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%).

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%.

scholarly journals Development of surface roughness model in turning process of 3X13 steel using TiAlN coated carbide insert

2021 ◽  
pp. 113-124
Author(s):  
Nhu-Tung Nguyen ◽  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Turning Process ◽  
Nose Radius ◽  
Machining Processes ◽  
Roughness Model ◽  
Input Parameters ◽  
Coated Carbide

Surface roughness that is one of the most important parameters is used to evaluate the quality of a machining process. Improving the accuracy of the surface roughness model will contribute to ensure an accurate assessment of the machining quality. This study aims to improve the accuracy of the surface roughness model in a machnining process. In this study, Johnson and Box-Cox transformations were successfully applied to improve the accuracy of surface roughness model when turning 3X13 steel using TiAlN insert. Four input parameters that were used in experimental process were cutting velocity, feed rate, depth of cut, and insert-nose radius. The experimental matrix was designed using Central Composite Design (CCD) with 29 experiments. By analyzing the experimental data, the influence of input parameters on surface roughness was investigated. A quadratic model was built to explain the relationship of surface roughness and the input parameters. Box-Cox and Johnson transformations were applied to develop two new models of surface roughness. The accuracy of three surface roughness models showed that the surface roughness model using Johnson transformation had the highest accuracy. The second one model of surface roughness is the model using Box-Cox transformation. And surface roughness model without transformation had the smallest accuracy. Using the Johnson transformation, the determination coefficient of surface roughness model increased from 80.43 % to 84.09 %, and mean absolute error reduced from 19.94 % to 16.64 %. Johnson and Box-Cox transformations could be applied to improve the acuaracy of the surface roughness prediction in turning process of 3X13 steel and can be extended with other materials and other machining processes

scholarly journals Machining Process for a Thin-Walled Workpiece Using On-Machine Measurement of the Workpiece Compliance

2019 ◽  
Vol 13 (5) ◽  
pp. 631-638 ◽  
Author(s):  
Takuma Umezu ◽  
◽  
Daisuke Kono
Keyword(s):  
Measurement System ◽  
End Milling ◽  
Numerical Control ◽  
Cutting Conditions ◽  
Machining Process ◽  
Experimental Result ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Workpiece Vibration ◽  
Thin Walled

Demand for highly productive machining of thin-walled workpieces has been growing in the aerospace industry. Workpiece vibration is a critical issue that could limit the productivity of such machining processes. This study proposes a machining process for thin-walled workpieces that aims to reduce the workpiece vibration during the machining process. The workpiece compliance is measured using an on-machine measurement system to obtain the cutting conditions and utilize the same for suppressing the vibration. The on-machine measurement system consists of a shaker with a force sensor attached on the machine tool spindle, and an excitation control system which is incorporated within the machine tool’s numerical control (NC). A separate sensor to obtain the workpiece displacement is not required for the estimation of the displacement. The system is also capable of automatic measurement at various measurement points because the NC controls the positioning and the preloading of the shaker. The amplitude of the workpiece vibration is simulated using the measured compliance to obtain the cutting conditions for suppressing the vibration. An end milling experiment was conducted to verify the validity of the proposed process. The simulations with the compliance measurement using the developed system were compared to the results of a conventional impact test. The comparison showed that the spindle rotation speed for suppressing the vibration could be successfully determined; but, the axial depth of cut was difficult to be determined because the simulated vibration amplitude was larger than that found in the experimental result. However, this can be achieved if the amplitude is calibrated by one machining trial.

Prediction of Surface Roughness for Periodic End Mill Tool Holder

2013 ◽  
Vol 330 ◽  
pp. 262-268
Author(s):  
Yaser Hadi
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
End Milling ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Manufacturing Cost ◽  
Milling Machine ◽  
Cnc Milling ◽  
Average Percentage ◽  
Tool Holder

This study presents a new approach of localization for an elastic periodic cutting tool holder of milling machine. The paper presents a study of the factorial design application to optimize surface quality in end milling operation. Maintaining good surface quality usually involves additional manufacturing cost or loss of productivity. Therefore, mathematical model using Matlab which is feasible and applicable in prediction of surface roughness is developed. Proper setting of cutting parameter is important to obtain better surface roughness. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 16 samples were run by using Emco CNC Milling machine tool. The predicted surface roughness has been obtained by using average percentage error method. A numerical model that describes the structure of the periodic holder is developed. The approximate values of periodic holder model versus to straight are plotted. The results from this research are useful to be implemented in industry to reduce time and cost in surface roughness (response) prediction.

scholarly journals Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e018628 ◽  
Author(s):  
Wang-Chuan Juang ◽  
Sin-Jhih Huang ◽  
Fong-Dee Huang ◽  
Pei-Wen Cheng ◽  
Shue-Ren Wann
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Statistical Tests ◽  
Demand Forecasting ◽  
Information Criterion ◽  
Emergency Department Visits ◽  
Percentage Error ◽  
Initial Development ◽  
Adequate Model ◽  
Ed Visits

ObjectiveEmergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits.MethodsWe retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses.ResultsA series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1).ConclusionThe ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.

scholarly journals On the role of complexity in machining time estimation

2021 ◽  
Author(s):  
Antonio Armillotta
Keyword(s):  
Cost Estimation ◽  
Time Estimation ◽  
Percentage Error ◽  
Machining Processes ◽  
Machining Time ◽  
Average Percentage ◽  
Wide Range ◽  
Machined Parts ◽  
Time Part ◽  
Average Percentage Error

