Inclusion of IoT technology in additive manufacturing: Machine learning-based adaptive bead modeling and path planning for sustainable wire arc additive manufacturing and process optimization

Abstract

Industrial civilization transforms current cutting edge technologies and the evolution of Industry 5.0 is more aggressive with the use of IoT-enabled smart machines and robots in the manufacturing sector today. IoT technology deals with digital data as in additive manufacturing (AM). The potential and progressive aspects of AM embarks for functional part development instead of initial prototyping. AM is one of large-scale production with less buy-to-fly (BTF) ratio. In the present work, a novel framework has been proposed and utilized to attain adaptive bead modeling and an appropriate path plan for enhanced deposition and surface quality of weld beads. Further, the influence of input process parameters toward sustainable wire arc additive manufacturing (WAAM) is also focused. Machine learning-based hybrid-TLBO (h-TLBO) and support vector machine (SVM) is deployed for the optimization process. With the aid of graph theory, weights are estimated for h-TLBO. The overall process parameters and entire data module is handled with IoT technology and can be accessed for processing. The simulated post-processing results are validated experimental test results and found to be in good concurrence.