Condition Monitoring With Round Ceramic Inserts While Face Milling Using Acceleration Data

2010 ◽  
Author(s):  
Justin L. Milner ◽  
John T. Roth
Keyword(s):  
Condition Monitoring ◽  
End Milling ◽  
Low Cost ◽  
Milling Operations ◽  
Tool Monitoring ◽  
Wear Life ◽  
Acceleration Data ◽  
Life Tests ◽  
The Fourier Transform ◽  
Machining Operations

In order to automate machining operations, it is necessary to develop robust tool condition monitoring techniques. In this paper, a tool monitoring strategy for round whisker-reinforced ceramic end milling tools is presented based on the Fourier transform and statistical analysis of the vibrations of the tool during the machining operations. Using a low-cost tri-axial piezoelectric accelerometer, the presented algorithm demonstrates the ability to accurately monitor the condition of the tools as the wear increases during linear milling operations. One benefit of using accelerometer signals to monitor the cutting process is that the sensor does not limit the machines capabilities, as a workpiece mounted dynamometer does. To demonstrate capabilities of the technique for round coated and uncoated ceramic tooling, six tool wear life tests were conducted under various conditions. The indirect method discussed herein successfully tracks the tool’s wear, even with the occurrence of minor chipping, and is shown to be sensitive enough to provide sufficient time to replace the insert prior to damage of the machine tool, cutter, and/or workpiece.

Development of a Cutting Direction and Sensor Orientation Independent Monitoring Technique for End-Milling

1999 ◽  
Author(s):  
John T. Roth ◽  
Sudhakar M. Pandit
Keyword(s):  
End Milling ◽  
Multivariate Models ◽  
Actual Event ◽  
Monitoring Technique ◽  
Sensor Orientation ◽  
Acceleration Data ◽  
Life Tests ◽  
Multivariate Autoregressive Models ◽  
Cutting Direction ◽  
Warning Time

Abstract In the authors’ previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to tri-axial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation.

Monitoring End-Mill Wear and Predicting Tool Failure Using Accelerometers

1999 ◽  
Vol 121 (4) ◽  
pp. 559-567 ◽  
Author(s):  
J. T. Roth ◽  
S. M. Pandit
Keyword(s):  
Early Warning ◽  
End Milling ◽  
Cutting Conditions ◽  
Catastrophic Failure ◽  
End Mill ◽  
Systems Methodology ◽  
Tool Failure ◽  
Detection Scheme ◽  
Acceleration Data ◽  
Life Tests

Autoregressive models are fit to end-milling acceleration data and the Data Dependent Systems methodology is utilized to isolate the modal energies of the first and second multiples of the tooth pass frequency. The modal energies are shown to be closely linked to the wear curve and a detection scheme is developed that is capable of tracking the end-mill’s wear and providing an early warning of impending failure. Six life tests are conducted under varying conditions to demonstrate the capabilities of the detection scheme: standard cutting conditions, extreme cutting conditions, premature catastrophic failure and accelerometer placement. In all six cases, the detection scheme was able to provide a warning of impending failure several centimeters before the failure occurred.

Machinability study of aircraft series aluminium alloys 7075-T6 and 7050-T7451

2020 ◽  
Vol 44 (3) ◽  
pp. 427-439
Author(s):  
Ali Yeganefar ◽  
Seyed Ali Niknam ◽  
Victor Songmene
Keyword(s):  
Aluminium Alloy ◽  
Surface Quality ◽  
Aluminium Alloys ◽  
End Milling ◽  
Experimental Studies ◽  
Burr Formation ◽  
Limited Information ◽  
Milling Operations ◽  
Aluminium Alloy 7050 ◽  
Machining Operations

The aluminium alloy 7050-T7451 is generally considered as the principal choice in aeronautical applications demanding adequate strength, stress corrosion cracking resistance, and toughness. Surprisingly, despite extensive research works on machining and machinability of aluminium alloys, including aluminium alloy 7075-T6, limited information was found on machining and machinability evaluation of 7050-T7451, which belongs to a similar family as 7075-T6. To remedy the lack of knowledge determined, dry ball-end milling operations were performed with coated end milling tools on both materials. Experimental characterization and cutting force measurements were performed to measure/evaluate the cutting forces, burr formation morphology, insert performance (wear/breakage), and surface quality attributes. According to experimental studies, 7050-T7451 was found more machinable than 7075-T6. Less burr formation and better surface quality were observed on 7075-T6. Machining attributes are influenced by different experimental factors. However, other machinability attributes, including residual stress, vibration modes, as well as particle emission, must be studied under various lubrication modes and machining operations in subsequent studies. This also recalls further studies on simultaneous multiple response optimization.

