A method for tool condition monitoring based on sensor fusion

Zhang, Kaifeng; Yuan, Huiqun; Nie, Peng

doi:10.1007/s10845-015-1112-y

Cited by 64 publications

(18 citation statements)

References 23 publications

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“…Nine combinations of cutting parameters, i.e., cutting speeds v c = [10,15,20] m/min and feed rates f = [0.03, 0.06, 0.09] mm/rev, were used in this study (Table 1). They were chosen based on tool manufacturer recommendations.…”

Section: Experimental Setup and Drilling Parametersmentioning

confidence: 99%

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On-Line Workpiece Hardness Monitoring in Stone Machining

Klaić

Brezak

Staroveški

et al. 2020

TFAMENA

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The application of four types of process signals in the indirect on-line monitoring of stone hardness has been analysed in this paper. Cutting forces, servomotor currents, vibration and acoustic emission signals were measured during the drilling of three types of stones characterised by different hardness and heterogeneity values. A group of features were extracted from each signal from the time and frequency domain. Their capacity to correctly classify stone hardness was analysed using an artificial neural network classifier. Stone samples were drilled with new drill bits and drill bits worn to three different wear levels in order to analyse the influence of tool wear on the hardness classification process. Nine combinations of cutting parameters were applied for each drill wear level and stone type. Features extracted from the vibration signals obtained the best results in the stone hardness classification. The results indicate their potential industrial application, since they have achieved a high classification precision regardless of the drill bit wear level.

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Section: Experimental Setup and Drilling Parametersmentioning

confidence: 99%

“…Cutting forces and servomotor currents were two most utilised types of signals for the indirect monitoring of stone mechanical properties or the machining process modelling [16,17]. Other types of signals often used in industrial monitoring systems, such as vibration and acoustic emission signals [18][19][20][21], were not analysed.…”

Section: Introductionmentioning

confidence: 99%

On-Line Workpiece Hardness Monitoring in Stone Machining

Klaić

Brezak

Staroveški

et al. 2020

TFAMENA

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“…Thus, it includes two characteristics, including EMI and EMS. In this research, the radiated emission which may influence normal operation of a product by a medical-grade liquid crystal computer during use is considered as a response value, test environment and data collection for the experiment as described below: Radiated Emission (RE) (dB): It means that the electromagnetic noise is radiated and propagated through a space in the form of electromagnetic field energy, and coupled to interfered devices (or their circuits) [24,25]. Noise of various frequencies may be generated in the EMI measurement of a product.…”

Section: Response Valuesmentioning

confidence: 99%

Using Parametric Design to Reduce the Emi of Electronics Products - Example of Medical-Grade Touch Panel Computer

Huang¹,

Chen²,

Greene³

2019

PIER C

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With technical advancement and development, the amount of electronic equipment is increasing, while the functions of products are enhanced, and the routing density of Printed Circuit Boards (PCBs) becomes larger. In the electronic industry, medical instruments are used to diagnose, treat, mitigate or prevent human diseases, and maintain and promote health. Industrial PCs for medical use and their accessories should be immune to interference from external electromagnetic noise, and should not become interference sources of electromagnetic noise radiation, so they have become issues of interest with respect to ensuring safety of medical equipments in medical operation environments in recent years. This research relates to parametric design using the Taguchi Method in the early stage of product development for medical-grade touch panel computers. In considering the use of Radiated Emission (RE) in Electromagnetic Compatibility (EMC) as a response value, the experiment covers control factors such as PCB and mechanism design related parameters. In addition, peripheral devices used in conjunction with a product are considered as noise factors when the product is in use, while interaction between the control factors is studied. The Taguchi Method is used to select an appropriate inner/outer orthogonal array, and a response diagram and a variance method are used for analysis to provide an optimal set of design parameters, in which the number of routing layers of a riser card is 6; the EMI filter on the isolated card is 600 Ω; the shunt capacity for the clock on main board is 33p; and the isolated card is grounded. Moreover, it is found that an interaction exists between the number of routing layers of the riser card and the EMI filter of the isolated card. From the result of the experiment, with such a set of parameters, the SN (Signal to Noise Ratio) lies in the confidence interval, indicating good reproducibility of the experiment. Such a parametric design effectively improves the electromagnetic interference (EMI) characteristics of a product to meet design specifications required by customers, accelerate the R&D process of electronic products, and pass EMI test regulations required by various countries in order to improve industrial competitiveness.

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“…The tool condition was estimated from the SVM results (Kaya et al, 2012). TCMs was designed using SVM with wavelet features of AE and sound signal and attain the better classification accuracy (Zhang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

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This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

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A method for tool condition monitoring based on sensor fusion

Cited by 64 publications

References 23 publications

On-Line Workpiece Hardness Monitoring in Stone Machining

On-Line Workpiece Hardness Monitoring in Stone Machining

Using Parametric Design to Reduce the Emi of Electronics Products - Example of Medical-Grade Touch Panel Computer

Decision support system for tool condition monitoring in milling process using artificial neural network

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