_b0150 article-title: Distributed NSGA-II sharing extreme non-dominated solutions for improving accuracy and achieving speed-up – start-page: 67 year: 2015 ident: 10.1016/j.cie.2025.111108_b0015 article-title: Efficient learning machines: Theories, concepts, and applications for engineers and system designers publication-title: Support Vector Regression – volume: 25 issue: 7 year: 2023 ident: 10.1016/j.cie.2025.111108_b0075 article-title: Parameter optimization of the ARBURG plastic freeforming process by means of a design of experiments approach publication-title: Advanced Engineering Materials doi: 10.1002/adem.202200279 – volume: 26 start-page: 212 issue: 2 year: 2024 ident: 10.1016/j.cie.2025.111108_b0045 article-title: Seaport profit analysis and efficient management strategies under stochastic disruptions publication-title: Maritime Economics & Logistics doi: 10.1057/s41278-023-00271-z – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.cie.2025.111108_b0205 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2007.892759 – volume: 20 start-page: 773 issue: 5 year: 2016 ident: 10.1016/j.cie.2025.111108_b0035 article-title: A reference vector guided evolutionary algorithm for many-objective optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2016.2519378 – volume: 34 start-page: 8174 issue: 11 year: 2022 ident: 10.1016/j.cie.2025.111108_b0165 article-title: Graph-based semi-supervised learning: A comprehensive review publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2022.3155478 – volume: 242 year: 2025 ident: 10.1016/j.cie.2025.111108_b0195 article-title: Multiconstraint quality–probability graph for quality monitoring of laser directed energy deposition manufacturing process publication-title: Measurement doi: 10.1016/j.measurement.2024.115883 – volume: 107 start-page: 126 year: 2023 ident: 10.1016/j.cie.2025.111108_b0055 article-title: Melt pool monitoring and process optimisation of directed energy deposition via coaxial thermal imaging publication-title: Journal of Manufacturing Processes doi: 10.1016/j.jmapro.2023.10.021 – volume: 23 start-page: 1304 issue: 8 year: 2012 ident: 10.1016/j.cie.2025.111108_b0110 article-title: Study on the impact of partition-induced dataset shift on k-fold cross-validation publication-title: IEEE Transaction on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2012.2199516 – volume: 49 start-page: 23 year: 2019 ident: 10.1016/j.cie.2025.111108_b0040 article-title: Improved NSGA-III with selection-and-elimination operator publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2019.05.011 – volume: 29 start-page: 3334 year: 2020 ident: 10.1016/j.cie.2025.111108_b0090 article-title: Selection of process parameters for near-net shape deposition in wire arc additive manufacturing by genetic algorithm publication-title: Journal of Materials Engineering and Performance doi: 10.1007/s11665-020-04847-1 – volume: 187 year: 2022 ident: 10.1016/j.cie.2025.111108_b0095 article-title: In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing publication-title: Measurement doi: 10.1016/j.measurement.2021.110232 – start-page: 13 year: 2013 ident: 10.1016/j.cie.2025.111108_b0085 article-title: Dimensionality reduction with unsupervised nearest neighbors publication-title: K-nearest Neighbors – volume: 99 start-page: 2025 year: 2018 ident: 10.1016/j.cie.2025.111108_b0155 article-title: Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel publication-title: The International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-018-2373-3 – volume: 69 start-page: 630 year: 2021 ident: 10.1016/j.cie.2025.111108_b0125 article-title: Image processing approach to automate feature measuring and process parameter optimizing of laser additive manufacturing process publication-title: Journal of Manufacturing Processes doi: 10.1016/j.jmapro.2021.07.064 – volume: 47 start-page: 31 issue: 1 year: 2017 ident: 10.1016/j.cie.2025.111108_b0145 article-title: Random forest publication-title: Journal of Insurance Medicine doi: 10.17849/insm-47-01-31-39.1 – volume: 12 start-page: 282 year: 2016 ident: 10.1016/j.cie.2025.111108_b0175 article-title: Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models publication-title: Additive Manufacturing doi: 10.1016/j.addma.2016.05.009 – volume: 87 start-page: 357 issue: 3 year: 2012 ident: 10.1016/j.cie.2025.111108_b0140 article-title: Meta-learning for evolutionary parameter optimization of classifiers publication-title: Machine Learning doi: 10.1007/s10994-012-5286-7 – volume: 40 start-page: 144 year: 