Tool condition monitoring of PCB milling machine based on vibration analysis

Gomathi, K.; Balaji, A.N.

doi:10.1016/j.matpr.2020.12.778

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…According to comprehensive reviews of Tool Condition Monitoring Systems [4][5][6][7], the most commonly used signal acquisition methods have been acoustic sensors, vibrometers, and cutting forces [8][9][10][11]. However, in the last decade, acquisition systems based on motor current and power consumption have increased considerably because they are cheap and easy to install and use in case the machine does not implement them by default [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Catastrophic Tool Failure Detection in Aeronautical Industrial Drilling Systems Based on Spindle Power Consumption Analysis

Guerra-Sancho,

Domínguez-Monferrer,

Miguélez

et al. 2023

10th Manufacturing Engineering Society International Conference (MESIC 2023)

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The aeronautical industry is at the forefront of the fourth industrial revolution, which implies an exponential deployment of monitorization, Data Analytics, and connectivity. In alignment with this new paradigm, this research work presents a Catastrophic Tool Failure (CTF) analysis based on spindle power consumption monitoring in an industrial aircraft fuselage drilling process. In the case under study, the airframe components are arranged in hybrid stacks of Carbon Fiber Reinforced Plastic (CFRP) and titanium (Ti6Al4V) during drilling, which adds to the highly variable industrial machining conditions. This inherent complexity can lead to CTF, a significant concern due to its associated cost and time, especially in automatic processes. Industrial CTF detection systems based on motor power consumption establish maximum and minimum power limits to detect tool breakage. However, these systems generate many false positives and false negatives due to process variability and unforeseen events. Therefore, an Exploratory Data Analysis (EDA) of the power spindle consumption signals and other machining-related features is proposed to gain insights into the breakage nature and develop more effective detection systems. This analysis is oriented to set the basis for real-time Catastrophic Tool Failure detection from power spindle consumption monitoring. As a result, advanced processing time-domain detection methods are proposed.

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Section: Introductionmentioning

confidence: 99%

Catastrophic Tool Failure Detection in Aeronautical Industrial Drilling Systems Based on Spindle Power Consumption Analysis

Guerra-Sancho,

Domínguez-Monferrer,

Miguélez

et al. 2023

10th Manufacturing Engineering Society International Conference (MESIC 2023)

View full text Add to dashboard Cite

show abstract

“…That includes data acquisition, data merging, data cleaning, smoothing, and bit tip inlet detection. According to comprehensive reviews of Tool Condition Monitoring Systems [12,13], the most commonly used signal acquisition methods have been acoustic sensors, vibrometers, and cutting forces [14][15][16]. However, in the last decade, the use of acquisition systems based on motor current and power consumption has increased considerably because they are cheap and easy to install and use in case the machine does not implement them by default [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Domínguez-Monferrer

Fernández-Pérez²,

Santos³

et al. 2022

Journal of Manufacturing Systems

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“…Therefore, direct measurement method is no longer suitable for current intelligent manufacturing system that emphasizes the continuity and efficiency of production process. For indirect measurement, the cutting signals related to tool wear, such as cutting force [5], acoustic [6], vibration [7], spindle current and power [8], are usually collected to monitor tool wear. Based on the mapping relationships between these signals and tool wear, the real-time tool status could be inferred indirectly.…”

Section: Introductionmentioning

confidence: 99%

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

Qiang¹,

Shi²,

Liu³

et al. 2022

Preprint

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Tool wear is inevitable in actual manufacturing, especially in extreme processing conditions for milling difficult-to-cut materials. With the degradation of cutting performance, the undesirable tool status could trigger instability in machining process that causes negative effect on machined surface and dimension accuracy of the precision components. Therefore, tool condition monitoring for machining process is imperative. With distinctive characteristics on easy acquisition and non-interference in machining process, the power consumption of machine tool has been proved to be a reliable indicator for reflecting the changes of cutting process in industry. Thus, the present study proposes a novel method for monitoring tool condition using net cutting power consumption. The result indicates that the developed approach performs better than traditional approach which uses original total power to predict tool wear. This can be attributed to the changes of net cutting power consumption is more relevant with tool wear evolution. Furthermore, predicting tool wear under variable cutting parameters is achieved using Gaussian process regression, because it has good generalization performance and also explains the uncertainty of prediction results in the form of probability. This study reveals that low-cost sensor like power meter, can be used as an important supplement to monitor tool condition in the industry, and also provides a research basis for predicting tool wear under different cutting conditions.

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Tool condition monitoring of PCB milling machine based on vibration analysis

Cited by 9 publications

References 14 publications

Catastrophic Tool Failure Detection in Aeronautical Industrial Drilling Systems Based on Spindle Power Consumption Analysis

Catastrophic Tool Failure Detection in Aeronautical Industrial Drilling Systems Based on Spindle Power Consumption Analysis

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

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