Neural network supported inverse parameter identification for stability predictions in milling

Postel, Martin; Bugdayci, Bircan; Kuster, Friedrich; Wegener, Konrad

doi:10.1016/j.cirpj.2020.02.004

Cited by 21 publications

(8 citation statements)

References 25 publications

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“…Each node in the hidden and output layer represents a specific output function, which is called excitation function f ; Each two nodes in the adjacent layers are connected through a certain weight w to transfer signal; In addition, the deviation b should be considered. 42 Therefore, the output vector Z ( t ) of the t th layer can be expressed as shown in equation (2):…”

Section: Proposed Methodologymentioning

confidence: 99%

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Zhang

Liu

Huang

et al. 2021

Advances in Mechanical Engineering

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Dynamic parameters of joints are indispensable factors affecting performance of machine tools. In order to obtain the stiffness and damping of sliding joints between the working platform and the machine tool body of the surface grinder, a new method of dynamic parameters identification is proposed that based on deep neural network (DNN) modeling. Firstly, the DNN model of dynamic parameters for working platform-machine tool body sliding joints is established by taking the stiffness and damping parameters as the input and the natural frequencies as the output. Secondly, the number of hidden layers in DNN topology is optimally selected in order to the optimal training results. Thirdly, combining the predicted results by DNN model with experimental results by modal test, the stiffness and damping are identified via cuckoo search algorithm. Finally, the relative error between the predicted and experimental results is less than 2.2%, which achieves extremely high prediction precision; and thereby indicates the feasibility and effectiveness of the proposed method.

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Section: Proposed Methodologymentioning

confidence: 99%

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Zhang

Liu

Huang

et al. 2021

Advances in Mechanical Engineering

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“…In the first approach [43] with the framework shown in Fig. 5, neural networks are implemented upstream of the stability solution to identify unknown relationships between easily measurable and unknown, hard-to-measure parameters.…”

Section: Neural-network Supported Inverse Parameter Identificationmentioning

confidence: 99%

“…Fig.5. Framework for neural network supported parameter identification and stability predictions[43] …”

mentioning

confidence: 99%

Operator Integrated – Concept for Manufacturing Intelligence

Wegener¹,

Weikert²,

Mayr³

et al. 2021

Journal of Machine Engineering

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Increasing autonomy and sustainability are major goals in manufacturing. Main technological trends provide enablers for achieving these goals and need to be implemented and combined in manufacturing machinery in a suitable manner. The paper exposes a vision of modern manufacturing machines, where the complexity of manufacturing processes is handled within the manufacturing machine and a simplistic front end is presented to the operator, which means that major elements of operators' tasks are fulfilled by the intelligence of the machine. Research vectors paving the ground for this concept from different points of view are then discussed. Research is presented on intelligent grinding, intelligent recognition and suppression of chatter, adaptive thermal and motion error compensation exploting also self-learning abilities. It is necessary to point out, that not only intelligent mastering of process and machine becomes more and more important but communications among machine tools enabling process chain overarching intelligent approaches and creating intelligent factories.

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“…Shao et al [[20]] extract feature from motors' original signals and used deep belief networks (DBN) to achieve automated and intelligent fault diagnosis for induction motors. Fujishima et al [ [21]] proposed a novel compensation method using deep learning algorithm to compensate the thermal deformation in machine tool structure. Postel et al…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cross-Tension Strength of Self-Piercing Riveted Joints Using Finite Element Simulation and XGBoost Algorithm

Lin

Wan

et al. 2021

Chin. J. Mech. Eng.

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Self-piercing riveting (SPR) has been widely used in automobile industry, and the strength prediction of SPR joints always attracts the attention of researchers. In this work, a prediction method of the cross-tension strength of SPR joints was proposed on the basis of finite element (FE) simulation and extreme gradient boosting decision tree (XGBoost) algorithm. An FE model of SPR process was established to simulate the plastic deformations of rivet and substrate materials and verified in terms of cross-sectional dimensions of SPR joints. The residual mechanical field from SPR process simulation was imported into a 2D FE model for the cross-tension testing simulation of SPR joints, and cross-tension strengths from FE simulation show a good consistence with the experiment result. Based on the verified FE model, the mechanical properties and thickness of substrate materials were varied and then used for FE simulation to obtain cross-tension strengths of a number of SPR joints, which were used to train the regression model based on the XGBoost algorithm in order to achieve prediction for cross-tension strength of SPR joints. Results show that the cross-tension strengths of SPR steel/aluminum joints could be successfully predicted by the XGBoost regression model with a respective error less than 7.6% compared to experimental values.

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Neural network supported inverse parameter identification for stability predictions in milling

Cited by 21 publications

References 25 publications

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Operator Integrated – Concept for Manufacturing Intelligence

Prediction of Cross-Tension Strength of Self-Piercing Riveted Joints Using Finite Element Simulation and XGBoost Algorithm

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