REFERENCES
1. Huang SH, Liu P, Mokasdar A, Hou L. Additive manufacturing and its societal impact: a literature review. Int J Adv Manuf Technol 2013;67:1191-203.
2. Suárez L, Domínguez M. Sustainability and environmental impact of fused deposition modelling (FDM) technologies. Int J Adv Manuf Technol 2020;106:1267-79.
3. Griffiths C, Howarth J, De Almeida-rowbotham G, Rees A, Kerton R. A design of experiments approach for the optimisation of energy and waste during the production of parts manufactured by 3D printing. J Clean Prod 2016;139:74-85.
4. Enemuoh EU, Duginski S, Feyen C, Menta VG. Effect of process parameters on energy consumption, physical, and mechanical properties of fused deposition modeling. Polymers 2021;13:2406.
5. Galetto M, Verna E, Genta G. Effect of process parameters on parts quality and process efficiency of fused deposition modeling. Comput Ind Eng 2021;156:107238.
6. Elkaseer A, Schneider S, Scholz SG. Experiment-based process modeling and optimization for high-quality and resource-efficient FFF 3D printing. Appl Sci 2020;10:2899.
7. Camposeco-negrete C. Optimization of printing parameters in fused deposition modeling for improving part quality and process sustainability. Int J Adv Manuf Technol 2020;108:2131-47.
8. Al-ghamdi KA. Sustainable FDM additive manufacturing of ABS components with emphasis on energy minimized and time efficient lightweight construction. Int J Lightweight Mater Manuf 2019;2:338-45.
9. Hassan MR, Jeon HW, Kim G, Park K. The effects of infill patterns and infill percentages on energy consumption in fused filament fabrication using CFR-PEEK. Rapid Prototyping J 2021;27:1886-99.
10. Feng M, Hua Z, Hon KKB. A qualitative model for predicting energy consumption of rapid prototyping processes - a case of fused deposition modeling processe. IEEE Access 2019;7:184825-31.
11. Tian W, Ma J, Alizadeh M. Energy consumption optimization with geometric accuracy consideration for fused filament fabrication processes. Int J Adv Manuf Technol 2019;103:3223-33.
12. Alizadeh M, Esfahani MN, Tian W, Ma J. Data-driven energy efficiency and part geometric accuracy modeling and optimization of green fused filament fabrication processes. J Mech Des 2020;142:041701.
13. Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Pr 2020;138:106587.
14. Yang J, Li S, Wang Z, Yang G. Real-time tiny part defect detection system in manufacturing using deep learning. IEEE Access 2019;7:89278-91.
15. Xiong Y, Guo L, Tian D, Zhang Y, Liu C. Intelligent optimization strategy based on statistical machine learning for spacecraft thermal design. IEEE Access 2020;8:204268-82.
16. Xia C, Pan Z, Polden J, Li H, Xu Y, Chen S. Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. J Intell Manuf 2022;33:1467-82.
17. Li J, Cao L, Hu J, Sheng M, Zhou Q, Jin P. A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength. J Intell Manuf 2022;33:687-702.
18. Baturynska I, Martinsen K. Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms. J Intell Manuf 2021;32:179-200.
19. Cai R, Wang K, Wen W, Peng Y, Baniassadi M, Ahzi S. Application of machine learning methods on dynamic strength analysis for additive manufactured polypropylene-based composites. Polym Test 2022;110:107580.
20. Gor M, Dobriyal A, Wankhede V, et al. Density prediction in powder bed fusion additive manufacturing: machine learning-based techniques. Appl Sci 2022;12:7271.
21. Kumar P, Jain NK. Surface roughness prediction in micro-plasma transferred arc metal additive manufacturing process using K-nearest neighbors algorithm. Int J Adv Manuf Technol 2022;119:2985-97.
22. Ranjan N, Kumar R, Kumar R, Kaur R, Singh S. Investigation of fused filament fabrication-based manufacturing of ABS-Al composite structures: prediction by machine learning and optimization. J Mater Eng Perform 2023;32:4555-74.
23. Wu D, Wei Y, Terpenny J. Predictive modelling of surface roughness in fused deposition modelling using data fusion. Int J Prod Res 2019;57:3992-4006.
24. Li Z, Zhang Z, Shi J, Wu D. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robot Cim Int Manuf 2019;57:488-95.
25. Zhang L, Zhong YJ, Kan HY, Zhang BK. Optimization method for high energy efficiency process parameters in fused deposition manufacturing. J Mech Des Manuf 2021;3:149-52+6. (in Chinese).
26. Zhang L, Zhang BK, Bao H, Zhang C, Zhang WW. Carbon emissions quantitative methodology of product fused deposition manufacturing. J Mech Eng 2017;5:50-9. (in Chinese). Available from: https://d.wanfangdata.com.cn/periodical/jxgcxb201705006. [Last accessed on 27 Aug 2024]
27. Li CB, Zhu YT, Li L, Chen XZ. Multi-objective CNC milling parameters optimization model for energy efficiency. J Mech Eng 2016;21:120-9. (in Chinese). Available from: https://d.wanfangdata.com.cn/periodical/jxgcxb201621015. [Last accessed on 27 Aug 2024]