Supervision of Carbide Tool Condition by Training of Vibration-based Statistical Model using Boosted Trees Ensemble

Khairnar, Apoorva; Patange, Abhishek D.; Pardeshi, Sujit S.; Jegadeeshwaran, R.

doi:10.23940/ijpe.21.02.p7.229240

Cited by 18 publications

(8 citation statements)

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“…The primary objective of this system is to acquire wheel hub vibration and secondarily to store the data for analyzing trend of response. An open source low-priced microcontroller Arduino Mega was interfaced with an accelerometer ADXL335 and communicated to Microsoft Excel to accumulate & exhibit the acquired readings [25][26][27].…”

Section: In-house Design and Development Of Daqmentioning

confidence: 99%

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Pardeshi¹,

Patange²,

Jegadeeshwaran³

et al. 2022

Structural Durability &Amp; Health Monitoring

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The regulation of tyre pressure is treated as a significant aspect of 'tyre maintenance' in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the same in near future. The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle. In order to examine the vibration response of a wheel hub, the in-house design & development of DAQ (Data Acquisition System) is described. Micro Electro-Mechanical Scheme (MEMS) built accelerometer is incorporated with open source hardware and software to collect and store the data. This framework is easy to develop, monitor and can be retrofitted in two wheeled vehicle. For various pressure conditions, the change in response of wheel hub vibration with respect to time is collected. The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters. The classification of different conditions of tyre pressure is carried out using ML classifiers.

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Section: In-house Design and Development Of Daqmentioning

confidence: 99%

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Pardeshi¹,

Patange²,

Jegadeeshwaran³

et al. 2022

Structural Durability &Amp; Health Monitoring

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“…The current tendency is to use SVM and boosted trees to address prediction issues that fall under either classification or regression issues [31] [32]. These techniques are less prone to overfitting than other prediction models like neural networks or K-nearest neighbor.…”

Section: Introductionmentioning

confidence: 99%

Machine vision-based gradient-boosted tree and support vector regression for tool life prediction in turning

Bagga

Patel

Makhesana

et al. 2023

Int J Adv Manuf Technol

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One of the essential elements of automated and intelligent machining processes is accurately predicting tool life. It also helps in achieving the goal of producing quality products with reduced production costs. This work proposes a computer vision-based tool wear monitoring and tool life prediction system using machine learning methods. Gradient-boosted trees and support vector machine (SVM) techniques are used to predict tool life. The experimental investigation on the CNC machine is conducted to study the applicability of the proposed tool wear monitoring system. Experiments are performed using workpiece material made of alloy steel and PVD-coated cutting inserts, and flank wear is monitored. An imaging system consisting of an industrial camera, lens, and LED ring light is mounted on the machine to capture tool wear zone images. Images are then processed by algorithms developed in MATLAB®. Boosted tree methods and the SVM methodology have 96% and 97% prediction accuracy, respectively. Validation tests are carried out to determine the accuracy of proposed models. It is observed that the prediction accuracy of boosted three and SVM is good, with a maximum error of 5.89% and 7.56%, respectively. The outcome of the study established that the developed system can monitor the tool wear with good accuracy and can be adopted in industries to optimize the utilization of tool inserts.

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“…The behavior of these models is easier to explain. Decision trees can also achieve high accuracy in some cases, particularly when using bagging and boosting techniques to create ensemble models [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Validation of Digital Agriculture Models

Romero-Gainza

Stewart

2023

Sensors

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Digital agriculture employs artificial intelligence (AI) to transform data collected in the field into actionable crop management. Effective digital agriculture models can detect problems early, reducing costs significantly. However, ineffective models can be counterproductive. Farmers often want to validate models by spot checking their fields before expending time and effort on recommended actions. However, in large fields, farmers can spot check too few areas, leading them to wrongly believe that ineffective models are effective. Model validation is especially difficult for models that use neural networks, an AI technology that normally assesses crops health accurately but makes inexplicable recommendations. We present a new approach that trains random forests, an AI modeling approach whose recommendations are easier to explain, to mimic neural network models. Then, using the random forest as an explainable white box, we can (1) gain knowledge about the neural network, (2) assess how well a test set represents possible inputs in a given field, (3) determine when and where a farmer should spot check their field for model validation, and (4) find input data that improve the test set. We tested our approach with data used to assess soybean defoliation. Using information from the four processes above, our approach can reduce spot checks by up to 94%.

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Supervision of Carbide Tool Condition by Training of Vibration-based Statistical Model using Boosted Trees Ensemble

Cited by 18 publications

References 0 publications

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Machine vision-based gradient-boosted tree and support vector regression for tool life prediction in turning

AI-Driven Validation of Digital Agriculture Models

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