2019 ident: 10.1016/j.cie.2025.111108_b0020 article-title: Research on parameters optimization for the additive manufacturing process publication-title: Transportation Research Procedia doi: 10.1016/j.trpro.2019.07.024 – volume: 31 start-page: 189 year: 2020 ident: 10.1016/j.cie.2025.111108_b0060 article-title: Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement publication-title: CIRP Journal of Manufacturing Science and Technology doi: 10.1016/j.cirpj.2020.05.009 – volume: 17 start-page: 971 issue: 5 year: 2024 ident: 10.1016/j.cie.2025.111108_b0070 article-title: Pathways toward the use of Non-destructive micromagnetic analysis for porosity assessment and process parameter optimization in additive manufacturing of 42CrMo4 (AISI 4140) publication-title: Materials doi: 10.3390/ma17050971 – volume: 74 start-page: 1187 year: 2014 ident: 10.1016/j.cie.2025.111108_b0030 article-title: A new method for determining and improving the accuracy of a powder bed additive manufacturing machine publication-title: The International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-014-6012-3 – volume: 111 start-page: 77 issue: 1–2 year: 2020 ident: 10.1016/j.cie.2025.111108_b0105 article-title: Estimation of clad geometry and corresponding residual stress distribution in laser melting deposition: Analytical modeling and experimental correlations publication-title: The International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-020-06047-6 – volume: 102 start-page: 1645 year: 2018 ident: 10.1016/j.cie.2025.111108_b0185 article-title: Development and application of artificial neural network publication-title: Wireless Personal Communications doi: 10.1007/s11277-017-5224-x – volume: 19 start-page: 62 issue: 3 year: 2019 ident: 10.1016/j.cie.2025.111108_b0005 article-title: Multinomial Naive Bayes classification model for sentiment analysis publication-title: IJCSNS - International Journal of Computer Science and Network – volume: 181 start-page: 1653 issue: 3 year: 2007 ident: 10.1016/j.cie.2025.111108_b0025 article-title: SMS-EMOA: Multiobjective selection based on dominated hypervolume publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2006.08.008 – volume: 4 start-page: 48 issue: 2 year: 2020 ident: 10.1016/j.cie.2025.111108_b0050 article-title: Dynamic analysis and management optimization for maritime supply chains using nonlinear control theory publication-title: Journal of International Maritime Safety, Environmental Affairs, and Shipping doi: 10.1080/25725084.2020.1784530 – volume: 8 start-page: 12 year: 2015 ident: 10.1016/j.cie.2025.111108_b0160 article-title: An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control publication-title: Additive Manufacturing doi: 10.1016/j.addma.2015.07.002 – volume: 76 start-page: 457 year: 2022 ident: 10.1016/j.cie.2025.111108_b0080 article-title: Towards hybrid laser-arc based directed energy deposition: Understanding bead formation through mathematical modeling for additive manufacturing publication-title: Journal of Manufacturing Processes doi: 10.1016/j.jmapro.2022.02.027 – volume: 16 start-page: 205 issue: 2 year: 2022 ident: 10.1016/j.cie.2025.111108_b0170 article-title: A review of the recent development in machining parameter optimization publication-title: Jordan Journal of Mechanical and Industrial Engineering – volume: 86 year: 2024 ident: 10.1016/j.cie.2025.111108_b0200 article-title: Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach publication-title: Additive Manufacturing doi: 10.1016/j.addma.2024.104208 – volume: 144 year: 2023 ident: 10.1016/j.cie.2025.111108_b0130 article-title: A comparative study on evolutionary multi-objective algorithms for next release problem publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2023.110472 – volume: 24 start-page: 214 issue: 1 year: 2018 ident: 10.1016/j.cie.2025.111108_b0010 article-title: Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry publication-title: Rapid Prototyping Journal doi: 10.1108/RPJ-04-2016-0059 – volume: 40 start-page: 2096 issue: 4 year: 2024 ident: 10.1016/j.cie.2025.111108_b0065 article-title: Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method publication-title: Quality and Reliability Engineering International doi: 10.1002/qre.3513 – volume: 77 start-page: 13 year: 2022 ident: 10.1016/j.cie.2025.111108_b0180 article-title: Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions publication-title: Journal of Manufacturing Processes doi: 10.1016/j.jmapro.2022.02.053 |
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