AbstractEarly cost estimation of machined parts is difficult as it requires detailed process information that is not usually available during product design. Parametric methods address this issue by estimating machining time from predictors related to design choices. One of them is complexity, defined as a function of dimensions and tolerances from an analogy with information theory. However, complexity has only a limited correlation with machining time unless restrictive assumptions are made on part types and machining processes. The objective of the paper is to improve the estimation of machining time by combining complexity with additional parameters. For this purpose, it is first shown that three factors that influence machining time (part size, area of machined features, work material) are not fully captured by complexity alone. Then an optimal set of predictors is selected by regression analysis of time estimates made on sample parts using an existing feature-based method. The proposed parametric model is shown to predict machining time with an average percentage error of 25% compared to the baseline method, over a wide range of part geometries and machining processes. Therefore, the model is accurate enough to support comparison of design alternatives as well as bidding and make-or-buy decisions.

Pemodelan Produksi Ayam Ras di Indonesia Menggunakan Regresi dengan Sisaan Deret Waktu

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Akhbamah Primadaniyah Febrin ◽  
Itasia Dina Sulvianti ◽  
Aji Hamim Wigena
Keyword(s):  
Time Series ◽  
Structural Equation ◽  
Broiler Chicken ◽  
Time Series Data ◽  
Retail Price ◽  
Series Data ◽  
Percentage Error ◽  
Average Percentage ◽  
Independent Variables ◽  
Real Price

The production of broiler chicken has fluctuated in recent years and many factors alleged to influence the production. The purpose of this study is modeling a structural equation of forecasting the production of broiler chicken. The study use a dependent variable (Y) that is production of broiler chickens (kilo ton) and five independent variables (X) consist of broiler chicken population (million), national chicken consumption (ton/year), retail price (Rp/kg), real price of corn (Rp), and real price of Kampung chicken (Rp). The variables are time series data with errors does not spread out randomly. Modeling method used and suitable to the conditions is regression with time series errors  combined with ARIMA (Autoregressive Integrated Moving Average). The results of the regression analysis showed that only population variable and retail price variable are influencing the production of broiler chicken in Indonesia. Those two independent variables then modeled by a dependent variable using regression with time series errors. The best modeling is regression with time series errors ARIMA(1,1,0) with MAPE (Mean Average Percentage Error) value of 2.4%, RMSE (Root Mean Square Error) value of 39.800, and correlation value 0.980. The results has proved that the production of broiler chicken in Indonesia is influenced by those two variables.

Fuzzy Modeling of Surface Roughness Parameters in Machining Ti-6Al-4V Alloy

2015 ◽  
Vol 766-767 ◽  
pp. 681-686 ◽  
Author(s):  
J. Nithyanandam ◽  
K. Palanikumar ◽  
Sushil Laldas
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Chemical Reactivity ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Fuzzy Modeling ◽  
Depth Of Cut ◽  
Work Piece ◽  
Nose Radius

Titanium alloys are attractive materials used in different engineering applications, due to its excellent combination of properties such as high strength to weight ratio, good corrosion resistance and high temperature applicability. They are also being used increasingly in chemical process, automotive, biomedical and nuclear plant. When machining of Titanium alloys with traditional tools the tool wear rate high. Because of high chemical reactivity and low modulus of elasticity resulting high cutting temperature and strong adhesion between the tool and work piece materials. The poly crystalline diamond (PCD) cutting tool is used for the turning experiment. The turning parameters for the experimental work are cutting speed, feed, nose radius, and depth of cut. From the results, analysis of the influences of the individual parameters on the surface roughness have been carried out. Fuzzy modeling technique is effectively used to predict the surface roughness in the machining of titanium alloy.

scholarly journals Prediksi Besaran Curah Hujan Menggunakan Metode Fuzzy Time Series

2018 ◽  
Vol 6 (4) ◽  
pp. 141 ◽  
Author(s):  
Diera Desmonda ◽  
Tursina Tursina ◽  
Muhammad Azhar Irwansyah
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Average Percentage ◽  
Average Percentage Error

Iklim tropis yang memiliki dua musim, yakni musim penghujan dan musim kemarau yang seharusnya berputar setiap enam bulan sekali. Namun beberapa tahun terakhir ini, perubahan iklim global terasa ditandai dengan tidak menentunya perputaran musim kemarau maupun musim penghujan. Untuk mengetahui perubahan pola curah hujan tersebut, maka dirancanglah prediksi besaran curah hujan untuk melihat dan menganalisa pola hujan yang akan terbentuk ke depannya. Aplikasi prediksi besaran curah hujan yang akan dibangun menggunakan forecasting atau peramalan dengan metode Fuzzy Time Series. Logika fuzzy digunakan karena dapat memetakan suatu input ke dalam suatu output dan memiliki toleransi terhadap data-data yang tersedia. Adapun hasil dari penelitian yang dilakukan adalah mengimplementasikan metode Fuzzy Time Series untuk membangun aplikasi yang dapat mengolah dan menghitung pola data curah hujan serta memprediksi besaran curah hujan. Hasil dari pengujian diperoleh nilai MAPE (Mean Average Percentage Error) bervariasi tergantung jumlah data dan jumlah interval yang digunakan. Nilai MAPE terbaik yang diperoleh adalah 0,151% pada penggunaan data curah hujan periode 2015 – 2017 dengan jumlah interval 401. Perhitungan menggunakan metode Fuzzy Time Series sangat dipengaruhi oleh jumlah data yang digunakan dan jumlah interval dalam membagi data tersebut.

Sign in / Sign up

Export Citation Format

Share Document