scholarly journals Modeling of cutting performances in turning process using artificial neural networks

2017 ◽  
Vol 9 ◽  
pp. 184797901771898 ◽  
Author(s):  
Samya Dahbi ◽  
Latifa Ezzine ◽  
Haj EL Moussami
Keyword(s):  
Network Architecture ◽  
Removal Rate ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Performance Criteria ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Turning Process ◽  
Average Percentage ◽  
Artificial Neural

In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled performances include surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a computer numerically controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient ( R2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R2 > 99%, MSE < 0.3%, and APE < 6%.

Modeling of Cutting Performances in Turning Process Using Multiple Regression Method

2017 ◽  
Vol 29 ◽  
pp. 54-69 ◽  
Author(s):  
Samya Dahbi ◽  
Latifa Ezzine ◽  
Haj El Moussami
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Performance Criteria ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Turning Process ◽  
Average Percentage ◽  
Multiple Regression Models

This paper presents the modeling of cutting performances in turning of 2017A aluminium alloy at four turning parameters: cutting speed, feed rate, depth of cut, and tool nose radius. These performances include: surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a Computer Numerically Controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop multiple regression models for the pre-cited cutting performances and investigate the effects of turning parameters and their interactions on responses. To evaluate the accuracy of the developed models, two performance criteria were used: Correlation Coefficient (R²) and Average Percentage Error (APE). It was clearly seen that the multiple regression models estimate the cutting performances with high accuracy: R²>94% and APE<7%. Therefore, this method is an effective tool for modeling the cutting performances in turning process.

ARIMA-Based Time Series Model of Cutting Temperature in Facing Process

2019 ◽  
Vol 18 (03) ◽  
pp. 395-411
Author(s):  
Samya Dahbi ◽  
Latifa Ezzine ◽  
Haj El Moussami
Keyword(s):  
Time Series ◽  
Cutting Temperature ◽  
Numerical Control ◽  
Performance Criteria ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Machining Processes ◽  
Adequate Model ◽  
Average Percentage

During machining processes, cutting temperature directly affects cutting performances, such as surface quality, dimensional precision, tool life, etc. Thus, evaluation of cutting temperature rise in the tool–chip interface by reliable techniques can lead to improved cutting performances. In this paper, we present the modeling of cutting temperature during facing process by using time series approach. The experimental data were collected by conducting facing experiments on a Computer Numerical Control lathe and by measuring the cutting temperature by an infrared camera. The collected data were used to test several Autoregressive Integrated Moving Average (ARIMA) models by using Box–Jenkins time series procedure. Then, the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 1, 1) and it was tested by conducting a new facing operation under the same cutting conditions (spindle speed, feed rate, depth of cut, and nose radius). It was clearly seen that there is a good agreement between experimental and simulated temperatures, which reveals that this approach simulates the evolution of cutting temperature in facing process with high accuracy (average percentage error [Formula: see text] 0.57%).

scholarly journals Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2998 ◽  
Author(s):  
Kubilay Aslantas ◽  
Mohd Danish ◽  
Ahmet Hasçelik ◽  
Mozammel Mia ◽  
Munish Gupta ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Small Diameter ◽  
Cutting Parameters ◽  
Ti6al4v Alloy ◽  
Depth Of Cut ◽  
Turning Process ◽  
Micro Turning

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

scholarly journals Machining Performance of Aluminium 7075 Composite: A Grey Based Taguchi Concept

2019 ◽  
Vol 9 (2) ◽  
pp. 81-92
Keyword(s):  
Removal Rate ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Stir Casting ◽  
Performance Criteria ◽  
Machining Performance ◽  
Relational Analysis ◽  
Surface Method ◽  
The Individual ◽  
Grey Relational

Irregular particulates of Silicon carbide (SiCp) augmented aluminium 7075 metal matrix composite (MMC) was processed by vortex route of stir casting. Homogeneous dispersion of the reinforcement was verified by metallurgical microscope. Machining performance of the synthesized MMC was investigated during turning, considering surface roughness, cutting temperature, tool flank wear and material removal rate as performance criteria. The chips formed during the turning trials were also analyzed. The machining quality targets were optimized simultaneously through grey relational analysis (GRA) based Taguchi method, which resulted that the optimal combination of parametric levels was 154 m.min-1 , 0.04 mm.rev-1 and 0.1 mm of cutting speed (V), feed (f) and cutting depth (d), respectively. Mathematical models were then generated for the individual responses using response surface method, followed by verification of their adequacy.

