scholarly journals Investigation of the influence of incremental sheet forming process parameters using response surface methodology

2021 ◽  
Vol 118 (4) ◽  
pp. 401
Author(s):  
Belouettar Karim ◽  
Ould Ouali Mohand ◽  
Zeroudi Nasereddine ◽  
Thibaud Sébastien
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Damage Evolution ◽  
Sheet Forming ◽  
Thickness Reduction ◽  
Forming Process ◽  
Influence Of Process Parameters ◽  
Forming Angle ◽  
Tool Diameter

New methods in metal forming are rapidly developing and several forming processes are used to optimize manufacturing components and to reduce cost production. Single Point Incremental Forming (SPIF) is a metal sheet forming process used for rapid prototyping applications and small batch production. This work is dedicated to the investigation of the profile geometry and thickness evolution of a truncated pyramid. The influence of process parameters during a SPIF process is also studied. A numerical response surface methodology with a Design of Experiments (DOE) is used to improve the thickness reduction and the effects of the springback. A set of 16 tests are performed by varying four parameters: tool diameter, forming angle, sheet thickness, and tool path. The Gurson-Tvergaard-Needleman (GTN) damage model is used to analyze the damage evolution during material deformation. It is found that the model can effectively predict the geometrical profile and thickness with an error of less than 4%. Furthermore, it is noticed that the forming angle is the most influential parameter on the thickness reduction and springback level. Finally, the damage evolution is demonstrated to be sensitive to the forming angle.

scholarly journals RSM and BPNN Modeling in Incremental Sheet Forming Process for AA5052 Sheet: Multi-Objective Optimization Using Genetic Algorithm

Metals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1003 ◽  
Author(s):  
Xiao Xiao ◽  
Jin-Jae Kim ◽  
Myoung-Pyo Hong ◽  
Sen Yang ◽  
Young-Suk Kim
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Sheet Forming ◽  
Incremental Sheet Forming ◽  
Thickness Reduction ◽  
Multi Objective Optimization ◽  
Minimum Thickness ◽  
Multi Objective ◽  
Forming Angle ◽  
Tool Diameter

In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of AA5052 in incremental sheet forming (ISF). The optimization objectives were maximum forming angle and minimum thickness reduction whose values vary in response to changes in production process parameters, such as the tool diameter, step depth, tool feed rate, and tool spindle speed. A Box–Behnken experimental design was used to develop an RSM and BPNN model for modeling the variations in the forming angle and thickness reduction in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process using the GA. The results showed that RSM effectively modeled the forming angle and thickness reduction. Furthermore, the correlation coefficients of the experimental responses and BPNN predictions of the experiment results were good with the minimum value being 0.97936. The Pareto optimal solutions for maximum forming angle and minimum thickness reduction were obtained and reported. The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process.

Optimization in single point incremental forming of Inconel 718 through response surface methodology

2020 ◽  
Vol 44 (1) ◽  
pp. 148-160
Author(s):  
S. Pratheesh Kumar ◽  
S. Elangovan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Inconel 718 ◽  
Single Point ◽  
Optimization Technique ◽  
Sheet Forming ◽  
Incremental Sheet Forming ◽  
Forming Process ◽  
Profile Accuracy

Incremental sheet forming is a flexible and versatile process with a promising future in the batch production and prototyping sectors. With decreased design time and negligible production time, incremental sheet forming provides reliability, flexibility, and quality, while being an economical option in contrast to the traditional forming process. In this paper, Inconel 718, a material that has extensive use in aircraft engines, is considered for experimental work to obtain the optimum combination of process parameters. Response surface methodology is used to optimize the process parameters, in particular feed rate, step depth, and lubricant viscosity. The output responses are surface roughness, profile accuracy, and wall thickness. Analysis of variance (ANOVA) is performed using the experimental results to predict the statistical influence of the process parameters. The optimal combination of process parameters is further predicted using a numerical optimization technique to achieve better profile accuracy and surface finish. The results obtained are experimentally validated and are in good agreement with the predicted values.

scholarly journals Optimization of FDM 3D Printing Process Parameters on ABS based Bone Hammer using RSM Technique

