On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence

Vallejo, Antonio J.

doi:10.5772/5833

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…In addition, critical cutting parameters with larger cutting depth can achieve maximum removal rate with high efficiency, but it may lead to poor surface quality due to excessive tool vibration or wear damage. Therefore, monitoring of the workpiece surface accuracy and tool damage are major concerns [18][19][20][21]. Authors in [18][19] applied sensing modules to measure the tool vibration and to detect possible damage of the tool.…”

Section: Introductionmentioning

confidence: 99%

Development of Surface Roughness Prediction and Monitoring System in Milling Process

Lai,

Lin,

Lin

et al. 2024

Eng. Technol. Appl. Sci. Res.

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High surface quality is an important indicator for high-performance machining during the manufacturing process. The surface roughness generated in machining can be affected by cutting parameters and machining vibration. To achieve processing efficiency, monitoring surface quality within the desired range is important. This study aimed to develop a surface roughness prediction system for the milling process. The predictive model was established based on data collected from machining experiments with the response surface methodology. The surface roughness is related to independent variables, including cutting parameters and machining vibration, in terms of nonlinear functions by regression analysis and the neural network approach, respectively. To be implemented in a CNC milling machine for online application, a predictive model was introduced in the Virtual Machine Extension (VMX) intelligent software development platform. This model can acquire the cutting parameters from the controller via the Open Platform Communications Unified Architecture (OPCUA) interface as well as the vibration features from the sensory module. The system can calculate the roughness based on these data and issue alert when the predicted value exceeds the preset threshold or abnormal vibration is detected.

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Section: Introductionmentioning

confidence: 99%

Development of Surface Roughness Prediction and Monitoring System in Milling Process

Lai,

Lin,

Lin

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…In addition, the frequency level of the AE signals produced from cutting processes has been found to be separable from audible noise [6]. In general, for TCM, an AE sensor mounted on a spindle or cutting tool showed higher reliability than when mounted on the workpiece [7][8][9]. Most of the efforts in modeling acoustic emission in manufacturing processes are built on the same model [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes

Hassan

Sadek

Attia

2023

Sensors

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Tool Condition Monitoring systems are essential to achieve the desired industrial competitive advantage in terms of reducing costs, increasing productivity, improving quality, and preventing machined part damage. A sudden tool failure is analytically unpredictable due to the high dynamics of the machining process in the industrial environment. Therefore, a system for detecting and preventing sudden tool failures was developed for real-time implementation. A discrete wavelet transform lifting scheme (DWT) was developed to extract a time-frequency representation of the AErms signals. A long short-term memory (LSTM) autoencoder was developed to compress and reconstruct the DWT features. The variations between the reconstructed and the original DWT representations due to the induced acoustic emissions (AE) waves during unstable crack propagation were used as a prefailure indicator. Based on the statistics of the LSTM autoencoder training process, a threshold was defined to detect tool prefailure regardless of the cutting conditions. Experimental validation results demonstrated the ability of the developed approach to accurately predict sudden tool failures before they occur and allow enough time to take corrective action to protect the machined part. The developed approach overcomes the limitations of the prefailure detection approach available in the literature in terms of defining a threshold function and sensitivity to chip adhesion-separation phenomenon during the machining of hard-to-cut materials.

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“…The well-known textbook provides the overall information about digital image processing approaches [5]. Several researchers have studied the tool wear measurement, geometry inspection, or tool condition monitoring by image processing because of the great significance [6][7][8][9][10][11][12]. [13] presented a tool wear measuring system for end mills by using a CCD camera and an exclusive jig.…”

Section: Introductionmentioning

confidence: 99%

Optical Image Inspection of Cutting Tool Geometry for Grinding Machines

Chen

Chang

Lee

et al. 2012

AMR

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The purpose of this paper is to develop a tool image inspection and measuring system by C++ Builder. Firstly, tool images are captured online for geometry analysis via a disassembled inspection mechanism mounted on the Z-axis of a five-axis tool grinding machine. One can use the controller of the machine to set the coordinate location of the mechanism and implement the humanized functions of autofocusing and automatic measurements. The digital images are calculated by the subpixel approach to improve the measurement resolution, and filtered the edge point location by Hough transform to upgrade the precision. The human machine interface (HMI) has a tutoring manner for users to operate the measuring procedures. These proposed functions can measure the geometric dimension such as the diameter, radius, and angle of different end mills or drills after finishing the tool grinding processes. Furthermore, the grinding processes can refer the online measured results to compensate the tool dimension. Therefore, this online image inspection and measuring system can improve the precision of tool grinding, product quality, and reduce the product cost. Finally, experiments are presented to show that the repeatability errors are ± 2 μm and ± 1 μm for the diameter and the radius measurements of end mills, respectively. The percentage error is 0.116％ for measuring the point angle of a drill. Thus, the results demonstrate the effectiveness of the proposed method that can be employed to measure tool geometry of different cutting tools.

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On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence

Cited by 7 publications

References 25 publications

Development of Surface Roughness Prediction and Monitoring System in Milling Process

Development of Surface Roughness Prediction and Monitoring System in Milling Process

A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes

Optical Image Inspection of Cutting Tool Geometry for Grinding Machines

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