The Influence of Cutting Parameters of Turning Process on the Oil Palm Wood Using Carbide Tool

2013 ◽  
Vol 415 ◽  
pp. 666-671
Author(s):  
Surasit Rawangwong ◽  
Jaknarin Chatthong ◽  
Worapong Boonchouytan
Keyword(s):  
Surface Roughness ◽  
Oil Palm ◽  
Feed Rate ◽  
Cutting Speed ◽  
Experimental Result ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Turning Process ◽  
Main Factors

This research study aimed to investigate the effect of main factors on the surface roughness in oil palm wood turning process for manufacturing furniture parts using carbide tools. The main factors, namely, cutting speed, feed rate and depth of cut were investigated for the optimum surface roughness in furniture manufacturing process. The result of preliminary trial shown that the depth of cut had no effect on surface roughness. Moreover, the experiment was found that the factors affecting a surface roughness were cutting speed and feed rate, with having tendency for reduction of roughness value at lower feed rate and greater cutting speed, Therefore in the turning process of oil palm wood, it was possible to determine a cutting condition by means of the equation Ra = 19.8-0.00742 Cutting Speed+3.98 Feed rate, This equation can be best used with limitation of cutting speed at 122-450 m/min, feed rate at 0.1-0.5 mm/rev and depth of cut does not over 1 mm,. To confirm the experiment result, a comparison between the equation value and an actual value by estimating a prediction error value was calculate with the surface roughness and margin of error does not over 10%. The experimental result reveals the mean absolute percentage error (MAPE) of the equation of surface roughness is 3.24%, which is less than the predicted error value and it is acceptable.

scholarly journals A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study

2021 ◽  
Vol 16 (4) ◽  
pp. 443-456
Author(s):  
D.D. Trung ◽  
H.X. Thinh
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Multi Criteria Decision Making ◽  
Turning Process ◽  
Decision Methods ◽  
Determination Methods

Multi-criteria decision-making is important and it affects the efficiency of a mechanical processing process as well as an operation in general. It is understood as determining the best alternative among many alternatives. In this study, the results of a multi-criteria decision-making study are presented. In which, sixteen experiments on turning process were carried out. The input parameters of the experiments are the cutting speed, the feed speed, and the depth of cut. After conducting the experiments, the surface roughness and the material removal rate (MRR) were determined. To determine which experiment guarantees the minimum surface roughness and maximum MRR simultaneously, four multi-criteria decision-making methods including the MAIRCA, the EAMR, the MARCOS, and the TOPSIS were used. Two methods the Entropy and the MEREC were used to determine the weights for the criteria. The combination of four multi-criteria making decision methods with two determination methods of the weights has created eight ranking solutions for the experiments, which is the novelty of this study. An amazing result was obtained that all eight solutions all determined the same best experiment. From the obtained results, a recommendation was proposed that the multi-criteria making decision methods and the weighting methods using in this study can also be used for multi-criteria making decision in other cases, other processes.

scholarly journals Machinability Studies and Optimization of AA 6082/Fly Ash/Carbonized Eggshell Matrix Composite

2021 ◽  
Vol 31 (4) ◽  
pp. 207-216
Author(s):  
Ndudim H. Ononiwu ◽  
Chigbogu G. Ozoegwu ◽  
Nkosinathi Madushele ◽  
Esther T. Akinlabi
Keyword(s):  
Fly Ash ◽  
Function Analysis ◽  
Removal Rate ◽  
Desirability Function ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Chip Morphology ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Desirability Function Analysis