2021 ◽  
Vol 1206 (1) ◽  
pp. 012001
Author(s):  
Umesh Kumar Vates ◽  
Nand Jee Kanu ◽  
Eva Gupta ◽  
Gyanendra Kumar Singh ◽  
Naveen Anand Daniel ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Layer Thickness ◽  
Process Parameters ◽  
Fused Deposition Modelling ◽  
Cross Sectional ◽  
Influence Of Process Parameters ◽  
Fused Deposition ◽  
Real Model ◽  
Nozzle Temperature

Abstract Rapid prototyping (RP) uses a cycle where a real model is made by explicitly adding material as thin cross-sectional layers. Fused deposition modelling (FDM) 3D printer is being use for synthesis of ABS based bone hammer. Response surface methodology (RSM) based L27 design of experiment were adopted to perform the experiment using four influencing parameters such as layer thickness, infill percentage, orientation and nozzle temperature for the three responses deflection, hardness and weight. Response surface methodology was used for modelling and optimization of considered process parameters. In present investigation, it is evident that bone hammer fabrication process parameters have been optimized on data such as bone hammer weight 19.8091g, hardness 104.5921 BHN, and force of 15 degree deflection 36.0681 N has been produced with RSM prediction with influence of process parameters such as layer thickness 0.250 mm, infill percentage 63.3333, orientation 60 degree, nozzle temperature 240°C.

Optimization of electrohydraulic forming process parameters using the response surface methodology

2021 ◽  
Vol 63 (6) ◽  
pp. 571-580
Author(s):  
Balasubramanian Arun Prasath ◽  
Pasupathy Ganesh ◽  
Karibeeran Shanmuga Sundaram
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Standoff Distance ◽  
Optimum Process ◽  
Forming Process ◽  
Electrohydraulic Forming ◽  
Acceptable Range ◽  
Steel Aisi ◽  
Experimental Values

Abstract This work’s main objective is to determine the optimum process parameters in the electrohydraulic forming (EHF) of austenitic stainless steel AISI 304 of 0.25 mm thickness for macro and micro shape. A truncated cone with grooves in the apex is considered as macro-micro shape. The response surface methodology (RSM) was developed for process variables such as voltage and standoff distance to determine the optimum parameters. To validate the model, confirmation experiments have been conducted, i. e. for the optimum value of voltage (V) = 8.935 kV and standoff distance (SOD) = 40.60 mm, and from the experiments the forming depth predicted is 9.221 mm and depth from the experiments is 9.5 mm. The percentage deviation from the predicted and experimental forming depth is 3.025 %, an acceptable range of less than 5 % for the surface roughness, the predicted value is 0.2598 microns, and the experimentally measured value is 0.268. The percentage deviation is 3.156 % between the predicted and experimental values, an acceptable range of less than 5 %. This shows that the model is suitable for predicting both responses. The validation experiments also found that the sheet fills one of the grooves and partially fills the other, which shows the capability of the electrohydraulic forming process. Confirmation experiments have been conducted.

The influence of process parameters on the properties of electrospun PLLA yarns studied by the response surface methodology

2014 ◽  
Vol 132 (5) ◽  
pp. n/a-n/a ◽  
Author(s):  
Homa Maleki ◽  
Ali Akbar Gharehaghaji ◽  
Giuseppe Criscenti ◽  
Lorenzo Moroni ◽  
Pieter J. Dijkstra
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Influence Of Process Parameters

scholarly journals Influence of Process Parameters on Rapid Prototype Part Using Response Surface Methodology

2019 ◽  
Vol 1 (1) ◽  
pp. 4-7
Author(s):  
Chockalingam Palanisamy ◽  
Natarajan Chinnasamy ◽  
Karthikeyan Muthu
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Layer Thickness ◽  
Process Parameters ◽  
Rapid Prototype ◽  
Acrylonitrile Butadiene Styrene ◽  
Fused Deposition Modelling ◽  
Influence Of Process Parameters ◽  
Fused Deposition

In this research the influencing process parameters on fused deposition modelling of Acrylonitrile Butadiene Styrene (ABS) parts were studied. The two process parameters, layer thickness and model interior fill style are studied. The specimens were built, tests carried out to find out the surface roughness quality of the specimens. The results analyzed using Response Surface Methodology (RSM). The result indicates that the specimen Type 1 with the 0.254mm layer thickness and solid model interior fill style is the best specimen among the types of specimens tested.

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