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.

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.

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.

scholarly journals A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3056
Author(s):  
Baiqian Shi ◽  
Stephen Catsamas ◽  
Peter Kolotelo ◽  
Miao Wang ◽  
Anna Lintern ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Water Depth ◽  
Asset Management ◽  
Low Cost ◽  
Rate Of Change ◽  
Urban Drainage ◽  
Urban Stormwater ◽  
High Resolution Data ◽  
Conductivity Sensor ◽  
Level Sensor

High-resolution data collection of the urban stormwater network is crucial for future asset management and illicit discharge detection, but often too expensive as sensors and ongoing frequent maintenance works are not affordable. We developed an integrated water depth, electrical conductivity (EC), and temperature sensor that is inexpensive (USD 25), low power, and easily implemented in urban drainage networks. Our low-cost sensor reliably measures the rate-of-change of water level without any re-calibration by comparing with industry-standard instruments such as HACH and HORIBA’s probes. To overcome the observed drift of level sensors, we developed an automated re-calibration approach, which significantly improved its accuracy. For applications like monitoring stormwater drains, such an approach will make higher-resolution sensing feasible from the budget control considerations, since the regular sensor re-calibration will no longer be required. For other applications like monitoring wetlands or wastewater networks, a manual re-calibration every two weeks is required to limit the sensor’s inaccuracies to ±10 mm. Apart from only being used as a calibrator for the level sensor, the conductivity sensor in this study adequately monitored EC between 0 and 10 mS/cm with a 17% relative uncertainty, which is sufficient for stormwater monitoring, especially for real-time detection of poor stormwater quality inputs. Overall, our proposed sensor can be rapidly and densely deployed in the urban drainage network for revolutionised high-density monitoring that cannot be achieved before with high-end loggers and sensors.

Application of Modern Emission Spectrography in Oil Monitoring

2011 ◽  
Vol 138-139 ◽  
pp. 723-726
Author(s):  
Bo Jian Yu ◽  
Xin Nian Li ◽  
Xu Feng Jiang
Keyword(s):  
Condition Monitoring ◽  
Analytical Process ◽  
The Core ◽  
Monitoring Technique ◽  
Oil Monitoring

In this paper, the principles of modern emission spectrography technique was described and the analytical process and characteristics of it was introduced. Based on an review of its application in equipment oil monitoring, emission spectrography was thought a very valuable technique of lubricants condition monitoring. Now, emission spectrography has become the core of the modern oil monitoring technique, and its development and application prospect are extremely wide.

scholarly journals Condition monitoring of critical industrial assets using high performing low-cost MEMS accelerometers

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 1389-1394
Author(s):  
Agusmian Partogi Ompusunggu ◽  
Kerem Eryılmaz ◽  
Karel Janssen
Keyword(s):  
Condition Monitoring ◽  
Low Cost ◽  
High Performing ◽  
Mems Accelerometers

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.

Chipless RFID Sensor for High Voltage Condition Monitoring

2012 ◽  
pp. 304-333
Author(s):  
Emran Md Amin ◽  
Nemai Chandra Karmakar
Keyword(s):  
Condition Monitoring ◽  
High Voltage ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Base Station ◽  
Novel Approach ◽  
Frequency Identification ◽  
On Line ◽  
Passive Rfid ◽  
Passive Circuit

A novel approach for non-invasive radiometric Partial Discharge (PD) detection and localization of faulty power apparatuses in switchyards using Chipless Radio Frequency Identification (RFID) based sensor is presented. The sensor integrates temperature sensing together with PD detection to assist on-line automated condition monitoring of high voltage equipment. The sensor is a multi-resonator based passive circuit with two antennas for reception of PD signal from the source and transmission of the captured PD to the base station. The sensor captures PD signal, processes it with designated spectral signatures as identification data bits, incorporates temperature information, and retransmits the data with PD signals to the base station. Analyzing the PD signal in the base station, both the PD levels and temperature of a particular faulty source can be retrieved. The prototype sensor was designed, fabricated, and tested for performance analysis. Results verify that the sensor is capable of identifying different sources at the events of PD. The proposed low cost passive RFID based PD sensor has a major advantage over existing condition monitoring techniques due to its scalability to large substations for mass deployment.

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