scholarly journals Sculptured surface-oriented machining error synthesis modeling for five-axis machine tool accuracy design optimization

2016 ◽  
Vol 89 (9-12) ◽  
pp. 3285-3298 ◽  
Author(s):  
Hai Li ◽  
Yingguang Li ◽  
Wenping Mou ◽  
Xiaozhong Hao ◽  
Zhixiang Li ◽  
...  
Keyword(s):  
Machine Tool ◽  
Design Optimization ◽  
Sculptured Surface ◽  
Machining Error ◽  
Machine Tool Accuracy ◽  
Five Axis

Tool Path Generation for Five-Axis Controlled Machining with Consideration of Motion of Two Rotational Axes

2011 ◽  
Vol 5 (3) ◽  
pp. 412-419 ◽  
Author(s):  
Koichi Morishige ◽  
◽  
Makoto Kaneko
Keyword(s):  
Machine Tool ◽  
Configuration Space ◽  
Tool Path ◽  
Tool Path Generation ◽  
Path Generation ◽  
Machining Error ◽  
Finished Surface ◽  
Route Searching ◽  
Five Axis ◽  
Original Configuration

In this paper, the original configuration space is applied to generate tool path, which indicate locations of two rotational axes of a 5-axis controlled machine tool. Moreover, A-star algorithm that is one of the methods for route searching is applied to decide tool postures considering the motion of two rotational axes. The developed method can generate tool path limiting the number of used rotational axes, and reducing the moving amount of two rotational axes. Furthermore, the method can generate tool path that avoids the reverse rotation of two rotational axes, which might cause the machining error. As a result, an excellent finished surface is actually obtained, and the usefulness of the developed method is confirmed.

scholarly journals Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

2018 ◽  
Vol 2 (3) ◽  
pp. 60 ◽  
Author(s):  
Kanglin Xing ◽  
J.R.R. Mayer ◽  
Sofiane Achiche
Keyword(s):  
Principal Component Analysis ◽  
Machine Tool ◽  
Principal Component ◽  
Component Analysis ◽  
Features Extraction ◽  
State Classification ◽  
Volumetric Errors ◽  
Machine Tool Accuracy ◽  
Normal States ◽  
Five Axis

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification.

scholarly journals Special Issue on Machine Accuracy Evaluation

2020 ◽  
Vol 14 (3) ◽  
pp. 359-359
Author(s):  
Soichi Ibaraki ◽  
Andreas Archenti
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Metrological Problem ◽  
Accuracy Evaluation ◽  
3D Measurement ◽  
Special Issue ◽  
Position Measurement ◽  
Machine Tool Accuracy ◽  
Error Calibration ◽  
Five Axis

The accuracy of a three-dimensional (3D) positioning system can ultimately be evaluated via measurement of a 3D vector between command and actual end-effector positions at arbitrary points over the entire workspace. This is a simple, yet challenging, metrological problem. The motion accuracy of a machine tool is traditionally evaluated on an axis-to-axis basis, with every error motion of every axis being independently measured as part of a one-dimensional measurement process in a different setup. Toward the ultimate goal of 3D position measurement over the entire workspace, research efforts have offered several new, practical measurement technologies. This special issue covers the technical and academic efforts regarding the evaluation of machine tool accuracy. The papers in this special issue clarify the latest research frontiers regarding machine tool accuracy from a metrological viewpoint. In the first paper, by Montavon et al., error calibration technologies and their management are reviewed within the Internet of production concept. Long-term accuracy monitoring and management are clearly among the most crucial technical challenges faced regarding machine tools, and the work by Xing et al. is related to them. Ibaraki et al. presented machining tests to evaluate the thermal distortion of a machine tool. Peukert et al. studied the dynamic interaction between machine tools and their foundations. Various 3D measurement schemes for determining machine error motions have been investigated by many researchers, and some have been implemented in industrial applications. Kenno et al. and Florussen et al. investigated 3D measurement using the R-test for five-axis machines. Miller et al. studied simultaneous measurement of six-degree-of-freedom error motions of a linear axis. Nagao et al. presented an error calibration method for a parallel kinematic machine tool. The editors appreciate the contributions of all the authors, as well as the work of the reviewers. We are confident that this special issue will further encourage research and engineering work for improving the accuracy and performance of machine tools.

scholarly journals Influence of Tool Length and Profile Errors on the Inaccuracy of Cubic-Machining Test Results

2021 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Zongze Li ◽  
Hiroki Ogata ◽  
Ryuta Sato ◽  
Keiichi Shirase ◽  
Shigehiko Sakamoto
Keyword(s):  
Machine Tools ◽  
Test Accuracy ◽  
Test Results ◽  
Machining Error ◽  
Tool Profile ◽  
Machining Test ◽  
The Difference ◽  
Side Error ◽  
Profile Errors ◽  
Five Axis

A cubic-machining test has been proposed to evaluate the geometric errors of rotary axes in five-axis machine tools using a 3 × 3 zone area in the same plane with different tool postures. However, as only the height deviation among the machining zones is detected by evaluating the test results, the machining test results are expected to be affected by some error parameters of tool sides, such as tool length and profile errors, and there is no research investigation on how the tool side error influences the cubic-machining test accuracy. In this study, machining inaccuracies caused by tool length and tool profile errors were investigated. The machining error caused by tool length error was formulated, and an intentional tool length error was introduced in the simulations and actual machining tests. As a result, the formulated and simulated influence of tool length error agreed with the actual machining results. Moreover, it was confirmed that the difference between the simulation result and the actual machining result can be explained by the influence of the tool profile error. This indicates that the accuracy of the cubic-machining test is directly affected by tool side errors.

Sign in / Sign up

Export Citation Format

Share Document