Beam wobbling effects on laser transmission welding of dissimilar polymers: Experiments, modeling, and process optimization. (February 2022) - Kumar, Dhiraj; Sarkar, Niladri Shekhar; Acherjee, Bappa; Kuar, Arunanshu Shekhar
Highlights: The effects of beam wobbling on laser transmission welding of dissimilar polymers are investigated. Statistical models are constructed to correlate welding parameters with desired responses. ANOVA confirms that the beam wobbling parameters have a substantial impact on weld strength. Parametric analysis and morphological study are used to examine the impact of beam wobbling. The process parameters are optimized to obtain desired weld strength and weld seam width. Abstract: Because of numerous advantageous characteristics of polymers and their expanding usage in microfluidic devices, automotive, household, packaging, and biomedical sectors, laser transmission welding (LTW) has emerged to meet the need for a potent polymer welding technology for industrial use. This paper presents an experimental investigation, mathematical modeling, and parameters optimization of wobble LTW of dissimilar transparent polymers. A low-power Nd:YVO4 laser is used to fuse transparent acrylic and polycarbonate plaques using a black marker ink line applied at the weld interface. Planned experiments and corresponding analyses are performed to develop the mathematical models and investigate the effect of beam wobbling on the process responses. The wobbling of the beam creates homogenized heat distribution and turbulence inside the weld pool, which improves material intermixing and joint strength. Morphological analysis reveals the presence of a number of tiny bubbles on the top surface of the weld bead, which strengthens the micromechanical joining at the weld interface. Artificial intelligence-based teaching learning-based optimization (TLBO) algorithm and desirability function analysis (DFA)-based optimization method are employed to improve the weld quality and to obtain the desired response. TLBO produces more accurate results than DFA because of its strong convergence towards global optima.