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.

Preliminary Study on the Effect of Mass-Induced Damping on Machining Delicate Workpiece Geometry

2013 ◽  
Vol 764 ◽  
pp. 83-89
Author(s):  
A. Kamaruddin ◽  
W.C. Pan ◽  
S.L. Ding ◽  
J. Mo
Keyword(s):  
Machining Process ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Stability Lobe ◽  
Mechanical Factors ◽  
Preliminary Study ◽  
Thin Walled ◽  
Enhance Efficiency ◽  
The Stability ◽  
Minor Increment

Study of predicting chatter has been around for many years. These studies are crucial for our understanding of machining processes and to enhance efficiency in manufacturing. This paper presents a new mechanism affecting the stability of machining process called mass induced damping. This effect is simulated numerically with tested values of initial parameters taken for impact tests of a thin-walled workpiece. Results from the simulation shows minor increment in allowable depth of cut by numerically calculated using stability lobe theory. This effect will open a new understanding how certain mechanical factors would affect the value of damping of a system.

scholarly journals Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5326
Author(s):  
Andrés Sio-Sever ◽  
Erardo Leal-Muñoz ◽  
Juan Manuel Lopez-Navarro ◽  
Ricardo Alzugaray-Franz ◽  
Antonio Vizan-Idoipe ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
End Milling ◽  
Low Cost ◽  
Milling Process ◽  
Machining Process ◽  
Measuring System ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Processes ◽  
Non Invasive

This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.

scholarly journals Modeling and optimization of temperature in end milling operations

2019 ◽  
Vol 23 (6 Part A) ◽  
pp. 3651-3660
Author(s):  
Jelena Baralic ◽  
Nedeljko Ducic ◽  
Andjelija Mitrovic ◽  
Pavel Kovac ◽  
Miroslav Lucic
Keyword(s):  
End Milling ◽  
Cutting Speed ◽  
Infrared Camera ◽  
Milling Process ◽  
Optimization Techniques ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Machined Surface ◽  
Milling Operations

Milling is one of the most important and most complex cutting machining processes. During the milling process, the cross-section of the chip is variable. Also, all milling operations are interrupted processes. The cutting edge of the mill tooth periodically enters and exits from the contact with the workpiece, which leads to periodic heating and cooling during the machining. This periodic change of temperature significantly affects the process of tool wear and therefore the quality of the machined surface. This paper aims at modeling and optimizing the parameters of the machining process to achieve the minimum temperature. In order to perform optimization, it was necessary to perform temperature measurements for the various parameters of the machining process. An infrared camera was used for the temperature measurement. Then, based on the measured values, the mathematical modeling of the temperature was performed depending on the cutting speed, the feed rate and the depth of cut. This model is then optimized using two different optimization techniques.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

Surface Roughness Identification in End Milling Using Vibration Signals and Fuzzy Clustering

2016 ◽  
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
A. H. M. Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Wavelet Transforms ◽  
Fourier Transforms ◽  
End Milling ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machined Surface ◽  
Vibration Signals

The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.

scholarly journals Automatic Temperature Measurement and Monitoring System for Milling Process of AA6041 Aluminum Aloy using MLX90614 Infrared Thermometer Sensor with Arduino

2021 ◽  
Vol 82 (2) ◽  
pp. 1-14
Author(s):  
Agus Sudianto ◽  
Zamberi Jamaludin ◽  
Azrul Azwan Abdul Rahman ◽  
Sentot Novianto ◽  
Fajar Muharrom
Keyword(s):  
Aluminum Alloy ◽  
Temperature Measurement ◽  
Monitoring System ◽  
Measurement System ◽  
Manufacturing Process ◽  
Milling Process ◽  
Numerical Control ◽  
Machining Process ◽  
Infrared Thermometer ◽  
Temperature Measurement System

Manufacturing process of metal part requires real-time temperature monitoring capability to ensure high surface integrity is upheld throughout the machining process. A smart temperature measurement and monitoring system for manufacturing process of metal parts is necessary to meet quality and productivity requirements. A smart temperature measurement can be applied in machining processes of conventional, non-conventional and computer numerical control (CNC) machines. Currently, an infrared fusion based thermometer Fluke Ti400 was employed for temperature measurement in a machining process. However, measured temperature in the form of data list with adjustable time range setting is not automatically linked to the computer for continuous monitoring and data analysis purposes. For this reason, a smart temperature measurement system was developed for a CNC milling operation on aluminum alloy (AA6041) using a MLX90614 infrared thermometer sensor operated by Arduino. The system enables data linkages with the computer because MLX90614 is compatible and linked to Microsoft Exel via the Arduino. This paper presents a work-study on the performance of this Arduino based temperature measurement system for dry milling process application. Here, the Arduino based temperature measurement system captured the workpiece temperature during machining of Aluminum Alloy (AA6041) and data were compared with the Fluke Ti400 infrared thermometer. Measurement results from both devices showed similar accuracy level with a deviation of ± 2 oC. Hence, a smart temperature measurement system was succeesfully developed expanding the scopes of current system setup.

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 Modified Mechanistic Model Based on Gaussian Process Adjusting Technique for Cutting Force Prediction in Micro-End Milling

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xiaoping Liao ◽  
Zhenkun Zhang ◽  
Kai Chen ◽  
Kang Li ◽  
Junyan Ma ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Mechanistic Model ◽  
Machining Process ◽  
Mechanistic Models ◽  
Machining Processes ◽  
Model Based ◽  
Micro End Milling

Micro-end milling is in common use of machining micro- and mesoscale products and is superior to other micro-machining processes in the manufacture of complex structures. Cutting force is the most direct factor reflecting the processing state, the change of which is related to the workpiece surface quality, tool wear and machine vibration, and so on, which indicates that it is important to analyze and predict cutting forces during machining process. In such problems, mechanistic models are frequently used for predicting machining forces and studying the effects of various process variables. However, these mechanistic models are derived based on various engineering assumptions and approximations (such as the slip-line field theory). As a result, the mechanistic models are generally less accurate. To accurately predict cutting forces, the paper proposes two modified mechanistic models, modified mechanistic models I and II. The modified mechanistic models are the integration of mathematical model based on Gaussian process (GP) adjustment model and mechanical model. Two different models have been validated on micro-end-milling experimental measurement. The mean absolute percentage errors of models I and II are 7.76% and 6.73%, respectively, while the original mechanistic model’s is 15.14%. It is obvious that the modified models are in better agreement with experiment. And model II performs better between the two modified mechanistic models.

Finite Element Analysis of a Frictionally Damped Slender Endmill

2012 ◽  
Author(s):  
Reza Madoliat ◽  
Sajad Hayati
Keyword(s):  
Finite Element ◽  
End Milling ◽  
Friction Stress ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Parameter Study ◽  
Bending Vibration ◽  
Element Analysis ◽  
Tool Axis

This paper primarily deals with suppression of chatter in end-milling process. Improving the damping is one way to achieve higher stability for machining process. For this purpose a damper is proposed that is composed of a core and a multi fingered hollow cylinder which are shrink fitted in each other and their combination is shrink fitted inside an axial hole along the tool axis. This structure causes a resisting friction stress during bending vibration. Using FEA-ANSYS the structure is simulated. Then a parameter study is carried out where the frequency response and the depth of cut are calculated and tabulated to obtain the most effective configuration. The optimal configuration of tool is fabricated and finite element results are validated using modal test. The results show a high improvement in performance of the tool with proposed damper. Good agreement between experiments and modeling is obtained.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

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