Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Abstract

This paper presents an experimental study carried out on Nimonic 263 alloy sheets to determine the optimal combination of laser cutting control factors (assisted gas pressure, beam focus position, laser power, and cutting speed), with respect to multiple characteristics of the cut area. With the aim of designing laser cutting parameters that satisfy the specifications of multiple responses, an advanced multiresponse optimization methodology was used. After the processing of experimental data to develop the process measure using statistical methods, the functional relationship between cutting parameters and the process measure was determined by artificial neural networks (ANNs).Using the trained ANN model, particle swarm optimization (PSO) was employed to find the optimal values of laser cutting parameters. Since the effectiveness of PSO could be affected by its parameter tuning, the settings of PSO algorithm-specific parameters were analyzed in detail. The optimal laser cutting parameters proposed by PSO were implemented in the validation run, showing the superior cut characteristics produced by the optimized parameters and proving the efficacy of the suggested approach in practice. In particular, it is demonstrated that the quality of the Nimonic 263 cut area and the microstructure were significantly improved, as well as the mechanical characteristics.Parameters of the laser cutting process must be properly tuned in order to obtain the desired outputs. The laser power must be sufficient to enable the cutting; the cutting speed should be high enough to prevent the diffusion of heat in a material and to form a broad heat-affected zone (HAZ) [4]. The assisting gas is very important for producing a high-quality cut without a grate, since it does not permit molten material droplets' solidification onto the cut surface. The cutting speed must be balanced; if excessive speed is used the grate would remain, while, with a very low speed, the edge quality of the cut would be affected and a wide HAZ would form [5].The cutting control factors considered in this study are the assisting gas pressure, the position of the beam focus, the laser power, and the cutting speed. At the output, seven responses of the cut area are observed. Since the process is characterized by multiple parameters and responses, an advanced optimization methodology is needed to obtain the optimal cutting setting that satisfies the specifications for multiple, correlated responses. Simulated annealing (SA) and a genetic algorithm (GA) have been employed previously, and it has been demonstrated that SA outperformed GA in the proposed method [6]. Particle swarm optimization (PSO) is used in this study and benchmarked with SA in terms of the quality, i.e., the accuracy of the obtained optimum, the effects of the algorithm's parameters on the obtained optimum, and the convergence speed. Since metaheuristic algorithms must be adequately tuned to obtain the right solution, the setting of the major PSO algorithm's parameters is studied in detail to evaluate th...

Energy Efficiency in CO2 Laser Processing of Hardox 400 Material

“…In the past few years, researchers studied the possibility of cutting with a CO 2 laser of different materials such as methyl polymethacrylate [16], Al 6061-T6 alloy [17], 304 stainless steel [18], AISI 1045 austenitic steel [19], AlSI 304, AlMg3 and St37-2 [20], SS41 and SUS304 [21], pure titanium and Ti-6Al-4V titanium alloys [22], Ti6Al4V alloy [23] and Nickel-based super alloys Nimonic 263 [24].…”

“…Sibalija et al analysed the following parameters: cutting speed, auxiliary gas pressure, and laser power focusing position on the 2 mm thick Nimonic 263 alloy. The authors optimized the cutting parameters and determined the combinations that lead to an increase in the quality of the cutting area, through use of the method of artificial neural networks [24]. Eltawahni et al applied a Box Behneken method to CO 2 laser cutting of AISI316L stainless steel.…”

An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters

“…r 1 and r 2 are the random numbers between 0 and 1, w represents inertia weight coefficient, which provides a balance between exploration and exploitation. PSO has been widely utilized by researchers as it is easy to implement, has fewer controlling variables, offers less computational burden and provides optimal results [54,55]. However, while solving multidimensional OF (having several local minima), PSO exhibits a lethargic search mechanism [51], providing a low-quality solution while forcing the algorithm to converge prematurely [13].…”

Cognitive capabilities for the CAAI in cyber-physical production systems

“…For an insight into the utilization of different strategies to optimize metaparameters (tuning) and performance analysis, we refer to [11]. A recent example showing the importance of parameter tuning of PSO for a real-world manufacturing problem can be found in [40]. Surrogate This class of optimizers in our approach uses a surrogate model (e.g., a regression model) as a replacement for the (potentially costly) objective function to estimate the objective function (via model predictions) relatively cheap (in comparison to the objective function itself).…”