Multi-response optimization of Nd:YAG laser cutting parameters of Ti-6Al-4V superalloy sheet

Tamilarasan, A.; Rajamani, D.

doi:10.1007/s12206-017-0133-1

Cited by 48 publications

(18 citation statements)

References 21 publications

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“…Sharma and Yadava [17] showed that the use of oxygen as the assisting gas in the laser cutting of a nickel alloy could promote a smooth cut surface. Tamilarasan and Rajamani [18] optimized the cutting parameters of Ti alloy samples and achieved a relative error of less than 2%. Pandey and Dubey [19] used pulsed Nd:YAG laser to cut the titanium alloy and showed that lower pulse width and pulse frequency and higher cutting speed result in a better cut.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Šibalija

Petronić²,

Milovanović

2019

Metals

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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...

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Section: Literature Reviewmentioning

confidence: 99%

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

Šibalija

Petronić²,

Milovanović

2019

Metals

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“…Metal removed from the workipece MRR g Cutting time  (4) MRR is quantified using an Infra IN210 (Infra Instruments Ltd, Chennai, India) electronic weight balance with a precision of 0.0001 gms. Similarly, the kerf taper ( Figure 3) is measured on an automated profile projector (Scientico Instruments, India) and the following relation is used to evaluate KT:…”

Section: Methodsmentioning

confidence: 99%

“…In addition, advanced metal cutting processes such as laser cutting, abrasive waterjet cutting, electrical discharge machining, plasma arc cutting, ultrasonic machining, etc. are very well suited for processing these kinds of difficult-to-cut materials [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis

et al. 2020

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Nickel-based alloys, especially Monel 400™, is gaining its significance in diverse applications owing to its superior mechanical properties and high corrosion resistance. Machining of these materials is extremely difficult through the traditional manufacturing process because of their affinity to rapid work hardening and deprived thermal conductivity. Owing to these difficulties a well-established disruptive metal cutting process namely plasma arc cutting (PAC) can be widely used to cut the sheet metals with intricate profiles. The present work focuses on an intelligent modeling of the PAC process and investigation on the multi-quality characteristics of PAC parameters using the fuzzy logic approach. The Box-Behnken response surface methodology is incorporated to design and conduct the experiments, and to establish the relationship between PAC parameters such as cutting speed, gas pressure, arc current, and stand-off distance and responses which include the material removal rate (MRR), kerf taper (KT), and heat affected zone (HAZ). The quadratic regression models are developed and their performances are assessed using the analysis of variance (ANOVA). Fuzzy set theory-based models are formulated to predict various responses using the Mamdani approach. Fuzzy logic and regression results are compared with the experimental data. A comparative evaluation predicted an average error of 0.04% for MRR, 0.48% for KT, and 0.46% for HAZ, respectively. The effect of variations in PAC process parameters on selected responses are estimated through performing the sensitivity analysis.

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“…A least variation is noted between the forecast data and the actual data. 51 In addition, regression F-value 91.828 implies that the developed GRG models are significant. From ANOVA, it can be seen that some terms are reflected as trivial terms because their 'Probability .…”

Section: Adequacy Of Developed Modelmentioning

confidence: 99%

Prediction and analysis of surface roughness in selective inhibition sintered high-density polyethylene parts: A parametric approach using response surface methodology–grey relational analysis

Rajamani

Ziout

Esakki

et al. 2018

Advances in Mechanical Engineering

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Selective inhibition sintering (SIS) process intends to produce near-net-shape components through sintering of specific region of powder particles. The prediction of surface quality in SIS parts is a challenging task due to its complex part building mechanism and influence of abundant process parameters. Therefore, this study investigates the key contributing parameters such as layer thickness, heater energy, heater feedrate and printer feedrate on the surface quality characteristics (R a , R z and R q) of high-density polyethylene specimens fabricated through selective inhibition sintering process. The SIS system is custom built and experiments are conducted based on four-factor, three-level Box-Behnken design. The empirical models have been developed for predicting the influence of selected parameters on surface quality. The optimal process parameters such as the layer thickness of 0.1 mm, heater energy of 28.48 J/mm 2 , heater feedrate of 3.25 mm/s and printer feedrate of 110 mm/min are attained using grey relational multi-criteria decision-making approach. Furthermore, response surface analysis revealed that surface quality of sintered components is influenced significantly with heater energy and heater feedrate, followed by layer thickness. The confirmation experiments based on optimal process variables validate the developed grey relational analysis strategy.

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Multi-response optimization of Nd:YAG laser cutting parameters of Ti-6Al-4V superalloy sheet

Cited by 48 publications

References 21 publications

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

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

Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis

Prediction and analysis of surface roughness in selective inhibition sintered high-density polyethylene parts: A parametric approach using response surface methodology–grey relational analysis

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