Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance

Chan, Tzu-Chi; Lin, Hsin-Hsien; Reddy, Sabbella Veera Venkata Satyanarayan

doi:10.1007/s00170-021-08634-7

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…In one study, a simulation model is constructed by taking into account the tool run-out error to determine the machining conditions (Denkena et al, 2021). Machining conditions are also determined based on the stiffness of the machine tool (Chan et al, 2022). Moreover, it is confirmed that tool paths affect the surface roughness of the machined surface and that surface roughness can be reduced by changing the tool tilt angle (Stejskal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Proposal of an evaluation method of variation in tool center points for ball-end milling of free-form surfaces

SOGABE,

NAKAMOTO

2024

JAMDSM

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Though high surface quality is expected in die and mold machining, machined surfaces often need to be polished to improve their quality after machining. One of the main reasons for the deterioration of the machined surface is the tool paths generated for ball-end milling. Variation in tool center points in the feed direction may occur even in tool paths that are generated using a commercial computer-aided manufacturing (CAM) system. The variation often causes a lack of uniformity on the machined surface. However, it is difficult for unskilled operators to estimate the machined surface prior to machining. Therefore, there is a need for a method for evaluating variation in tool center points that does not depend on the skills of operators. In this study, to achieve high surface quality, a novel method for evaluating variation in tool center points is devised for the free-form surfaces of dies and mold. An appearance indicator is introduced to quantify the degree of non-uniformity of the machined surface and derive an evaluation index. The effectiveness of the proposed method is verified by conducting machining experiments using different tool paths. The tool radius and pick feed are changed to confirm correspondence between the obtained surface quality and the derived evaluation index. The machining experiments successfully demonstrate that the proposed evaluation method can be used to improve surface quality.

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Section: Introductionmentioning

confidence: 99%

Proposal of an evaluation method of variation in tool center points for ball-end milling of free-form surfaces

SOGABE,

NAKAMOTO

2024

JAMDSM

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show abstract

“…[7] found that the effect of a lower-order frequency is hardly experienced by a milling machine even at lower cutting depths, as the machine governs stability, resulting in a completely different behavior. To verify the influence of natural frequencies and structural characteristics on the processing quality, [8,9] implemented FEA to extract the static and dynamic features of the machine. An AIM polynomial was used to establish a prediction model for the quality processing of a five-axis machine.…”

Section: Introductionmentioning

confidence: 99%

Effect of spatial moving structure and topology optimization of the CNC turning machine tools

Chan,

Reddy,

Ullah

et al. 2023

Int J Adv Manuf Technol

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In order to enhance the accuracy and productivity of CNC machine tools, Finite Element Method (FEM) is utilized for analyzing machining operations and machine tool structures. Through this approach, it becomes possible to assess and reduce machining errors, thereby improving the precision of machined parts. Additionally, by subjecting machine tool components to analysis of stress and deformations under real operating conditions, the overall performance and lifespan of the machine tool structures can be augmented. This study provides a comprehensive review of recent published papers focusing on the implementation of finite element methods for the analysis and optimization of CNC machine tool operations and structures. In this study, a virtual model of the Goodway CNC turning machine was used to study the machine's behavior. The first three vibrational modes analyzed through a modal analysis were 108.2Hz, 133.4Hz, and 191.7Hz, which were experimentally validated using modal tests. The design precision was supported using the harmonic response. For an applied external load of 500N on the head chuck, the tool turret's transient response was established through transient analysis at 0.4s. However, after the static analysis, the maximum deformation of the machine at the same external applied load on the tool turret was upheld at 7.6µm. The spatial position analysis rectified the modal performance of the machine at a variety of set working positions. As the principal cause of rigidity originates from the base of the machine, three sets of ground support combinations were claimed to test the machine's rigidity. After the topology optimization analysis was achieved, a better-optimized version of the design is suggested. The old and new models were compared, and an increase of 1.5% in the first three modal frequencies was observed.

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“…In the field of predicting surface roughness, domestic and overseas scholars have done a great deal of relevant research. Chan et al [3] planned the machining experiments, the measurement records were used as training data for the AIM polynomial neural network to build a surface roughness prediction model. The prediction model could input processing parameters to achieve the surface roughness prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

Qin

et al. 2023

Int J Adv Manuf Technol

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For the problem of difficulty in measuring the surface roughness of large-pitch internal threads, predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in scope of use. In this paper, the homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and RBF neural network, which are suitable for researches with small sample sizes, from the machine learning algorithm were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. The SVM model was verified to have absolute errors within 0.05μm and relative errors within 4% for turning both left and right threaded surface, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness are difficult to measure due to structural characteristics.

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Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance

Cited by 12 publications

References 24 publications

Proposal of an evaluation method of variation in tool center points for ball-end milling of free-form surfaces

Proposal of an evaluation method of variation in tool center points for ball-end milling of free-form surfaces

Effect of spatial moving structure and topology optimization of the CNC turning machine tools

Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

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