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.

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%.

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%).

Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions

2020 ◽  
Vol 10 (2) ◽  
pp. 154-162
Author(s):  
Engin Özdemir ◽  
Didem Eren Sarici
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Calorific Value ◽  
Volatile Matter ◽  
Ash Content ◽  
Percentage Error ◽  
Multiple Regression Models ◽  
Artificial Neural

Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values. Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor. Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined. Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90). Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.

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.

Experimental and Numerical Investigation into Residual Stress During Turning Operation for Stainless Steel AISI 316

2020 ◽  
Vol 38 (12A) ◽  
pp. 1862-1870
Author(s):  
Safa M. Lafta ◽  
Maan A. Tawfiq
Keyword(s):  
Stainless Steel ◽  
Residual Stresses ◽  
Orthogonal Cutting ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Main Role ◽  
Turning Operation ◽  
Aisi 316

RS (residual stresses) represent the main role in the performance of structures and machined parts. The main objective of this paper is to investigate the effect of feed rate with constant cutting speed and depth of cut on residual stresses in orthogonal cutting, using Tungsten carbide cutting tools when machining AISI 316 in turning operation. AISI 316 stainless steel was selected in experiments since it is used in many important industries such as chemical, petrochemical industries, power generation, electrical engineering, food and beverage industry. Four feed rates were selected (0.228, 0.16, 0.08 and 0.065) mm/rev when cutting speed is constant 71 mm/min and depth of cutting 2 mm. The experimental results of residual stresses were (-15.75, 12.84, 64.9, 37.74) MPa and the numerical results of residual stresses were (-15, 12, 59, and 37) MPa. The best value of residual stresses is (-15.75 and -15) MPa when it is in a compressive way. The results showed that the percentage error between numerical by using (ABAQUS/ CAE ver. 2017) and experimental work measured by X-ray diffraction is range (2-15) %.

scholarly journals 242 Effect of trucking distance on sale price of beef calf and feeder cattle lots sold through video auctions from 2010 through 2018

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 10-11
Author(s):  
Esther D McCabe ◽  
Mike E King ◽  
Karol E Fike ◽  
Maggie J Smith ◽  
Glenn M Rogers ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Average Weight ◽  
Sale Price ◽  
Feeder Cattle ◽  
A Value ◽  
Multiple Regression Models ◽  
Determine Effect

Abstract The objective was to determine effect of trucking distance on sale price of beef calf and feeder cattle lots sold through Superior Livestock Video Auctions from 2010 through 2018. Data analyzed were collected from 211 livestock video auctions. There were 42,043 beef calf lots and 19,680 feeder cattle lots used in these analyses. Six states (Colorado, Iowa, Kansas, Nebraska, Oklahoma, and Texas) of delivery comprised 70% of calf lots and 83% of feeder cattle lots and were used in these analyses. All lot characteristics that could be accurately quantified or categorized were used to develop multiple regression models that evaluated effects of independent factors using backwards selection. A value of P &lt; 0.05 was used to maintain a factor in the final models. Based upon reported state of origin and state of delivery, lots were categorized into one of the following trucking distance categories: 1) Within-State, 2) Short-Haul, 3) Medium-Haul, and 4) Long-Haul. Average weight and number of calves in lots analyzed was 259.2 ± 38.4 kg BW and 100.6 ± 74.3 head, respectively. Average weight and number of feeder cattle in lots analyzed was 358.4 ± 34.3 kg BW and 110.6 ± 104.1 head, respectively. Beef calf lots hauled Within-State sold for more ($169.24/45.36 kg; P &lt; 0.0001) than other trucking distance categories (Table 1). Long-Haul calf lots sold for the lowest (P &lt; 0.0001) price ($166.70/45.36 kg). Within-State and Short-Haul feeder cattle lots sold for the greatest (P &lt; 0.0001) price ($149.96 and $149.81/45.36 kg, respectively; Table 2). Long-Haul feeder cattle lots sold for the lowest (P &lt; 0.0001) price, $148.43/45.36 kg. These results indicate there is a price advantage for lots expected to be hauled shorter distances, likely because of cost and risk associated with transportation.

Cutting Temperature and Cutting Forces Investigation Based on the Taguchi Design Method when High-Speed Milling of Titanium Matrix Composites with PCD Tool

2016 ◽  
Vol 836-837 ◽  
pp. 168-174 ◽  
Author(s):  
Ying Fei Ge ◽  
Hai Xiang Huan ◽  
Jiu Hua Xu
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
High Speed ◽  
Volume Fraction ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
High Speed Milling ◽  
Radial Depth

High-speed milling tests were performed on vol. (5%-8%) TiCp/TC4 composite in the speed range of 50-250 m/min using PCD tools to nvestigate the cutting temperature and the cutting forces. The results showed that radial depth of cut and cutting speed were the two significant influences that affected the cutting forces based on the Taguchi prediction. Increasing radial depth of cut and feed rate will increase the cutting force while increasing cutting speed will decrease the cutting force. Cutting force increased less than 5% when the reinforcement volume fraction in the composites increased from 0% to 8%. Radial depth of cut was the only significant influence factor on the cutting temperature. Cutting temperature increased with the increasing radial depth of cut, feed rate or cutting speed. The cutting temperature for the titanium composites was 40-90 °C higher than that for the TC4 matrix. However, the cutting temperature decreased by 4% when the reinforcement's volume fraction increased from 5% to 8%.

Multiple regression models for predicting total daily pollen concentration in Cartagena

Grana ◽  
2005 ◽  
Vol 44 (2) ◽  
pp. 108-114 ◽  
Author(s):  
José Manuel Angosto ◽  
Stella Moreno‐Grau ◽  
Javier Bayo ◽  
Belén Elvira‐Rendueles
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Pollen Concentration ◽  
Multiple Regression Models ◽  
Daily Pollen Concentration

Micro Textured Cutting Inserts with Solid Lubrication as Alternative Coolant to Mineral Oil-Based Cutting Fluid in Turning Operation

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
C. Divya ◽  
L. Suvarna Raju ◽  
B. Singaravel
Keyword(s):  
Process Parameters ◽  
Orthogonal Array ◽  
Inconel 718 ◽  
Cutting Speed ◽  
Solid Lubricants ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process ◽  
Optimization Of Process Parameters ◽  
Cutting Inserts

Turning process is a primary process in engineering industries and optimization of process parameters enhance the machining performance. Inconel 718 is a nickel-based superalloy, widely found applications in the manufacturing of blades, sheets and discs in aircraft engines and rocket engines. It provides toughness at low temperature, with stand high mechanical stresses at elevated temperature and creep resistance. In this work, turning process is carried out on Inconel 718 with micro whole textured cutting inserts filled with solid lubricants. Three different solid lubricants are used namely molybdenum-di-sulfide (MoS2), tungsten-di-sulfide (WS2) and calcium-di-fluoride (CaF2). Experiments are performed as per L9 orthogonal array. Statistical approaches such as orthogonal array, Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) are used to find the importance and effects of machining parameters. In this study, input parameters included are feed, cutting speed and depth of cut and output parameter includes surface roughness. Optimization of process parameters is carried out and the significance is estimated. The result suggested that WS2 followed by MoS2 and CaF2 given good surface finish value. Also, solid lubricant in machining enhances the sustainability in manufacturing.

Sign in / Sign up

Export Citation Format

Share Document