Five-Axis Numerical Control Processing Application and Surface Quality Control of HyperMILL-Based Mobile Phone Mold Cavity
The mold cavity structure of mobile phones is one of the most typical complex surface structures. Specific to this type of product, this paper takes some typical product as an example, to conduct Hyper Mill-based analysis and computation for surface processing quality control problem in numerical control milling on the basis of the analysis of traditional mobile phone cavity processing. The description of five-axis processing and path as well as VERICUT simulation is explained with the final application verification. The result shows that the quality control formula of the processing surface is consistent with the actual situation, and the advantages of five-axis processing technology in the application of such types of products are clarified as well.
Process Analysis and Simulation of Complex Parts
For complex parts, computer-aided machining simulation software can efficiently verify the correctness of the program. This paper deals with a kind of compound parts for turning and milling. Analyze and design their process and processes route. The practical scheme is put forward in view of the processing requirements of structure, stiffness and irregular streamline, then analysis it. At the same time, the technical difficulties encountered in the scheme are analyzed in depth, and verifies its feasibility by simulating it for the proposed solutions. After meeting the various process requirements, the actual machining of the parts is carried out. Verify the results in practice.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.