Development of automatic orbital pipe MIG welding system and process parameters’ optimization of AISI 1020 mild steel pipe using hybrid artificial neural network and genetic algorithm

Mengistie, Amanuel Kassa; Bogale, Teshome Mulatie

doi:10.1007/s00170-023-11796-1

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…Due to these, the control factors were current, speed, voltage and gas flow rate for welding the selected metal and forecasting ultimate tensile strength, bending strength and Rockwell hardness strength. The levels of selected welding parameters shown on table 2 were also chosen based on the specifications of the TIG welding equipment having Model-EURO TIG 200 AC/DC; Make-Helvi; Italy, utilized in this study and by reviewing previous researches that were in the same context as the welding parameter decision [2,15,16]. The four control parameters had three levels; besides, there were also the fixed TIG welding parameters that are listed on table 3 during this experimental work.…”

Section: Parameters and Orthogonal Array Sectionsmentioning

confidence: 99%

“…Four welding parameters with their three levels were considered for this study, with two alternatives L9 and L27 orthogonal arrays were being viable under Taguchi design. The L27 might have higher prediction accuracy; however, L9 was provided to minimize the number of runs and cost [2,[15][16][17]; as a result, the selected orthogonal array was L9 that is shown on table 4.…”

Section: Parameters and Orthogonal Array Sectionsmentioning

confidence: 99%

“…And, it was assumed that as gas flow rate increased, cooling rate would also increase. In contexts of these, locations and types of fractures that were observed on tensile tested specimens on figure 3(b) are briefly summarized in table 5 with their possible causes [15].…”

Section: Visual Inspectionmentioning

confidence: 99%

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Optimization of TIG welding process parameters on 304 austenitic stainless steel sheet metal using fuzzy logic based Taguchi method

Abebe,

Bogale

2023

Eng. Res. Express

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TIG welding can be used to produce excellent weld quality and precise welding operation for sheet metals. The aim of this study was to get the best welding parameters for enhancing ultimate tensile strength, bending strength and Rockwell hardness of the butt-weld joint. The experimental work was used, and the experiment was carried out on 2 mm thickness of 304 austenitic-stainless steel sheet metal using the L9 orthogonal array of the Taguchi design. Automated TIG welding fixture was developed to control the welding speed accurately. The selected welding parameters were welding speed, current, voltage and gas flow rate with their three levels. Based on the fuzzy logic based Taguchi method, the best optimal levels of parameters were found at the values of 110 A of current, 13.86 cm/min of speed, 17.5 V of voltage and 7.5 L/min of gas flow rate. The analysis results of ANOVA showed that gas flow rate and current were found as the significant factors, and the contributions of the gas flow rate, current, speed and error were 47.63%, 34.34%, 16.49% and 1.54%, respectively. According to the confirmation tests, the multi response performance index mean value of the confirmatory test of 0.6068 was found between the 95% confidence interval of 0.5028 and 0.7314, and the maximum ultimate tensile strength, bending strength and Rockwell hardness were obtained 614.8 MPa, 765.32 MPa and 95.3 HRB, respectively.

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Section: Parameters and Orthogonal Array Sectionsmentioning

confidence: 99%

Section: Parameters and Orthogonal Array Sectionsmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of TIG welding process parameters on 304 austenitic stainless steel sheet metal using fuzzy logic based Taguchi method

Abebe,

Bogale

2023

Eng. Res. Express

Self Cite

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“…As results, the control parameters were current, speed, gas pressure, and standoff distance for cutting the selected metal to forecast material removal rate, surface roughness and nozzle diameter change. The levels of selected cutting parameters shown on Table 2 were also chosen based on the specifications of plasma cutting machine which was utilized in this study and by reviewing previous researches that were in the same context as the cutting parameter decision [22][23][24].…”

Section: Cutting Parameters and Orthogonal Array Selectionsmentioning

confidence: 99%

“…In this stage, weight optimization was occurred, new weights were extracted by changing the constant parameters and MSE error for weight were calculated. Finally, using the selection function, the parents were chosen for the following stage after sorting the responses [23,[31][32][33]. The fitness of all initial populations was calculated using the fitness function based on the neural network model, which was produced at random.…”

Section: Ga Based Optimizationmentioning

confidence: 99%

Parameters optimization in plasma arc cutting of AISI 1020 mild steel plate using hybrid genetic algorithm and artificial neural network

Melaku,

Bogale

2023

Int. J. Simul. Multidisci. Des. Optim.

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The aim of this study was to optimize the cutting parameters such as cutting speed, standoff distance, cutting current and gas pressure of the CNC plasma arc cutting process that affected the material removal rate, surface roughness and nozzle diameter change after cutting performed on AISI 1020 mild steel plate. Three levels of variation were taken to the four cutting parameters that were chosen. Twenty-seven trial experiments were carried out using L27 orthogonal array of Taguchi design. In this experimental investigation, the highest material removal rate (MRR) of 8.96 g/s, Ra surface roughness (SR) of 15.734 µm and nozzle orifice diameter (ND) of 1.4637 mm were achieved, whereas the lowest obtained values of MRR, SR and ND were 2.324 g/s, 5.98 µm and 1.2114 mm, respectively. For modeling the plasma arc cutting process experimental input parameters and responses' results, a hybrid ANN-GA model was constructed. This model was used to forecast and optimize MRR, SR and ND, as well as the control factors that go with it. The results indicated that the ANN-GA model could predict the output responses with a mean square error of 1.06885e–1. During optimization, a 4-9-3 network trained with neural network of back propagation by Levenberg-Marquardt algorithm was used to have the greatest prediction capability, with optimum values of MRR, SR and ND of 7.0032 g/s, 4.2062 µm and 1.3142 mm, respectively. From the confirmation tests, the average results of 6.9247 g/s of MRR, 4.3429 µm of SR and 1.3703 mm of ND were obtained. The percentage of errors between the ANN-GA predicted optimal responses' results and the confirmatory experimental results were found 1.121%, 3.250% and 4.269% for MRR, SR and ND, respectively.

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Tensile strength prediction and process parameters optimization of FSW thick AA2219-T8 based on ANN-GA

Yang,

Lu,

Sun

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Development of automatic orbital pipe MIG welding system and process parameters’ optimization of AISI 1020 mild steel pipe using hybrid artificial neural network and genetic algorithm

Cited by 3 publications

References 21 publications

Optimization of TIG welding process parameters on 304 austenitic stainless steel sheet metal using fuzzy logic based Taguchi method

Optimization of TIG welding process parameters on 304 austenitic stainless steel sheet metal using fuzzy logic based Taguchi method

Parameters optimization in plasma arc cutting of AISI 1020 mild steel plate using hybrid genetic algorithm and artificial neural network

Tensile strength prediction and process parameters optimization of FSW thick AA2219-T8 based on ANN-GA

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