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

Author(s):  
Christopher A. Suprock ◽  
John T. Roth ◽  
Larry M. Downey

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.

Author(s):  
Christopher A. Suprock ◽  
John T. Roth ◽  
Larry M. Downey

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.


Author(s):  
Justin L. Milner ◽  
John T. Roth

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.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3056
Author(s):  
Baiqian Shi ◽  
Stephen Catsamas ◽  
Peter Kolotelo ◽  
Miao Wang ◽  
Anna Lintern ◽  
...  

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.


2020 ◽  
Vol 87 (s1) ◽  
pp. s79-s84
Author(s):  
Qummar Zaman ◽  
Senan Alraho ◽  
Andreas König

AbstractThe conventional method for testing the performance of reconfigurable sensory electronics of industry 4.0 relies on the direct measurement methods. This approach gives higher accuracy but at the price of extremely high testing cost and does not utilize the new degrees of freedom for measurement methods enabled by industry 4.0. In order to reduce the test cost and use available resources more efficiently, a primary approach, called indirect measurements or alternative testing has been proposed using a non-intrusive sensor. Its basic principle consists in using the indirect measurements, in order to estimate the sensory electronics performance parameters without measuring directly. The non-intrusive property of the proposed method offers better performance of the sensing electronics and virtually applicable to any sensing electronics. Efficiency is evaluated in terms of model accuracy by using six different classical metrics. It uses an indirect current-feedback instrumentation amplifier (InAmp) as a test vehicle to evaluate the performance parameters of the circuit. The device is implemented using CMOS 0.35 μm technology. The achieved maximum value of average expected error metrics is 0.24, and the lowest value of correlation performance metrics is 0.91, which represent an excellent efficiency of InAmp performance predictor.


Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.


Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 1389-1394
Author(s):  
Agusmian Partogi Ompusunggu ◽  
Kerem Eryılmaz ◽  
Karel Janssen

Author(s):  
Emran Md Amin ◽  
Nemai Chandra Karmakar

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.


Author(s):  
Bhatraj Anudeep ◽  
Paresh Kumar Nayak

Abstract In distributed generation (DG) systems, the rate of change of voltage and the rate of change of frequency are the two most common and widely used simple and low-cost passive islanding detection schemes. Unfortunately, these passive islanding detection schemes find limitation for detecting the islandings that cause very small power imbalance. In this paper, an improved passive islanding detection scheme is proposed by using the two newly derived indices from the sequence components of the current signal with the conventional voltage and frequency parameters. The performance of the proposed scheme is tested for numerous islanding and non-islanding cases generated on IEEE Std 399–1997 and IEC microgrid model distribution system integrated with both inverter-interfaced and synchronous DGs through PSCAD/EMTDC. The obtained results confirm the effectiveness of the proposed scheme.


1999 ◽  
Vol 121 (4) ◽  
pp. 559-567 ◽  
Author(s):  
J. T. Roth ◽  
S. M. Pandit

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.


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