Machinability studies of aluminium matrix composites (AMCs) is a necessary investigation required to understand their behaviour during machining to produce components effectively and efficiently. This established need has led to the investigation into the machinability of AA 6082 reinforced with 2.5 wt.% fly ash and 2.5 wt.% carbonized eggshell fabricated via stir casting. The studied machinability indices were material removal rate (MRR), cutting temperature, built-up edges (BUE) formation and chip morphology while the selected inputs were cutting speed (100 mm/min, 200 mm/min, 300 mm/min), feed (0.1 mm/rev, 0.2 mm/rev, 0.3 mm/rev) and depth of cut (0.5 mm, 1 mm, 1.5 mm). For the experimental design, the L9 orthogonal array was preferred to create 9 experimental runs. The analysis of the built-up edges showed that it increased at lower cutting speeds and increased feed and depth of cut. The examination of the produced chips after each experimental run showed the presence of c-shaped, helically shaped and ribbon-shaped chips. The analysis of variance (ANOVA) for both MRR and cutting temperature indicated that the depth of cut was the most influential factor on both responses. Multi-objective optimization using desirability function analysis showed that the optimum combination of parameters was 300 mm/min, 0.2 mm/rev and 1.0 mm for the cutting speed, feed and depth of cut respectively. The ANOVA of the composite desirability indicated that the cutting speed was the most contributing factor.

scholarly journals Optimization of Multiple Response Using Taguchi-WPCA In ST 60 Tool Steel Turning Process With Minimum Quantity Cooling Lubrication (MQCL) Method

2018 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Dian Ridlo Pamuji ◽  
Muhammad Abdul Wahid ◽  
Abdul Rohman ◽  
Achmad As’ad Sonief ◽  
Moch Agus Choiron
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Orthogonal Array ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Turning Process ◽  
Feeding Speed

A research was conducted for the optimization of the turning process st 60 tool steel with multiple performance characteristics based on the orthogonal array with Taguchi-WPCA method. Minimum Quantity Cooling Lubrication (MQCL) metode was applied as a coolant. The experimental studies were conducted under varying the cutting speed, feeding, depth of cut and type of coolant. The optimized multiple performance characteristics were surface roughness, and material removal rate. An orthogonal array, signal-to-noise ratio, grey relational analysis, weighted pricipal component analysis and analysis of variance were employed to study the multiple performance characteristics. Experimental results show that cutting speed gives the highest contribution for minimize of surface roughness and maximize of material removal rate, followed by feeding speed, type of coolant and depth of cut. The minimum of surface roughness and maximize of material removal rat could be obtained by using the values of cutting speed, feeding speed,  depth of cut and type of coolant of 172.95 m/minute, 0.053 mm/rev, 0.25 mm, and vegetable oil as a coolant respectively.

scholarly journals Multi-Response Optimisation of Turning Process Parameters with GRA and TOPSIS Methods

2020 ◽  
Vol 19 (4) ◽  
pp. 547-558
Author(s):  
M. Ficko ◽  
D. Begic-Hajdarevic ◽  
V. Hadziabdic ◽  
S. Klancnik
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Turning Process ◽  
Cnc Turning ◽  
Taguchi Design Of Experiments ◽  
Optimisation Methods ◽  
Cnc Turning Process

The research deals with the optimisation of CNC turning process parameters to determine the optimal parametric combination that provides the minimal surface roughness (Ra) and maximal material removal rate. The experiment was conducted by the CNC turning process of S355J2 carbon steel. Data from the Taguchi design of experiments were the subject of analysis with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the present study, three process parameters, such as cutting speed, feed rate and depth of cut, were chosen for the experimentation. It was found that 250 m/min cutting speed, 0.10 mm/rev feed rate and 1.8 mm depth of cut presented the optimal parametric combination by both used multi-objective optimisation methods. Analysis of variance (ANOVA) at a 95 % confidence level was used to determine the most significant parameters. Finally, the accuracy of GRA and TOPSIS results were validated by confirmation experiments.

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.

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