Application of AE and cutting force signals in tool condition monitoring in micro-milling

Jemielniak, Krzysztof; Arrazola, P.J.

doi:10.1016/j.cirpj.2008.09.007

Cited by 135 publications

(52 citation statements)

References 13 publications

(16 reference statements)

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“…As the critical aspect of the operation is the cutting-tool state (cutting-tool wear), sensors applied in tool condition monitoring are commonly used. A large number of research works have been conducted for cutting-tool monitoring and some of them applied to micromilling operations on hardened steels can be found in [12][13][14][15]. In general, the monitoring module is composed of a sensor system unit, amplification and conditioning unit, and a signal processing and feature extraction unit.…”

Section: Monitoring Modulementioning

confidence: 99%

“…In general, the monitoring module is composed of a sensor system unit, amplification and conditioning unit, and a signal processing and feature extraction unit. In tool condition monitoring systems for machining operations, different sensors have been successfully applied, such as dynamometers [12,14,16,17], accelerometers [12,15], acoustic emission [13,14], etcetera. Different studies have been also conducted in signal processing and feature extraction [18][19][20][21].…”

Section: Monitoring Modulementioning

confidence: 99%

See 1 more Smart Citation

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Section: Monitoring Modulementioning

confidence: 99%

Section: Monitoring Modulementioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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“…Examples of ANNs applied to the tool condition classifi cation may be found in Rivero (2008), Mehrabian et al (2008), Patra et al (2007) and Kuljanic et al (2009). Jantunen (2002) reported a summary of methods used to detect breakage and wear of the cutt ing tools, showing that cutt ing forces are commonly used to classify the tool wear, also, this fact may be appreciated in works presented by Kuljanic et al (2005), Rivero et al (2008), Bhatt acharyya et al (2007) and Jemielniak et al (2008). In order to evaluate the cutt ing forces, Zuperl et al (2004) and Kurt (2009) developed simulation models, these models determine the cutt ing forces with more precision than the analytical models due to the application of Multi Layer Perceptron (MLP) type ANNs.…”

Section: Introductionmentioning

confidence: 99%

ANN Based Tool Condition Monitoring System for CNC Milling Machines

Mota-Valtierra

Franco-Gasca

Herrera-Ruíz

et al. 2011

IIT

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Sistema basado en redes neuronales artificiales para el monitoreo de la herramienta en fresadoras CNC AbstractMost of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutt ing tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to att ach the system sensors. This paper presents an intelligent classifi cation system which determines the status of cutt ers in a Computer Numerical Control (CNC) milling machine. This tool state is mainly detected through the analysis of the cutt ing forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classifi cation is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital fi lter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the fi ltered signal in order to compress the data amount and to optimize the classifi er structure. Then a multilayer perceptron-type neural network is responsible for carrying out the classifi cation of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutt er.

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“…The most common among them are the measurement of microforces; further, the measurement of audio signals generated by the process; and, finally, the recording and analysis of the process by video (cf. [19,26,44]). Of course, all of these methods may be considered separately as well as in combination of each other.…”

Section: Introductionmentioning

confidence: 99%

A statistical filtering method for denoising of micro-force measurements

Kazimierski

Piotrowska-Kurczewski

Böhmermann

et al. 2016

Int J Adv Manuf Technol

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Precise measurement of mechanical forces is crucial to efficient micro-manufacturing. The quality of such measurements depends heavily on the properties of the noise inevitably accompanying every measurement process. In the micro-range, the signal-to-noise ratio tends to be very low, and the noise dynamic varies for different frequencies. In result, common denoising methods that assume white noise perform poorly in this setting. In this paper, a novel, easily implementable denoising method based on a local statistic of the measured data's spectrum is proposed. By testing it on a representative dataset, it is shown that the proposed method is robust and stable. Particularly, it allows for an efficient retrieval of the force signal encountered in micro-milling processes.

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Application of AE and cutting force signals in tool condition monitoring in micro-milling

Cited by 135 publications

References 13 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

ANN Based Tool Condition Monitoring System for CNC Milling Machines

A statistical filtering method for denoising of micro-force measurements

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