Fault diagnosis studies of face milling cutter using machine learning approach

Madhusudana, C K; Budati, S; Gangadhar, N.; Kumar, Hemantha; Narendranath, S.

doi:10.1177/0263092316644090

Cited by 34 publications

(18 citation statements)

References 26 publications

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“…The specific explanation for this can be found in literature. [14][15][16] The nature of LMD is to demodulate the AM-FM signals. By using the LMD method, a complicated signal can be decomposed into a set of PFs, each of which is the product of an envelope signal and a pure FM signal.…”

Section: Features Extractionmentioning

confidence: 99%

Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

Dong

Luo

Zhong

et al. 2017

Journal of Low Frequency Noise, Vibration and Active Control

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Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time-frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

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Section: Features Extractionmentioning

confidence: 99%

Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

Dong

Luo

Zhong

et al. 2017

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

show abstract

“…In the field of the milling operations, only few authors worked on artificial intelligence developments and tools [15,29]. Most of the works involved Artificial Neuronal Networks (ANN) and related approaches (e.g.…”

Section: Introductionmentioning

confidence: 99%

Milling diagnosis using artificial intelligence approaches

Knittel

Makich

Nouari

2019

Mechanics & Industry

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The Industry 4.0 framework needs new intelligent approaches. Thus, the manufacturing industries more and more pay close attention to artificial intelligence (AI). For example, smart monitoring and diagnosis, real time evaluation and optimization of the whole production and raw materials management can be improved by using machine learning and big data tools. An accurate milling process implies a high quality of the obtained material surface (roughness, flatness). With the involvement of AI-based algorithms, milling process is expected to be more accurate during complex operations. In this work, a milling diagnosis using AI approaches has been developed for composite sandwich structures based on honeycomb core. The use of such material has grown considerably in recent years, especially in the aeronautic, aerospace, sporting and automotive industries. But the precise milling of such material presents many difficulties. The objective of this work is to develop a data-driven industrial surface quality diagnosis for the milling of honeycomb material, by using supervised machine learning methods. In this approach cutting forces are online measured in order to predict the resulting surface flatness. The developed diagnosis tool can also be applied to the milling of other materials (metal, polymer, etc.).

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“…TCM was applied to various machining operations such as drilling, milling and turning. Madhusudana et al [6,7] experimented to diagnose milling tool condition through vibration signals. Histogram features were extracted from vibration signals pertaining to healthy and faulty conditions of the tool.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

2019

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In various machining operations, the tool condition monitoring (TCM) is highly necessary to avoid uncertain downtime in production. TCM provides continuously the condition of cutting tool by noticing various parameters such as temperature, acoustic emission and vibration. One of the best ways to monitor the condition of cutting tools for unmanned machining is by observing tool vibration signature. In the present work, vibration signals are acquired from the cutting tool. One healthy state and three faulty conditions of tools are considered for the study. The faulty tools considered in the current study are worn flank, broken tool and extended overhang. The vibration signals of these faulty tool conditions are used to train the machine learning algorithm. Statistical features are extracted from the vibration signal to feed as input to the J48 decision tree. The classifier algorithm used in the current study is rotation forest algorithm. The algorithm uses only significant features which are selected from a decision tree. The algorithm is validated with test dataset to recognize the faulty or healthy state of the tool. It was found that the algorithm could classify the tool condition with 95.00% classification accuracy.

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Fault diagnosis studies of face milling cutter using machine learning approach

Cited by 34 publications

References 26 publications

Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

Milling diagnosis using artificial intelligence approaches

Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

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