A novel feedrate scheduling method based on Sigmoid function with chord error and kinematic constraints

Li, Hexiong; Jiang, Xin; Huo, Guanying; Su, Chun-Yi; Wang, Bolun; Hu, Yujin; Zheng, Zhiming

doi:10.1007/s00170-021-08092-1

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…Third, the activation function of the network is differentiable, usually a Sigmoid function or a linear function [ 14 ]. The Sigmoid function is divided into the Log-Sigmoid function and Tan-Sigmoid function [ 15 ]. The expressions and strings of each function are as follows.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Under the background of the rapid development of the new generation of information technology, artificial neural networks (ANNs) have made some progress in the evaluation research of various courses in colleges and universities. However, there is little research on the application of ANNs in Ideological and Political Education (IPE) courses. Based on this, this work attempts to introduce the Backpropagation Neural Network (BPNN) into the evaluation system of IPE courses. Firstly, the structure and characteristics of the BPNN are given, and it is optimized by the genetic algorithm based on its characteristics. Secondly, the theoretical framework of IPE course evaluation is established, and the corresponding evaluation model is constructed using the optimized BPNN. Finally, a questionnaire survey is designed to analyze the current situation of IPE evaluation in colleges and universities, and a simulation experiment is set up to test the BPNN evaluation model before and after optimization. The results are as follows. First, there are mainly five evaluation methods for IPE courses in Chinese colleges and universities: 6/4, 3/7, 2/2/6, 5/5, and 3/3/4. Second, the training error value of the BPNN model is in the interval (−2.3, 2.2), and when the number of cycles is 552, the error is infinitely close to zero. Thirdly, the training error value of the optimized BPNN model is in the interval (−0.22, 1.2), and when the number of cycles is 775, the error is infinitely close to zero. Fourthly, the error between the output value of the optimized BPNN model and the expert score value is generally smaller than the error value before optimization. This work aims to provide a theoretical reference for the further application of neural network technology in the evaluation of IPE.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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“…To couple with this problem, some works such as Taylor 's expansion method (Yang & Kong, 1994), predictor-corrector interpolator (PCI) algorithm (Mi-Ching et al, 2003), quintic spline interpolation method (Erkorkmaz & Altintas, 2005) method (Lei et al, 2007) were presented. When the interpolation period is constant, the interpolation step lengths, which are utilized to compute the interpolation points, are determined by the feed rate, so the feed rate scheduling has been one of the most critical processes in NURBS interpolation (H. Li et al, 2022). Though a higher feed rate value can reduce machining time, when the limitation is exceeded, significant geometric deviations such as contour error (Schmitz et al, 2008) and chord error (Yeh & Hsu, 2002) are unavoidable, which seriously worsen the machining quality.…”

Section: Introductionmentioning

confidence: 99%

A novel feed rate scheduling method with acc-jerk-continuity and round-off error elimination for non-uniform rational B-spline interpolation

Jiang

Huo

et al. 2023

Journal of Computational Design and Engineering

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Feed rate scheduling is a critical step in computer numerical control (CNC) machining, as it has a close relationship with machining time and surface quality. It has now become a hot issue in both industry and academia. In this article, we present a novel and complete S-shape-based feed rate scheduling method for three-axis NURBS tool paths, which can reduce high chord errors and round-off errors, and generate continuous velocity, acceleration, and jerk profile. The proposed feed rate scheduling method consists of three modules: a bidirectional scanning module, a velocity scheduling module, and a round-off error elimination module. The bidirectional scanning module aims to guarantee the continuity of the feed rate at the junctions between successive NURBS blocks, where the chord error, tangential acceleration, and tangential jerk limitations are considered. After the NURBS blocks have been classified into two cases by the previous module, the velocity scheduling module first calculates the actual maximum feed rate. It then generates the feed rate profiles of all NURBS blocks according to the proposed velocity profile. Later, the round-off error elimination module is applied to adjust the actual maximum feed rate so that the total interpolation time becomes an integer multiple of the interpolation period, which leads to the elimination of round-off errors. Finally, benchmarks are conducted to verify the applicability of the proposed method. Compared with the traditional method, the proposed method can save the interpolation time by $4.67\% $ to $14.26\% $.

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“…The scheduled time-optimal feedrate profile may vary frequently when the tool path is relatively complex, which can result in significant machining vibration [23][24][25]. However, it is challenging and time-consuming to change and verify the considered constraints at every discrete position along the tool path, which is unnecessary.…”

Section: Introductionmentioning

confidence: 99%

Reducing the contour error of leading and trailing edge through feedrate scheduling in 5-axis machining of blisk

Wang,

Lin,

Shan

et al. 2024

Preprint

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The blisks are complex thin-walled parts with specific structures that have narrow channels and a large degree of bowed-twisted blades. These parts are typically machined using 5-axis machining. However, conventional feedrate scheduling of the tool tip can cause flutter and reduced machining accuracy when dealing with very small radii of curvature and dramatic changes in the tool axis vector. This is because there is a significant difference in moving speed between the tool tip and cutting contact points. To address this issue, we establish an optimization model for feedrate under constraints such as process-allowed cutting speed and machine tool drive along the tool path. For sections of short tool paths in the regions of leading and trailing edges with drastic changes in curvature and tool orientations, we schedule a constant feedrate for the tool contact point. For other sections of the tool path, we construct time-optimal acceleration and deceleration velocity curves using parallel computing technology, ensuring no abrupt changes in acceleration or acceleration velocity at boundary points. Additionally, we smooth the feedrate profile curves for the entire toolpath using the parallel bidirectional scanning method. This approach improves both the efficiency of feedrate scheduling and cutting stability within the regions corresponding to the leading and trailing edges of blisks.

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A novel feedrate scheduling method based on Sigmoid function with chord error and kinematic constraints

Cited by 17 publications

References 27 publications

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Evaluation of Ideological and Political Education under Deep Learning Neural Network

A novel feed rate scheduling method with acc-jerk-continuity and round-off error elimination for non-uniform rational B-spline interpolation

Reducing the contour error of leading and trailing edge through feedrate scheduling in 5-axis machining of blisk

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