Laser transmission welding (LTW) is a widely used polymer welding technology in industries today. The performance of LTW is governed by a number of process parameters, and fine-tuning those parameters is critical for the process to achieve the intended results. Optimization of process parameters for enhancing LTW performance, like weld strength and weld width, is always tricky since they are inherently contradictory. Therefore, in this research, an effort is made to optimize the LTW parameters utilizing the three best-known optimization approaches, and the performance of those optimization approaches in LTW process optimization is compared. First, the response surface method (RSM) is employed to build empirical equations that establish an empirical correlation between process variables and desired performance features. These empirical equations are then employed as objective functions for process optimization utilizing the RSM-based desirability function approach (DFA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) algorithms. The performance of the chosen optimization approaches is compared with reference to optimum results, accuracy, convergence rate, and computing time. PSO and TLBO algorithms outperform the DFA approach for single and multi-objective optimizations. TLBO is found to have faster convergence, whereas PSO takes less time for computation.

The Transmission welding using incremental scanning technique (TWIST) combines linear feed with an oscillating laser beam to enhance weld quality and expand the process window. However, TWIST welding is influenced by nonlinear process variables, and achieving multiple objectives concurrently is challenging due to conflicting performance attributes. In industrial practice, time constraints and project specifications limit the effectiveness of methodologies tailored to specific workpiece materials or single performance optimization. The present study employs an artificial neural network (ANN) to establish a correlation between TWIST welding parameters and desired performance attributes. Various ANN model architectures are evaluated, with the 5-11-6-2 architecture achieving the highest accuracy (correlation coefficient of 0.998). For multi-objective optimization, the non-dominated sorted genetic algorithm (NSGA-II) and non-dominated sorted teaching learning-based optimization (NSTLBO) algorithm are employed, utilizing the ANN model's fitness function as the objective. The newly developed two-step model provides operators with the flexibility to prioritize factors based on project requirements, resulting in improved outcomes. Comparative analysis of the algorithms using seven metrics demonstrates that NSGA-II outperforms NSTLBO in solution prediction, albeit with slightly increased computing time. NSGA-II offers a broader range of Pareto optimum solutions compared to NSTLBO, which converges narrowly and restricts non-dominated sets. Validation experiments confirm the adequacy of both algorithms, supporting the effectiveness of the two-step model. The proposed methodology enables practitioners to achieve better weld quality, accommodate conflicting performance attributes, and effectively optimize multiple objectives in industrial applications.

Owing to the increasing use of polymers in microfluidic devices, lightweight automotive and modern industries, laser transmission welding has evolved as a reliable method over the years. In this paper, sensitivity analysis has been performed to find out the critical parameters and to compare the relative impact of process variables on weld quality. Two transparent PMMA materials have been joined using low power laser with 1064 nm wavelength. The results are analyzed for laser transmission welding processes by considering corresponding parameters such as laser power, pulse frequency and scanning speed. Scanning speed is found to be dominant parameter based on statistical method while sensitiveness of weld strength and weld width are maximum for laser power. Optimization of process variable helps to obtain desired responses. A novel metaheuristic Teaching Learning Based Algorithm has been used to maximize the weld strength and minimize the weld width. Copyright (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Applied Research and Engineering 2021

Laser transmission welding (LTW) is a proven technique, but it is not yet at its peak. Polymeric products are increasingly being used in various applications, including packaging, appliances, medical electronics, automobile, and aerospace, due to their ease of manufacture, low cost, and recycling capability.LTW is a cutting-edge welding technique used to join polymers and plastics. The key benefits of LTW over traditional plastic welding methods are the contactless and vibration-free energy input, precise control of the applied energy, and the resulting reproducibility and low thermal load of the components. However, material restriction continues to be a significant issue when it comes to the welding of polymers. The researchers are focusing on LTW of polymer combinations that are difficult to weld due to differing degrees of incompatibility. This article covers many of the critical scientific and technological aspects of laser welding of thermoplastic materials, demonstrating the process fundamentals and how the degree of sophistication for LTW of polymers has progressed over time. This frame of reference addresses various techniques and novel ideas for polymer LTW. Furthermore, many LTW applications are presented, showing how rapidly the market accepted this novel technology.

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.

Laser transmission welding (LTW) is widely recognized as an effective joining method for thermoplastic polymers. Excellent mechanical, physical and thermal properties of polypropylene makes it suitable for many application in aerospace, automotive manufacturing and laboratory accessories; however, joining of semi-crystalline and amorphous polymers is very difficult due to their poor compatibility or different crystal structure. To overcome this problem, white ink is applied at the interface of weld to increase the solubility and retain the laser heat input. As white ink is an opaque, containing copolymer resins which helps to enhance the bonding strength. Here, acrylic (amorphous) and polypropylene copolymer (PPCP) (semi-crystalline) each of 4 mm thickness have been joined by low power diode laser. Experimental analysis of the effect of process parameters such as laser power, scanning speed and pulse frequency on the weld zone has been studied. Greater weld strength is achieved at low scan speed. A novel multi objective snap drift cuckoo search (SDCS) optimization technique has been implemented for betterment of weld strength and weld width. A scanning electron microscope (SEM) has been used to observe the welded zone morphology. It is observed that weld strength is primarily dependent on mechanical interlocking between the two layers of polymers.

This chapter presents the effect of various process parameters, namely laser power, pulse frequency, and welding speed, on the weld shear strength and weld width using a diode laser system. Here, laser transmission welding of transparent polycarbonate and black carbon filled acrylic each of 2.8 mm thickness have been performed to create lap joint by using low power laser. Response surface methodology is applied to develop the mathematical model between the laser welding process parameters and the responses of weld joint. The developed mathematical model is tested for its adequacy using analysis of variance and other adequacy measures. It has been observed that laser power and welding speed are the dominant factor followed by frequency. A confirmation test has also been conducted to validate the experimental results at optimum parameter setting. Results show that weld strength of 34.3173 N/mm and weld width of 2.61547 mm have been achieved at optimum parameter setting using desirability function-based optimization technique.

