Volume 1 Issue 3
May  2021
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Danni BAI, Pengfei GAO, Xinggang YAN, Yao WANG. Intelligent forming technology: State-of-the-art review and perspectives[J]. Journal of Advanced Manufacturing Science and Technology , 2021, 1(3): 2021008. doi: 10.51393/j.jamst.2021008
Citation: Danni BAI, Pengfei GAO, Xinggang YAN, Yao WANG. Intelligent forming technology: State-of-the-art review and perspectives[J]. Journal of Advanced Manufacturing Science and Technology , 2021, 1(3): 2021008. doi: 10.51393/j.jamst.2021008

Intelligent forming technology: State-of-the-art review and perspectives

doi: 10.51393/j.jamst.2021008

This work was financially supported by the National Natural Science Foundation of China (No. 92060107, 51875467), the National Key R&D Program of China (No. 2020YFA0711100), the National Science Fund for Distinguished Young Scholars of China (No. 51625505), and the Young Elite Scientists Sponsorship Program by CAST (No. 2018QNRC001).

  • Received Date: 2021-04-02
  • Rev Recd Date: 2021-04-18
  • Available Online: 2021-05-19
  • Publish Date: 2021-05-19
  • The rapid development of artificial intelligence (AI) technology makes it possible for achieving intelligent forming. It will bring great breakthrough of material forming technology, realizing the unmanned watching, intelligent processing design and intelligent control during forming process. Moreover, it can greatly improve the forming accuracy, mechanical properties, forming efficiency and economic benefits, and promote the continuous emergence of new forming technology. Thus, the intelligent forming technology, integrating AI technology and advanced forming technology, has become an international research focus. This paper reviews the recent developments of intelligent forming technology from four kinds of common forming technology, i.e., intelligent casting, intelligent plastic forming, intelligent welding, and intelligent additive manufacturing. Moreover, the current research issues and future trends of intelligent forming technology are put forward at the end of the paper.

