A Review of Sensor System and Application in Milling Process for Tool  Condition Monitoring

Rizal, Muhammad; Ghani, Jaharah A; Nuawi, Mohd. Zaki; Haron, Che Hassan Che

doi:10.19026/rjaset.7.502

Cited by 44 publications

(25 citation statements)

References 119 publications

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“…Multiple sensor-based methods can enhance the richness of information that contains potential tool wear levels [ 18 ]. Although multisensor setups provide more redundant information, they can reduce the overall uncertainty of the measurement and improve the resolution and accuracy of the TCM system [ 4 , 19 , 20 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Zhou

Wang

2018

Sensors

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Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Zhou

Wang

2018

Sensors

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“…In addition, by analyzing vibration signals (i.e., by analyzing the electrical outputs of the accelerometers), the criteria are successfully established for the system to accurately simulate the milling processes and cutting conditions in real time (for example, the wear conditions of the cutter and the saw breaker appearance). Study [5] provides an overview of modern sensor technology and its application in the milling process for measuring the processing signal for tool monitoring systems. Processing signals such as cutting force, torque, vibration, acoustic emission, current / power, sound and temperature during milling are briefly considered to determine the process parameters.…”

Section: Fig 1 Schematic Representation Of the End Milling Processmentioning

confidence: 99%

Self-powered wireless sensor system application for cutting process con-trol

Karpavicius

Ostaševičius

Jurenas

et al. 2017

mech

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“…According to the smart machining-monitoring technologies provided by leading machining companies [ 9 , 10 , 11 , 12 ], monitoring of milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence) can produce many significant benefits such as improving machining automation, preventing machining failures, decreasing machine downtime, and reducing maintenance costs. More recently, wireless sensing technology has been widely used in industrial and medical applications [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Specifically for the machining and manufacturing industries, researchers have demonstrated novel wireless sensing applications for real-time machining monitoring [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

Chung

Yeh

Lü

et al. 2016

Sensors

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An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

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A Review of Sensor System and Application in Milling Process for Tool Condition Monitoring

Cited by 44 publications

References 119 publications

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Self-powered wireless sensor system application for cutting process con-trol

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

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