Development of a Cutting Direction and Sensor Orientation Independent Monitoring Technique for End-Milling

1999 ◽  
Vol 122 (4) ◽  
pp. 671-677 ◽  
Author(s):  
John T. Roth ◽  
Sudhakar M. Pandit
Keyword(s):  
End Milling ◽  
Multivariate Models ◽  
Actual Event ◽  
Monitoring Technique ◽  
Sensor Orientation ◽  
Acceleration Data ◽  
Life Tests ◽  
Multivariate Autoregressive Models ◽  
Cutting Direction ◽  
Warning Time

In the authors’ previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to tri-axial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation. [S1087-1357(00)01003-0]

scholarly journals Investigating the Design of Condition Monitoring Systems to Evaluate Surface Roughness under the Variability in Tool Wear and Fixturing Conditions

2013 ◽  
Vol 572 ◽  
pp. 467-470 ◽  
Author(s):  
Jabbar Abbas ◽  
Amin Al-Habaibeh ◽  
Dai Zhong Su
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Condition Monitoring ◽  
End Milling ◽  
Milling Operations ◽  
Different Types ◽  
Machined Parts ◽  
Condition Monitoring Systems ◽  
Condition Monitoring System

Surface finish of machined parts in end milling operations is significantly influenced by process faults such as tool wear and tool holding (fixturing system). Therefore, monitoring these faults is considerably important to improve the quality of the product. In this paper, an investigation is presented to design the condition monitoring system to evaluate the surface roughness of the workpiece under effects of gradual tool wear and different types of the fixturing system. Automated Sensor and Signal Processing Selection (ASPS) approach is implemented and tested to determine the sensitivity of the sensory signals to estimate surface roughness under the variable conditions in comparison to surface roughness measurement device. The results indicate that the system is capable of detection the change and the trend in surface roughness. However, the sensitive features are found to be different based on the change in the fixturing system.

Endmill Condition Monitoring and Failure Forecasting Method for Curvilinear Cuts of Nonconstant Radii

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Christopher A. Suprock ◽  
John T. Roth ◽  
Larry M. Downey
Keyword(s):  
Condition Monitoring ◽  
Prior Information ◽  
Low Cost ◽  
Indirect Measurement ◽  
Rate Of Change ◽  
Cutting Conditions ◽  
Forecasting Method ◽  
Life Tests ◽  
Impending Fracture ◽  
Monitoring Algorithm

In this paper, an endmill condition monitoring technique is presented for curvilinear cutting. This algorithm operates without the need for prior knowledge of cutting conditions, tool type, cut curvature, cut direction, or directional rate of change. The goal of this method is an indirect measurement of the tool wear able to indicate when wear is accelerating without direct measurement of the tool. This technique is based on an autoregressive-type monitoring algorithm, which is used to track the tool’s condition using a tri-axial accelerometer. Accelerometer signals are monitored due to the sensor’s relatively low cost and since use of the sensor does not limit the machining envelope. To demonstrate repeatability, eight life tests were conducted. The technique discussed herein successfully delivers prognosis of impending fracture or meltdown due to wear in all cases, providing sufficient time to remove the tools before failure is realized. Furthermore, the algorithm produces similar trends capable of forecasting failure, regardless of tool type and cut geometry. Success is seen in all cases without requiring algorithm modifications or any prior information regarding the tool or cutting conditions.

Endmill Condition Monitoring and Failure Forecasting Method for Curvilinear Cuts of Non-Constant Radii

2007 ◽  
Author(s):  
Christopher A. Suprock ◽  
John T. Roth ◽  
Larry M. Downey
Keyword(s):  
Condition Monitoring ◽  
Prior Information ◽  
Low Cost ◽  
Rate Of Change ◽  
Cutting Conditions ◽  
Monitoring Technique ◽  
Forecasting Method ◽  
Life Tests ◽  
Impending Fracture ◽  
Monitoring Algorithm

In this paper, an endmill condition monitoring technique is presented for curvilinear cutting. This algorithm operates without the need for prior knowledge of cutting conditions, tool type, cut curvature, cut direction, or directional rate of change. This technique is based on an autoregressive-type monitoring algorithm which is used to track the tool’s condition using a tri-axial accelerometer. Accelerometer signals are monitored due to the sensors relatively low cost and since use of the sensor does not limit the machining envelope. To demonstrate repeatability, eight life tests were conducted. The technique discussed herein successfully prognosis impending fracture or meltdown due to wear in all cases, providing sufficient time to remove the tools before failure is realized. Furthermore, the algorithm produces similar trends capable of forecasting failure, regardless of tool type and cut geometry. Success is seen in all cases without requiring algorithm modifications or any prior information regarding the tool or cutting conditions.

