Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations

Jun, Martin B. G.; Özdoğanlar, O. Burak; DeVor, Richard E.; Kapoor, Shiv Gopal; Kirchheim, Andreas; Schaffner, Georges

doi:10.1016/s0890-6955(01)00156-0

Cited by 73 publications

(32 citation statements)

References 2 publications

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“…3 shows sensors integrated into the main force flux of the motor spindle. These concepts have been slow to transfer into practice because the spindle or structure itself must be characterised and strategies to isolate process phenomena from spindle and machine dynamics must be developed [28][29][30][31][32].…”

Section: Piezoelectric Sensorsmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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Section: Piezoelectric Sensorsmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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“…The second focuses on the developments in, trends observed in, and conclusions from, research performed in the areas of turning, face milling, drilling and end milling. The third and final section draws overall conclusions and suggests future [43] Ultrasonic emission [102] [16] Chip reflectance [5] Cutting forces [21,[68][69][70][71][72]78] [8, 19, 29, 30, 32-36, 38-43, 45] [ 49, 51, 53, 54, 56, 58-64, 99, 103, 104] [5, 12-15, 25, 26, 105] Cutting speed sensors [3,73] [ 49,51,53,55,64,102,104] [ 16,17] Custom sensors [18,20] [99] [83] Vision systems [68,74] directions for TCM research based on successful and currently under-utilized strategies.…”

mentioning

confidence: 96%

State-of-the-art methods and results in tool condition monitoring: a review

Rehorn

Jiang

Orban³

2005

Int J Adv Manuf Technol

330

118

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This paper presents a review of the state-of-the-art in sensors and signal processing methodologies used for tool condition monitoring (TCM) systems in industrial machining applications. The paper focuses on the technologies used in monitoring conventional cutting operations, including drilling, turning, end milling and face milling, and presents important findings related to each of these fields. Unlike existing reviews, which categorize results according to the methodology used, this paper presents results organized according to the type of machining operation carried out. By extensively reviewing and categorizing over one hundred important papers and articles, this paper is able to identify and comment on trends in TCM research, and to identify potential weaknesses in certain areas. The paper concludes with a list of recommendations for future research based on the trends and successful results observed, thus facilitating the cross-fertilization of ideas and techniques within the field of TCM research.

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“…It has the basic ability to learn, memorize, and generalize. (11)(12)(13)(14)(15)(16) Among the many types of neural network, the back propagation neural network is the core of all the others and has the following four advantages: (1) function approximation: train the input vector and correspondent output vector to a model and establish function approximation; (2) model identification: link an undetermined output vector with an input vector, thereby achieving the effect of discrimination; (3) classification: define and classify the type of input vector; and (4) data compression: reduce the dimension of the input vector for convenient transfer and storage.…”

Section: Back Propagation Neural Network For Mtdsmentioning

confidence: 99%

Development of a Machine Tool Diagnostic System Using Micro-Electromechanical System Sensors: A Case Study

2015

Sensors and Materials

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Key words: MEMS sensor, back propagation neural network, machine tool diagnostic systemIn this study, micro-electromechanicals (MEMS) system sensors were used to extract physical signals from a machine tool. A performance assessment was carried out using fuzzy logic theory. This, in turn, was used to judge the ownership of various signals. It then defined the level that each ownership group belonged to in order to determine whether the performance was broken or not. If normal, the system determined specified rules for such normality. During the process of extracting the vibration and noise signals, the system used Fourier transform to analyze any changes made to each signal in the frequency field, and then principal component analysis was used to decrease the data dimensions. We then evaluated the work status of the machine tool on the basis of the signal features. Furthermore, we built a feature math model from the recorded signals using a back propagation neural network and further determined the abnormal items using an error function. Finally, we obtained a diagnostic feature for the performance of the machine tool using physical signals through diagnostic reports from a humanmachine interface. The machine tool diagnostic system is able to provide maintenance personnel with a proper way of responding quickly to any reduction in the output from the machine tool so as to avoid further damage.

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Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations

Cited by 73 publications

References 2 publications

Advanced monitoring of machining operations

Advanced monitoring of machining operations

State-of-the-art methods and results in tool condition monitoring: a review

Development of a Machine Tool Diagnostic System Using Micro-Electromechanical System Sensors: A Case Study

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