Tool failure diagnosis in milling using a neural network

Tarng, Y.S.; Hwang, S.T.; Hseih, Y. W.

doi:10.1006/mssp.1994.1002

Cited by 6 publications

(2 citation statements)

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“…Wu [7] and Elkordy [9] used a neural network for damage diagnosis in composite materials. The back-propagation network is one of the most successful recurrent network paradigms in use today [10,11]. The backpropagation procedure gradually minimizes the squared error between the actual outputs and the desired outputs (Figure 3).…”

Section: Output Layermentioning

confidence: 99%

Detection of Structural Damage in Medium Density Fiberboard Panels using Neural Network Method

Long

Rice

2008

Journal of Composite Materials

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This research assessed the feasibility of using a neural network to detect low levels of damage in small samples of medium density fiberboard (MDF). The neural network was a three-layer back-propagation network. The undamaged stress wave frequency spectrum patterns were trained by the neural network. The trained patterns were then compared to stress waves patterns taken from MDF samples loaded to various percentages of their estimated failure load. In this experiment, if an application load is below the proportional limit, a small change in wave patterns occurs. The neural network has the unique ability to train data to recognize spectral patterns and has been used with success for the detection of structural damage.

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Section: Output Layermentioning

confidence: 99%

Detection of Structural Damage in Medium Density Fiberboard Panels using Neural Network Method

Long

Rice

2008

Journal of Composite Materials

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“…First, the work proposed by Liu et al in [29] using a two-stage improved Elman Neural Network model to perform failure detection on a hydraulic servo system shows the strengths of NNs approaches to overcome strong nonlinearities. Lastly, the work developed by Tarng et al in [30] employed a multi-layer feed-forward neural network with Back-Propagation Algorithm (BPA) to detect abnormal situations in milling processes.…”

Section: Literature Reviewmentioning

confidence: 99%

Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach

Sanchez¹

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A failure detection and identification (FDI) scheme is developed for a small remotely controlled jet aircraft based on the Artificial Immune System (AIS) paradigm. Pilot-in-the-loop flight data are used to develop and test a scheme capable of identifying known and unknown aircraft actuator and sensor failures. Negative selection is used as the main mechanism for self/non-self definition; however, an alternative approach using positive selection to enhance performance is also presented. Tested failures include aileron and stabilator locked at trim and angular rate sensor bias. Hyper-spheres are chosen to represent detectors. Different definitions of distance for the matching rules are applied and their effect on the behavior of hyper-bodies is discussed. All the steps involved in the creation of the scheme are presented including design selections embedded in the different algorithms applied to generate the detectors set. The evaluation of the scheme is performed in terms of detection rate, false alarms, and detection time for normal conditions and upset conditions. The proposed detection scheme achieves good detection performance for all flight conditions considered. This approach proves promising potential to cope with the multidimensional characteristics of integrated/comprehensive detection for aircraft subsystem failures. A preliminary performance comparison between an AIS based FDI scheme and a Neural Network and Floating Threshold based one is presented including groundwork on assessing possible improvements on pilot situational awareness aided by FDI schemes. Initial results favor the AIS approach to FDI due to its rather undemanding adaptation capabilities to new environments. The presence of the FDI scheme suggests benefits for the interaction between the pilot and the upset conditions by improving the accuracy of the identification of each particular failure and decreasing the detection delays. DEDICATION To my family for being always supportive in every decision I made and for helping me become who I am today. iii ACKNOWLEDGEMENTS First and foremost, I would like to thank Prof. Perhinschi and Prof. Napolitano for giving me the opportunity to further extend my college education. Specials thanks go to Prof. Perhinschi for letting me take part of this groundbreaking research giving me the best guidance one can expect from an Advisor. I would also like to thank Dr. Bojan Cukic for eagerly agreeing to form part of my committee and for providing positive feedback towards the successful completion of this work. My parents, Olga and Vicente, deserve a special thank for always letting me choose my own path and giving me an education one cannot learn from books; their generosity is out of imaginable limits. I would also like to express my sincere appreciation to my siblings Javier and Mariana for always believing in me even more than myself. I would also like to extend my gratitude to my friends, now spread throughout the world, for being the siblings one gets to choose to walk side by side through life, I am su...

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Prediction of tool breakage in face milling using support vector machine

Hsueh¹,

Yang²

2007

Int J Adv Manuf Technol

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Tool failure diagnosis in milling using a neural network

Cited by 6 publications

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Detection of Structural Damage in Medium Density Fiberboard Panels using Neural Network Method

Detection of Structural Damage in Medium Density Fiberboard Panels using Neural Network Method

Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach

Prediction of tool breakage in face milling using support vector machine

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