Experimental Introduction to Surface Roughness Parameters Measurement

2013 ◽  
Vol 759 ◽  
pp. 63-71 ◽  
Author(s):  
Daniel Teixidor ◽  
Guillem Quintana ◽  
Joaquim de Ciurana
Keyword(s):  
Surface Roughness ◽  
Engineering Students ◽  
End Milling ◽  
Experimental Methodology ◽  
Milling Operations ◽  
Texture Generation ◽  
Training Courses ◽  
Experimental Introduction ◽  
Machining Operations ◽  
Parameters Measurement

Surface roughness influences the performance of a finished part. In machining operations, the surface roughness generated is influenced by an enormous set of factors. In ball end milling operations, the geometric characteristics of the cut clearly affect the surface crests generated. This paper presents an experimental methodology that permits engineering students to identify and analyze the surface roughness. The methodology is applicable to training courses and surface texture generation as well.

Directionally Independent Failure Prediction of End Mill Cutting Tools: An Investigation of Noise Reduction Using Higher Dimensional Real Fourier Analysis

2006 ◽  
Author(s):  
Christopher A. Suprock ◽  
John T. Roth
Keyword(s):  
Noise Reduction ◽  
End Milling ◽  
Dimensional Space ◽  
Cutting Tools ◽  
Milling Operations ◽  
Higher Dimensional ◽  
Life Tests ◽  
On Line ◽  
System Energy ◽  
Fourier Type

Accurate on-line forecasting of a tool's condition during end-milling operations is advantageous to the functionality and reliability of automated industrial processes. The ability to disengage the tool prior to catastrophic failure reduces manufacturing costs, excessive machine deterioration, and personnel hazards. Rapid computational feedback describing the system's state is critical for realizing a practical failure forecasting model. To this end, spectral analysis by fast Fourier type algorithms allows a rapid computational response. The research described herein explores the development of nontraditional real FFT (Discrete Cosine Transform) based algorithms performed in unique higher-dimensional states of observed datasets. The developed Fourier algorithm is novel since it quantifies chaotic noise rather than relying on the more traditional observation of system energy. By increasing the vector dimensionality of the DCT, the respective linear transform basis will more effectively cross-correlate the transform data into fewer (more significant) transform coefficients. Thus, a single vector in orthogonally higher-dimensional space is observed instead of multiple orthogonal vectors in single-dimensional space. More specifically, a novel noise reduction technique is utilized to track trends measured from tri-axial force dynamometer signals. This transformation effectively achieves both system noise reduction and directional independence by observing the chaotic noise instead of system energy. Algorithm output trends from six end-milling life-tests are tracked from both linear and pocketing maneuvers in order to demonstrate the technique's capabilities. In all six tests, the algorithm predicts impending tool failure with sufficient time for tool removal.

scholarly journals Design and Development of a 5-Channel Arduino-Based Data Acquisition System (ABDAS) for Experimental Aerodynamics Research

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2382 ◽  
Author(s):  
Antonio Vidal-Pardo ◽  
Santiago Pindado
Keyword(s):  
Data Acquisition ◽  
Low Cost ◽  
Data Acquisition System ◽  
High Accuracy ◽  
Acquisition System ◽  
Calibration System ◽  
Experimental Aerodynamics ◽  
Accuracy Level ◽  
The Fourier Transform ◽  
Cup Anemometer

In this work, a new and low-cost Arduino-Based Data Acquisition System (ABDAS) for use in an aerodynamics lab is developed. Its design is simple and reliable. The accuracy of the system has been checked by being directly compared with a commercial and high accuracy level hardware from National Instruments. Furthermore, ABDAS has been compared to the accredited calibration system in the IDR/UPM Institute, its measurements during this testing campaign being used to analyzed two different cup anemometer frequency determination procedures: counting pulses and the Fourier transform. The results indicate a more accurate transfer function of the cup anemometers when counting pulses procedure is used.

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