Owing to the growing applications in medical equipment, the marine sector and automotive industries, laser transmission welding (LTW) is gaining increasing importance. LTW of two transparent acrylic thermoplastic materials, each of 4 mm thickness, was demonstrated using a diode laser-pumped solid-state Nd:YVO4 pulsed laser. The effect of process parameters such as laser power, pulse frequency and scanning speed have been investigated and a multi-objective optimization based on desirability function technique was used for the betterment of the results. A validation test has been performed which shows that the predicted results are compatible with actual value. Scanning speed was found to be the process parameter that mostly affects the weld strength. A scanning electron microscope (SEM) was used to study the morphology of the weld zone. It was observed from SEM micrographs that the heating zone in the weld area often generates bubbles and ablation which enhance the joining strength.

Wire electric discharge machining (WEDM) has become a widely accepted electrothermal process for electrically conductive materials. Still, it puts a certain restriction that the limiting criteria of electrical conductivity should be 0.01 S/cm. To overcome this limiting criterion, a copper foil is applied over the workpiece surface. Due to application of copper foil, voltage drop and energy loss in workpiece material are reduced. Kerosene is used as dielectric fluid because it is a combustible hydrocarbon liquid, thereby, formation of continuous cracked carbon from working oil maintains the required electrical conductivity during machining. It is experimented that peak current and pulse on time are the most dominant factors that impact the machining characteristics. The optimum parameter setting obtained for best MRR and SR in WEDM is V-G = 30, T-ON = 80, T-OFF = 5, and I-P = 2.84. The surface texture of the machined sample is studied to estimate the responses.

This paper shows the impact of different process parameters and powder characteristics on the material removal rate and surface roughness obtained in surfactant added Powder Mixed Wire Electric Discharge Machining (PMWEDM). Inconel-718 is selected as the workpiece material, which has ample application in the industries handling environment of extreme stress, pressure and temperature. It has high work hardening properties along with high rupture strength, fatigue, and creep, making it extremely difficult to machine. So, additives having different thermo-physical properties are studied to improve the machining efficiency. The additives experimented includes aluminium, silicon carbide, graphite, and aluminium oxide. It is found that the electrostatic force present creates an agglomeration effect with dielectric additive powders, causing inhomogeneity in the mixture. So, a surfactant SPAN20 is used here to maintain the homogeneity of the mixture. The obtained MRR and SR are then modelled and optimised through Particle Swarm Optimization technique (PSO). It is observed that the addition of SPAN20 has improved the MRR by 13.56% and, SR by 45.05%. Also, it has been found that due to the combined abrasive action, abrasive powders increase MRR significantly than others. Furthermore, it is found that low grit size powders with lower density produces better machined surfaces.

Laser transmission welding is growing day by day with an increase of the uses of thermoplastic materials. This article presents the effect of various process parameters on weld strength and weld seam width obtained. The transparent polycarbonate and black carbon filled PMMA, each of 2.8 mm thickness have been joined by using low power laser. Here, effect of wobble frequency and wobble width are studied along with other process parameters. It is observed that weld seam width much depends upon the wobble width and the effect of wobble frequency is minimum. It has been observed that laser beam wobbling provides the greater weld strength by enlargement of joint area. Moreover, Beam wobbling plays a significant role to achieve better weld strength and weld width. Response surface methodology has been used to model the laser welding process parameters and responses of welding through regression analysis. The results of ANOVA reveal that the models formed appropriately predict the responses within the range of process parameters. A confirmation experiment has also been conducted to validate the results. A multi objective optimization has been used to find the optimum solution by Particle swarm optimization technique. (C) 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference of Materials Processing and Characterization.

Abrasive powder mixed Electrical discharge machining is a hybrid machining process providing both thermal and mechanical interactions. Addition of abrasive powders provides better discharge distribution along-with the abrasive action in dielectric. This paper analyses the effect of different parameters as Pulse on time, Pulse off time, Peak current and Gap voltage along-with the powder particle size and its concentration on MRR and SR while machining Inconel 718. SiC and Al2O3powders are investigated here. Regression analysis has been done to model responses and then their optimization is performed through PSO. Finally, the betterment has been proved through confirmation experiments.

Amorphous InSbX3 (X = Te or Se) thin films were obtained by thermal evaporation technique of bulk material on to well cleaned glass substrates. The current-voltage characteristics have been measured in the temperature range (303-393 K) and thickness range (230-490 nm). The obtained I-V curves revealed two types of conduction. The first region is ohmic type in the lower field followed by non-ohmic type of conduction in the high filed region. In the high-filed region, the field lowering coefficient β is evaluated, and has been analyzed by the anomalous Poole-Frenkel effect. The temperature dependence of ohmic current is that of thermally activated process. The variation of dielectric constant with temperature for the two compounds has been studied

In this paper, we have studied study the size effects of the ferroelectric nanotube phase diagrams and polar properties allowing for effective surface tension and depolarization field influence. The approximate analytical expression for the paraelectric-ferroelectric transition temperature dependence on the radii of nanotube, polarization gradient coefficient, extrapolation length, surface tension and electrostriction coefficient was derived. It was shown that the transition temperature could be higher than the one of the bulk material for negative electrostriction coefficient. Therefore we predict conservation and enhancement of polarization in long ferroelectric nanotubes. Obtained results explain the observed ferroelectricity conservation and enhancement in Pb (Zr, Ti) O3 and BaTiO3 nanotubes.