PDE-Constrained Gaussian Process Model on Material Removal Rate of Wire Saw Slicing Process

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Hongxu Zhao ◽  
Ran Jin ◽  
Su Wu ◽  
Jianjun Shi
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Material Removal ◽  
Process Model ◽  
Removal Rate ◽  
Kriging Model ◽  
Random Errors ◽  
Gaussian Process Model ◽  
Thickness Uniformity ◽  
Wire Saw

Thickness uniformity of wafers is a critical quality measure in a wire saw slicing process. Nonuniformity occurs when the material removal rate (MRR) changes over time during a slicing process, and it poses a significant problem for the downstream processes such as lapping and polishing. Therefore, the MRR should be modeled and controlled to maintain the thickness uniformity. In this paper, a PDE-constrained Gaussian process model is developed based on the global Galerkin discretization of the governing partial differential equations (PDEs). Three features are incorporated into the statistical model: (1) the PDEs governing the wire saw slicing process, which are obtained from engineering knowledge, (2) the systematic errors of the manufacturing process, and (3) the random errors, including both random manufacturing errors and measurement noises. Real experiments are conducted to provide data for the validation of the PDE-constrained Gaussian process model by estimating the model coefficients and further using the model to predict the overall MRR profile. The results of cross-validation indicate that the prediction performance of the PDE-constrained Gaussian process model is better than the widely used universal Kriging model with a mean of second order polynomial functions.

Study on Thin Diamond Wire Slicing with Taguchi Method

2006 ◽  
Vol 505-507 ◽  
pp. 1219-1224 ◽  
Author(s):  
Pei Lum Tso ◽  
Bo Huei Yan ◽  
Chan Hsing Lo
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Processing Parameters ◽  
Diamond Wire ◽  
Wire Saw ◽  
Diamond Wire Saw

Presently, the loose abrasive wire saw is the most commonly used technique for slicing hard and brittle materials. However its productivity is relatively low. A diamond wire saw has been developed for slicing brittle materials such as silicon wafer. The objects of this paper is to make the thin diamond wire saw apply to high cost production in semiconductor industries with the effective processing parameters such as machined surface roughness, material removal rate, the wear of the wire and the kerf width of the slicing. Effects of processing parameters on the performance of the diamond wire sawing processes are investigated by using the Taguchi method for this design of experiment (DOE). The analysis of the result shows that the optimal combinations for good surface roughness are small grain size, high wire speed, and low feed rate. Wire speed and feed rate are positively related to material removal rate.

scholarly journals Integrated optimization model in wire electric discharge machining using Gaussian process regression and wolf pack algorithm approach while machining SiCp/Al composite

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401878740 ◽  
Author(s):  
Jun Ma ◽  
Wuyi Ming ◽  
Jinguang Du ◽  
Hao Huang ◽  
Wenbin He ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Optimization Model ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Gaussian Process Regression ◽  
Wire Electrical Discharge Machining ◽  
Al Composite

To further improve prediction accuracy and optimization quality of wire electrical discharge machining of SiCp/Al composite, trim cuts were performed using Taguchi experiment method to investigate the influence of cutting parameters, such as pulse duration ( Ton), pulse interval ( Toff), water pressure ( Wp), and wire tension ( Wt)), on material removal rate and three-dimensional surface characteristics ( Sq and Sa). An optimization model to predict material removal rate and surface quality was developed using a novel hybrid Gaussian process regression and wolf pack algorithm approach based on experiment results. Compared with linear regression model and back propagation neural network, the availability of Gaussian process regression is confirmed by experimental data. Results show that the worst average predictive error of five independent tests for material removal rate, Sq, and Sa are not more than 10.66%, 19.85%, and 22.4%, respectively. The proposed method in this article is an effective method to optimize the process parameters for guiding the actual wire electrical discharge machining process.

Research on Endless Wire Saw Cutting of Al2O3/TiC Ceramics

2006 ◽  
Vol 315-316 ◽  
pp. 571-574 ◽  
Author(s):  
J.F. Meng ◽  
Jian Feng Li ◽  
Pei Qi Ge ◽  
R. Zhou
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Wire Saw ◽  
Wire Cutting ◽  
Wire Wear ◽  
Cut Surface ◽  
Fixed Abrasive

This article investigates the cutting of Al2O3/TiC ceramics using a fixed abrasive diamond endless wire. The effect of wire speed and feed rate on material removal rate, cutting force, surface roughness and wire wear is investigated. The cut surface of Al2O3 /TiC ceramics is studied. This study demonstrates the advantage of fixed abrasive diamond endless wire cutting of Al2O3/TiC ceramics.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

Effect of SiC-Cu Electrode on Material Removal Rate, Tool Wear and Surface Roughness in EDM Process

2020 ◽  
Vol 38 (9A) ◽  
pp. 1406-1413
Author(s):  
Yousif Q. Laibia ◽  
Saad K. Shather
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
304 Stainless Steel ◽  
Tool Electrode ◽  
Pulse Time ◽  
Edm Process

Electrical discharge machining (EDM) is one of the most common non-traditional processes for the manufacture of high precision parts and complex shapes. The EDM process depends on the heat energy between the work material and the tool electrode. This study focused on the material removal rate (MRR), the surface roughness, and tool wear in a 304 stainless steel EDM. The composite electrode consisted of copper (Cu) and silicon carbide (SiC). The current effects imposed on the working material, as well as the pulses that change over time during the experiment. When the current used is (8, 5, 3, 2, 1.5) A, the pulse time used is (12, 25) μs and the size of the space used is (1) mm. Optimum surface roughness under a current of 1.5 A and the pulse time of 25 μs with a maximum MRR of 8 A and the pulse duration of 25 μs.

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