Multi Objective Optimization of Surface Roughness and Workpiece Surface Temperature in Turning Operation

2014 ◽  
Vol 554 ◽  
pp. 376-380
Author(s):  
Ahmad Nooraziah ◽  
V. Janahiraman Tiagrajah
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Surface Temperature ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Workpiece Surface ◽  
Multi Objective ◽  
Controllable Factors ◽  
Single Objective

This paper presents the optimization of multiple performance characteristics (surface roughness and workpiece surface temperature) based on the Taguchi method. Three controllable factors of the turning process were studied at three levels. The single objective optimization was conducted using Taguchi method. The multiple Signal-to-Noise (MSNR) value was used to correspond to multi objective cases. The optimum combination of cutting parameters was obtained based on the highest value of MSNR.

scholarly journals Multi-objective Optimization in CNC Turning Process using Taguchi Method

IARJSET ◽  
2017 ◽  
Vol 4 (6) ◽  
pp. 131-139
Author(s):  
K. Kushal Kumar ◽  
Asst. Prof. Gangadhar Biradar ◽  
Asst. Prof. MD. Ashfaq Hussain
Keyword(s):  
Taguchi Method ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Cnc Turning ◽  
Multi Objective ◽  
Cnc Turning Process

scholarly journals Pengaruh Kecepatan dan Kedalaman Potong pada Proses Pembubutan Konvensional Terhadap Kekasaran Permukaan Lubang

2018 ◽  
Vol 3 (2) ◽  
pp. 82-86
Author(s):  
Bambang Siswanto ◽  
Sunyoto Sunyoto
Keyword(s):  
Surface Roughness ◽  
Descriptive Analysis ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cylinder Block ◽  
Depth Of Cut ◽  
Cast Aluminum ◽  
Turning Process ◽  
Workpiece Surface ◽  
Independent Variables

There are several factors that affect the roughness of the workpiece surface when doing turning process on the lathe, especially the use of cutting parameters. This study aims to determine the effect of cutting speed and depth of cut parameters on the roughness of cylinder block hole surface. This research is an experimental research using two independent variables, which are cutting speed and depth of cut. The dependent variable is surface roughness of cylinder block’s hole. The research was done by making cast aluminum specimens and then turning a hole in the specimen with varied cutting speed and depth of cut. The surface roughness was then tested using Surfcorder SE 300. The obtained data were analyzed using descriptive analysis. Results show that there is an effect of cutting speed on the surface roughness of cylinder block, with the best result (smallest roughness value) was obtained from the use of cutting speed of 125 m / min. There is also an effect of depth of cut on the surface roughness of the cylinder block, with the best result given from the use of a 0.2 mm depth of cut.Ada beberapa faktor yang mempengaruhi tingkat kekasaran permukaan benda kerja pada proses pembubutan. Penelitian ini bertujuan untuk mengetahui pengaruh parameter kecepatan potong dan kedalaman potong terhadap kekasaran permukaan pada pembubutan lubang blok silinder mesin pemotong rumput. Penelitian ini merupakan penelitian eksperimen dengan variabel bebas kecepatan potong dan kedalaman potong, dan variabel terikat kekasaran permukaan lubang. Penelitian dilakukan dengan pembuatan spesimen dengan proses pengecoran aluminium kemudian spesimen dibubut lubang dengan diberi variasi kecepatan potong dan variasi kedalaman potong. Hasil pembubutan dilakukan uji kekasaran menggunakan Surfcorder SE 300. Data yang diperoleh kemudian dianalisis dengan analisis deskriptif. Hasil penelitian menunjukkan bahwa ada pengaruh kecepatan potong terhadap hasil kekasaran permukaan blok silinder mesin pemotong rumput, hasil  paling baik dengan nilai kekasaran paling kecil diperoleh dari kecepatan potong 125 m/menit. Ada pengaruh kedalaman potong terhadap hasil kekasaran permukaan blok silinder mesin pemotong rumput, hasil paling baik dengan nilai kekasaran paling kecil diperoleh dari kedalaman potong 0,2 mm.

scholarly journals Optimization Study on Turning Process by Using Taguchi-copras Method

2021 ◽  
Vol 309 ◽  
pp. 01010
Author(s):  
Do Duc Trung ◽  
Nguyen Huu Quang ◽  
Tran Quoc Hoang ◽  
Cao The Anh ◽  
Nguyen Hong Linh ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Nose Radius ◽  
Multi Objective ◽  
Optimization Study ◽  
Input Parameters ◽  
Cutting Velocity ◽  
Copras Method

In this article, a multi-objective optimization of turning process study is presented. Two output parameters of the turning process taken into consideration are surface roughness and Material Removal Rate (MRR). Taguchi method has been applied to design the experimental matrix with four input parameters including nose radius, cutting velocity, feed rate and cutting depth. Copras method has been employed to solve the multi-objective optimization problem. Finally, the optimal values of the input parameters have been determined to simultaneously ensure the two criteria of the minimum surface roughness and the maximum MRR.

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

2020 ◽  
Vol 4 (3) ◽  
pp. 64 ◽  
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.

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

2019 ◽  
Vol 9 (18) ◽  
pp. 3684 ◽  
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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