Microstructure Analysis and Multi-objective Optimization of Pulsed TIG Welding of 316/316L Austenite Stainless Steel

In this experimental work, Magneto rheological abrasive flow nano finishing processes were conducted on AISI Stainless steel 316L work pieces that are widely used in medical implants. The focus of the present study is to assess the effect of input variables namely the volume percentage of iron (Fe) particles, silicon carbide (SiC) abrasive particles in the Magneto rheological abrasive fluid and number of cycles on the final surface roughness at nano level as well as the material removal rate. The volume % of Fe particles were taken as 20, 25 and 30 and the volume % of SiC particles were taken as 10, 15 and 20. The different number of cycles considered for the study is 100,200 and 300. There are 20 different set of experiments with different combinations of input variables mentioned have been carried out based on the experimental design derived through central composite design technique. The minimum surface roughness observed is 23.34 nanometer (nm) from the initial surface roughness of 1.92 micro meter (µm). Towards optimizing the input process variables, a multi objective optimization was carried out by using response surface methodology.

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Pulsed TIG Welding–Brazing of Aluminum–Stainless Steel with an Al-Cu Twin Hot Wire

Journal of Materials Engineering and Performance ◽

10.1007/s11665-018-3848-y ◽

2019 ◽

Vol 28 (2) ◽

pp. 1180-1189 ◽

Cited By ~ 3

Author(s):

Huan He ◽

Chuansong Wu ◽

Sanbao Lin ◽

Chunli Yang

Keyword(s):

Stainless Steel ◽

Tig Welding ◽

Hot Wire ◽

Twin Hot Wire ◽

Pulsed Tig Welding

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Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

Journal of Manufacturing and Materials Processing ◽

10.3390/jmmp4030064 ◽

2020 ◽

Vol 4 (3) ◽

pp. 64 ◽

Cited By ~ 2

Author(s):

Mahamudul Hasan Tanvir ◽

Afzal Hussain ◽

M. M. Towfiqur Rahman ◽

Sakib Ishraq ◽

Khandoker Zishan ◽

...

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Optimization Algorithm ◽

Cutting Parameters ◽

Whale Optimization Algorithm ◽

Multi Objective Optimization ◽

Turning Operation ◽

Multi Objective ◽

Whale Optimization ◽

Optimum Cutting

In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.

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Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

Applied Sciences ◽

10.3390/app9183684 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3684 ◽

Cited By ~ 3

Author(s):

Tao Zhou ◽

Lin He ◽

Jinxing Wu ◽

Feilong Du ◽

Zhongfei Zou

Keyword(s):

Genetic Algorithm ◽

Surface Roughness ◽

Stainless Steel ◽

Surface Quality ◽

Regression Tree ◽

Cutting Parameters ◽

304 Stainless Steel ◽

Gradient Boosting ◽

Multi Objective Optimization ◽

Multi Objective

Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

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