Five-axis iso-error numerical control tool path generation for flat-end tool machining sculptured surface

Liu, Wei; Fan, Lv-Yang; Zhu, Quanxin; Zhu, Shumei; Wang, Tian‐Li; Tian, Feng

doi:10.1007/s00170-022-08711-5

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…To determine interference occurrences for every tool orientation in regards to the CC point, a tool's posture, diameter and effective length must be determined. For this paper, flat-end feeds were used for computations because they are used in general roughing processes [24].…”

Section: Collision Avoidancementioning

confidence: 99%

Improved Five Axis–Rough Milling Process Efficacy: a Close-Bounded Volume (CBV) Evaluation Method Based on a Faceted Model

Sutrisno¹,

Kiswanto

Istiyanto³

2023

Preprint

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Roughing processes create workpieces that are similar to actual forms in short periods of time. However, the current method, which involves either three-axis or five-axis machines, is limited in regards to workpiece feed accessibility. Often, significant amounts of material are left on workpieces that can be removed. This paper presents a solution to this issue. By conducting a Close Bouded Volume (CBV) evaluation of an area for which a roughing process can be used, a tool path can be formed with a consideration of tool-path length. It can be done by using a point cloud as a cutter contact (CC) point. Tool orientations can be calculated based on a vector operation from a CC point in a CBV area to the nearest and highest CC point outside the CBV area. By orienting each tool on a formed CC point, interference detection can be used to avoid collisions and gouges that occur between feeds and workpieces on the tool path. Tool interference avoidance can be applied using rotation and translation methods until tools are free from interference. Then, from a CC point that is already free of interference, the avoidance can be tested using an actual rough machining process with a five-axis milling machine. According to this study's results, volume increases can be obtained in roughing so residual materials on workpieces can be reduced. Thus, this method improves disposal processes for residual materials. Moreover, it increases the efficacy of roughing processes.

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Section: Collision Avoidancementioning

confidence: 99%

Improved Five Axis–Rough Milling Process Efficacy: a Close-Bounded Volume (CBV) Evaluation Method Based on a Faceted Model

Sutrisno¹,

Kiswanto

Istiyanto³

2023

Preprint

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“…Wang 7 introduced the DICT (Double Interpolation algorithm based on the Cosine Theorem) method, which uses the STE (Second-order Taylor’s Expansion) method twice within one interpolation cycle, achieving minimal feed rate fluctuations within a stable consumption time. Liu 8 proposed a method that uses a flat-bottom cutter to substitute the cutter envelope surface with a discrete bottom circle, iterating step errors in calculations. Chen 9 modeled the nonlinear error of the tool center point, demonstrating that, within the same interpolation cycle, the tool center point lies on the same plane, thereby converting the three-dimensional problem into a two-dimensional one for research.…”

Section: Introductionmentioning

confidence: 99%

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

Chen,

Xu,

Huang

et al. 2024

Sci Rep

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In current five-axis computer numerical control (CNC) machining, the use of minute linear path segments as an approximation for the ideal cutter contacting (CC) point trajectory is still prevalent. However, introducing rotation axes leads to a deviation of the actual CC point trajectory from the ideal, resulting in nonlinear errors. An integrated method is proposed in this paper for compensating and correcting both the contour error, associated with the approximation of the part surface by the ideal CC point trajectory and the nonlinear error of the CC point trajectory based on the information in the CC point data. By analyzing the spatial relationship between the tool posture and the CC point path during the five-axis linear interpolation process, two adjacent machining tool positions containing CC point data information are selected as the starting and ending points of the five-axis linear interpolation machining. The ideal tool center point and the actual CC point are calculated during the interpolation process, as well as the distance and the unit vector in the perpendicular direction between the actual CC point and the ideal CC point trajectory segment. In the comprehensive error compensation and correction phase, the obtained unit vectors are used as direction vectors for error compensation, and the tool center point during interpolation is first compensated and corrected. This ensures the actual CC point and the contour curve are on the same plane. The compensation direction for contour error is calculated using the start/end tool axis vectors and the ideal CC point trajectory vectors. The size of the contour error approximating the contour curve is calculated through the chord error. A second compensation and correction are applied to the tool center point for interpolation, ultimately achieving comprehensive compensation and correction of nonlinear errors. The data calculations were conducted in the MATLAB environment using actual machining data. After compensation and correction, the contour error was reduced by 76%, the nonlinear error of the CC point trajectory decreased to below 0.88 μm, and the comprehensive nonlinear error of the CC point trajectory was reduced from 19 to 1.5 μm, a reduction of 93%. This demonstrates significant practical value in enhancing the accuracy of five-axis CNC machining. Through actual machining verification, after using the method described in this paper, the average surface roughness decreased from 1.133 to 0.220 μm, and the maximum surface roughness decreased from 6.667 to 1.240 μm. This significantly demonstrates that the compensation and correction method proposed in this paper can significantly improve the surface quality of machined parts.

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“…Min 2 calculated the slope of the curve and the connecting line between two adjacent CC points, and the step error value is calculated by the distance formula at the point with the same slope. Our team 3 uses discrete bottom circles of flat-end tool instead of tool envelope surface to calculate step error iteratively.…”

Section: Introductionmentioning

confidence: 99%

A numerical control machining tool path step error prediction method based on BP neural network

Zhang,

Liu,

et al. 2023

Sci Rep

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Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks can be calculated on GPUs and cloud platforms, which is conducive to reducing computation time and improving accuracy through continuous learning. This article innovatively introduces a BP neural network model to predict step error values. Firstly, the core parameters required for step error calculation are taken as the data samples to construct the neural network model, and map to the same scale through Z-score normalization to eliminate the adverse effects of singular parameters on the calculation results. Then, considering only a small number of parameters determine theoretical values of step error, the Dropout technique can drop hidden layer neurons with a certain probability, which is helpful to avoid overfitting and used in the neural network model design. In the neural network model training, this paper adds the Stochastic Gradient Descent with Momentum (SGDM) optimizer to the back propagation of network training in order to improves the network’ stability and accuracy. The proposed neural network predicts step error of samples from three surface models, the results show that the prediction error decreases as sample training increases. After trained by 15% of the surface samples, the neural network predicts the step errors of the remaining samples. Compared with theoretical values, more than 99% of the predicted values have an absolute error less than 1 μm. Moreover, the cost time is only one-third of the geometric method, which verifies the effectiveness and efficiency of our method.

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Five-axis iso-error numerical control tool path generation for flat-end tool machining sculptured surface

Cited by 7 publications

References 21 publications

Improved Five Axis–Rough Milling Process Efficacy: a Close-Bounded Volume (CBV) Evaluation Method Based on a Faceted Model

Improved Five Axis–Rough Milling Process Efficacy: a Close-Bounded Volume (CBV) Evaluation Method Based on a Faceted Model

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

A numerical control machining tool path step error prediction method based on BP neural network

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