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  • [1]
    . Li WQ. Application of hydraulic forming lightweight technology for subframe. Equip Manuf Technol 2018;1:215-218[Chinese].
    . Hua L, Wei PF, Hu ZL. Green and intelligent forming technology and its applications for High Strength Lightweight Material. Chin J Mech Eng 2020;31(22):2753-2762+2771[Chinese].
    . Enea S, Seung KM. Additive manufacturing for space:status and promises. Int J Adv Manuf Technol 2019;105(5):4123-4146.
    . Chen L, Chuan ZH, Xiang YZ, et al. Design, manufacturing and applications of auxetic tubular structures:A review. Thin-Walled Struct 2021;163:107682.
    . Polyblanka JA, Allwooda JM, Duncan SR. Closed-loop control of product properties in metal forming:A review and prospectus. J Mater Process Technol 2014;214:2333-2348.
    . Mehta N, Gohil AV, Dave KG, et al. Development of casting defect analysis module through integrated approach for small and medium scale industries. Mater Today:Proc 2021;38:2935-2942.
    . Music O, Allwood JM. The use of spatial impulse responses to characterise flexible forming processes with mobile tools. J Mater Process Technol 2012;212:1139-1156.
    . Cheng YC, Wang QY, Jiao WH, et al. Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding. J Manuf Process 2020;56:908-915.
    . Farshidianfar MH, Khodabakhshi F, Khajepour A, et al. Closed-loop control of microstructure and mechanical properties in additive manufacturing by directed energy deposition. Mater Sci & Eng A-Struct Mater Prop Microstruct Process 2021;803:140483.
    . Zhou J. Intelligent manufacturing-main direction of "Made in China 2025". Chin J Mech Eng 2015;26(17):2273-2284[Chinese].
    . Li DQ. Integrating digital and networked intelligent technology to help innovate and develop material forming manufacturing-foreword of material digital and intelligent forming album. Chin J Mech Eng 2020;31(22):2647[Chinese].
    . Wang LH. From intelligence science to intelligence manufacturing. Engineering 2019;5:615-618.
    . Zhou J, Li PG, Zhou YH, et al. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11-20.
    . Raj PAC, Kavitha P, Sophia S, et al. IoT based stir casting system of aluminum MMC. Mater Today:Proc 2021;16.
    . Liang L, Guo LG, Wang YF, et al. Towards an intelligent FE simulation for real-time temperature-controlled radial-axial ring rolling process. J Manuf Process 2019;48:1-11.
    . Mahadevan R, Jagan A, Pavithran L, et al. Intelligent welding by using machine learning techniques. Mater Today:Proc 2021;16.
    . Yang W, Jian R. Research on Intelligent Manufacturing of 3D Printing/Copying of Polymer. Adv Ind Eng Polym Res 2019;2(2):88-90.
    . Liang C. Development of high-quality, efficient and green foundry industry-interview with Tianyou Huang, vice president of China Foundry Association. Aeronaut Manuf Technol 2011;68-70[Chinese].
    . Ji N, Hong Y. Development of Intelligent Casting Technology Visualization and Expert System for Casting. China Soc Auto Eng 2017;11.
    . Ji XY, Zhou JX, Yin YJ, et al. Research on the digital and intelligent casting technology and its application in casting enterprise. Chin Mech Eng Soc 2014;14[Chinese].
    . Development status and trend of intelligent manufacturing in foundry industry. Foundry Eng 2020;44(06):76[Chinese].
    . Liu JY. What is intelligent manufacturing in magnesium alloy casting. Chin J Nonferrous Met 2021;4:44-45[Chinese].
    . He W. Intelligent control system for automobile brake disc investment casting based on PLC. Hot Work Technol 2020;49(03):84-88[Chinese].
    . Chu Q, Liang JD. Design of ultrasonic high pressure casting intelligent control system based on PLC. Hot Work Technol 2019;48(23):89-92[Chinese].
    . Shi GF. Research on intelligent control of aluminum alloy wheel hub low pressure casting based on PLC. Chin J Nonferrous Met 2019;11:279-280[Chinese].
    . Wang S, Zhang X, Li XY. An intelligent screening system for profiled aluminum ingots based on MES System. Metall Ind Autom 2020;44(S1):15-17[Chinese].
    . Xu SL, Zhang QW, Bai XS, et al. Design and application of intelligent flexible production linefor investment casting shell mold. Foundry Technol 2021;42(1):43-45[Chinese].
    . Sun GY, Li GY, Gong ZH, et al. Multiobjective robust optimization method for drawbead design in sheet metal forming. Mater Des 2010;31(4):1917-1929.
    . Jansson T, Nilsson L, Moshfegh R. Reliability analysis of a sheet metal forming process using Monte Carlo analysis and metamodels. J Mater Process Technol 2008;202(1-3):255-268.
    . Kim H, Altan T, Yan QG. Evaluation of stamping lubricants in forming advanced high strength steels (AHSS) using deep drawing and ironing tests. J Mater Process Technol 2009;209(8):4122-4133.
    . Hamedon Z, Mori K, Abe Y. In-situ measurement of three-dimensional deformation behaviour of sheet and tools during stamping using borescope. J Mater Process Technol 2014;214(4):945-950.
    . Fischer JD, Woodside MR, Gonzalez MM, et al. Iterative learning control of single point incremental sheet forming process using digital image correlation. Proc Manuf 2019;34:940-949.
    . Ren HQ, Xie JX, Liao SH, et al. In-situ springback compensation in incremental sheet forming. CIRP Annals 2019;68(1):317-320.
    . Dong YX, Huang SS, Xue JX, et al. Condition and expectation of intelligent control in welding processes. J S Univ Technol 2001;23(1):18-20+42.
    . Ahadevan RM, Jagan A, Pavithran, L, et al. Intelligent welding by using machine learning techniques, Mater Today:Proc 2021;2.
    . Wang BC, Hu SJ, Sun L, et al. Intelligent welding system technologies:State-of-the-art review and perspectives. J Manuf Syst 2020;56.
    . Huang YM, Yuan YX, Yang LJ, et al. Real-time monitoring and control of porosity defects during arc welding of aluminum alloys. J Mater Process Technol 2020;286:116832.
    . Wang QY, Jiao WH, Zhang YM. Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control. J Manuf Syst 2020;57:429-439.
    . Zhang YX, You DY, Gao XD, et al. Online Monitoring of Welding Status Based on a DBN Model During Laser Welding. Engineering 2019;57:671-678.
    . Heigel JC, Michaleris P, Reutzel EW. Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti-6Al-4V. Addit Manuf 2015;5:9-19.
    . Gohari H, Barari A, Kishawy H, et al. Intelligent Process Planning for Additive Manufacturing. IFAC-PapersOnLine 2019;52(10):218-223.
    . Ren K, Chew Y, Zhang YF, et al. Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Comput Meth Appl Mech Eng 2020;362(C):112734-112734.
    . Zhou ZY, Shen HY, Liu B, et al. Thermal field prediction for welding paths in multi-layer gas metal arc welding-based additive manufacturing:A machine learning approach. J Manuf Processes 2021;64:960-971.
    . Khanzadeh M, Chowdhury S, Marufuzzaman M, et al. Porosity prediction:supervised-learning of thermal history for direct laser deposition. J Manuf Syst 2018;47:69-82.
    . Scime L, Beuth J. Using machine learning to identify in-Situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf 2018;25:151-165.
    . Tian Q, Guo S, Melder E, et al. Deep learning-based data fusion method for in situ porosity detection in laser-based additive manufacturing. J Manuf Sci Eng 2020;143(4):1-38.
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