Tool wear evaluation by stereo vision and prediction by artificial neural network

Prasad, Kamisetti N V; Ramamoorthy, B.

doi:10.1016/s0924-0136(00)00896-7

Cited by 61 publications

(7 citation statements)

References 16 publications

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“…Measurement error is reported to be less than 5 %. Niranjan Prasad and Ramamoorthy [15] measured the crater wear using stereo vision. Stereo vision is not achieved by using two cameras but with the tool moving between two images taken with the same grey-scale camera.…”

Section: Tool Wear Measurement Reviewmentioning

confidence: 99%

A New Approach to Spatial Tool Wear Analysis and Monitoring

Čerče¹,

Pušavec²,

Kopač³

2015

SV-JME

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The tool wear of cutting tools has a very strong impact on the product quality as well as on the efficiency of the machining processes. Despite the current high automation level in the machining industry, a few key issues prevent complete automation of the entire turning process. One of these issues is tool wear, which is usually measured off the machine tool. Therefore, its in-line characterization is crucial. This paper presents an innovative, robust and reliable direct measurment procedure for measuring spatial cutting tool wear in-line, using a laser profile sensor. This technique allows for the determination of 3D wear profiles, which is an advantage over the currently used 2D subjective techniques (microscopes, etc.). The use of the proposed measurement system removes the need for manual inspection and minimizes the time used for wear measurement. In this paper, the system is experimentally tested on a case study, with further in-depth analyses of spatial cutting tool wear performed. In addition to tool wear measurements, tool wear modelling and tool life characterization are also performed. Based on this, a new tool life criterion is proposed, which includes the spatial characteristics of the measured tool wear. The results of this work show that novel tool wear and tool life diagnostics yield an objective and robust methodology allowing tool wear progression to be tracked, without interruptions in the machining process or in the performance of the machining process. This work shows that such an automation of tool wear diagnostics, on a machine tool, can positively influence the productivity and quality of the machining process. Keywords: machining process, tool wear measurement, spatial tool wear, in-line monitoring, volumetric estimator, tool life prediction Highlights • Newly developed measuring system to determine spatial cutting tool wear in-line. • Spatial cutting tool wear measurements were performed on a case study. • Tool wear modelling and tool life characterization has been performed. • A new tool life criterion is proposed.

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Section: Tool Wear Measurement Reviewmentioning

confidence: 99%

A New Approach to Spatial Tool Wear Analysis and Monitoring

Čerče¹,

Pušavec²,

Kopač³

2015

SV-JME

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“…This is time consuming when window size chosen is large. The sequential similarity detection algorithm (SSDA) provides a reliable method of template matching [8]. For a given window with a size of n m × , the similarity is denoted as D S , and computed as:…”

Section: Matching Algorithmmentioning

confidence: 99%

Measurement Technique of Grinding Wheel Topography Based on Binocular Stereo Vision

Zhang

Hong

2006

MSF

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Measurement of the grinding wheel topography and its change in machining process is important for revealing the grinding principle of high-temperature brazed mono-layer super-abrasive grinding wheel. A new approach of measuring grinding wheel topography based on binocular stereovision is presented. Firstly, the binocular images are achieved while the optical axes of the two cameras are parallel, and a corner reflection function algorithm is applied to detect corners. Secondly, in order to get matching corners, optimal matching template is obtained by matching curves. The experimental results show that the sequential similarity matching operation with optimal template has advantages of high accuracy, and less error matching. Finally, the height of the corners is obtained by calculating their parallax on left and right image, and the height error is less than 5%.

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“…Dynamical investigation comprises the evaluation of the force of cutting for determining the nervousness motion quality of the manufacturing equipment. At present, standard methods to interaction evaluation contain the single-frequency method [12],the multifrequency technique [13], the timedomain form nite simulation method [14], and the discrete method [15][16][17].Tool wear monitoring has garnered considerable interest among researchers in recent times [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Sun et al [37] Effectively carried out fault detection on an enormous rotational machine utilizing algorithms for classi cation.…”

Section: Introductionmentioning

confidence: 99%

Improving CNC Turning Machine Precision through Vibration Analysis for Clamping Error Detection Employing Principal Component Analysis

Ullah,

Chan,

Xie

2024

Preprint

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The vibration caused by mechanical manufacturing will lead to unpredictable changes in product quality, which will increase the manufacturing cost. Plenty of research is imposed to establish a normal vibration coefficient or to develop an accurate and efficient production process. Therefore, various health diagnosis methods and feedback mechanisms are obtained, such as dynamic feature Detection, machine tool state Detection, cutting chatter analysis, health state feedback of specific parts in the machine tool, and so on. This study analyzes the lathe and establishes the vibration analysis and health diagnosis method used for NC lathe or traditional machine tools through the vibration signals generated by different clamping states. Before the spindle speed reaches 2000 rpm, 5 experiments at an interval of 250- rpm and three clamping states are executed. Moreover, the vibration signal is obtained using the intelligent prediction and diagnosis performance system, analyzed in the frequency domain, matched with the root mean square result, and checked for the accuracy of the vibration signal data and vibration eigenvalues. Then, digital tools are used to filter the signal according to the filtered outliers and the matching results of feature extraction. Principal component analysis (PCA) and the projection matrix are used to evaluate 37 features and reduce the dimension of the data, to obtain the vibration data distribution map under different rotating speeds of each clamping state. It can identify the distribution range and tightness of the distribution map under each clamping state. In the above experiment, the signal data of each 250-rpm interval and three clamping states were collected and compared. The results show that it is feasible to use the PCA method to determine the change of vibration value in the CNC lathe and establish a health state feedback data set based on the vibration change mechanism.

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Tool wear evaluation by stereo vision and prediction by artificial neural network

Cited by 61 publications

References 16 publications

A New Approach to Spatial Tool Wear Analysis and Monitoring

A New Approach to Spatial Tool Wear Analysis and Monitoring

Measurement Technique of Grinding Wheel Topography Based on Binocular Stereo Vision

Improving CNC Turning Machine Precision through Vibration Analysis for Clamping Error Detection Employing Principal Component Analysis

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