On-line monitoring of flank wear in turning with multilayered feed-forward neural network

Liu, Qiang; Altintaş, Yusuf

doi:10.1016/s0890-6955(99)00020-6

Cited by 71 publications

(30 citation statements)

References 12 publications

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“…After that, the user can post-process these time signals, for example, to obtain estimations of the specific cutting energy. Once the threshold value for the power consumption in stable cutting is defined, this feature can be used either to predict tool breakages or to keep wear under control [22].…”

Section: Signal Measurement and Analysismentioning

confidence: 99%

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

et al. 2017

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Abstract:The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy, and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on the equations describing the physical laws of the machining processes; however, additional efforts are needed to overcome the gap between scientific research and real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on "deep-knowledge and models" that aid machine designers, production engineers, or machinists to get the most out of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system and process as input data, and generates results that help in the proper decision-making and machining plan. Direct benefits can be found in (a) the fixture/clamping optimal design; (b) the machine tool configuration; (c) the definition of chatter-free optimum cutting conditions and (d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

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Section: Signal Measurement and Analysismentioning

confidence: 99%

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

et al. 2017

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“…After that, user can post-process these time signals, for example, to obtain estimations of the specific cutting energy. Once the threshold value for the power consumption in stable cutting is defined, this feature can be used either to predict tool breakages or to keep wear under control [22].…”

Section: Signals Measurement and Analysismentioning

confidence: 99%

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

Urbikaín¹,

Lacalle²,

Alonso³

et al. 2016

Preprint

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Abstract:The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on "deep knowledge and models" that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.

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“…Different classifiers have been used in this application. The most popular is application of neural networks and fuzzy systems [8,[12][13][14][15] or autoregressive model [16].…”

Section: Introductionmentioning

confidence: 99%

Developing automatic recognition system of drill wear in standard laminated chipboard drilling process

Kurek

Kruk

Osowski

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: "useful" (sharp enough) and "useless" (worn out). The case "useless" indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the "useful" state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state ("useful" versus "useless") can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system. Key words: diagnostic expert systems, neural networks, wavelet packets, wear monitoring.Developing automatic recognition system of drill wear in standard laminated chipboard drilling process [5,7].The key issue in diagnostics of cutting tool state is the selection of sensors. Usually force sensors, electrical power, acoustic emission, vibration and acoustic pressure are applied in TCM [4,8]. The best results in metal working are achieved using force sensors.The disadvantage of this kind of measurement in normal production is that the force or torque transducers are relatively expensive and there are difficulties in mounting these sensors to the cutting tool or work piece. Therefore vibration sensors -easy to install, but at the same time less accurate because of background noise -are most commonly used in wood industry [5]. Vibration measurement is easy, since an accelerometer can be mounted close to the spindle bearing and no modifications of machine tools are needed [9].Quite similar to vibration is the sound signal, which can also be used in assessment of drill state. Mechanical vibration of the cutting tool, machine holder and drill are partly transferred to airborne vibration. It means that part of the information contained in vibration can also be obtained from sound measurement. The acquisition of the sound can be done easily using a microphone. However, the sound pressure sensors (microphones), are even more susceptible to background [7,8]. The sound measurement in an extended range of frequencies from 20 kHz to 80 kHz is usually applied [4]. This range is called the acoustic emission. The effectiveness of acoustic emission...

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On-line monitoring of flank wear in turning with multilayered feed-forward neural network

Cited by 71 publications

References 12 publications

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages

Developing automatic recognition system of drill wear in standard laminated chipboard drilling process

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