An approach of comprehensive error modeling and accuracy allocation for the improvement of reliability and optimization of cost of a multi-axis NC machine tool

Abstract

Machining accuracy is critical for the quality and performance of a mechanical product, and the reliability of a multi-axis NC machine tool reflects the ability to reach and maintain the required machining accuracy. The objective of this study is to propose a general methodology that will simultaneously consider geometric errors and thermal-induced errors to allocate the geometric accuracy of components, for improving machining accuracy reliability under certain design requirements. The multi-body system (MBS) theory was applied to develop a comprehensive volumetric error model, showing the coupling relationship between the individual errors of the components of this machine tool and their volumetric accuracy. Additionally, a thermal error model was established based on the neural fuzzy control theory and was compared to the common thermal error modeling method called BP neural network. Based on the traditional cost model and the reliability analysis model, a geometric error-cost model and a geometric error-reliability model were established, taking the weighted function principle into consideration. Then, an allocation approach of the geometric errors, for optimizing total cost (manufacture and QLF) and reliability, subject to the geometrical and operational constraints of the machine tool, was proposed and formulated into a mathematical model, in order to perform the optimization process of accuracy allocation by using the advanced NSGA-II algorithm. A case study was also performed in a five-axis machine tool, and the traditional NSGA algorithm was used for comparison. The optimization results for the five-axis machining center showed that the proposed approach is effective and able to perform the optimization of geometric accuracy and improve the machining accuracy and the reliability of the machine tool.

Prediction of machining accuracy based on geometric error estimation of tool rotation profile in five-axis multi-layer flank milling process

“…However, most of the existing comprehensive error models [8,[39][40][41][44][45][46] do not consider the inter-layer interference in the study of machining surface accuracy for multi-layer flank milling, and there has no work on calculation and analysis for the possible machining inter-layer interference.…”

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

“…As a high-precision and efficient automation equipment, the failure of the CNC machine tools can cause huge loss, so it is imperative to improve the reliability of optimization design [4]. The performance of CNC machine tools has been improved drastically in recent years.…”

“…It is an important analytical tool to enhance the inherent reliability of products, and the reasonable and effective reliability optimization design has a guiding significance for the later product detailed design [2,3]. At the same time, the product cost is also a determining factor which can decide their market competitiveness, and, during application in practical engineering, the product optimal design scheme with high reliability and minimum cost is often desired by designers and customers [4,5]. The reliability-based and cost-oriented product optimization has been a hot area of research for several years [6,7].…”

Comprehensive analysis of the influence of structural and dynamic parameters on the accuracy of nano-precision positioning stages

“…For example, for multiaxis precision machines, Zhou et al [21], Guo et al [22], Chen et al [23], and Tian et al [24] established mapping relationships between the main geometric errors on each axis and tool positioning accuracy and achieved system error compensations. Zhang et al [25] and Cheng et al [26] achieved optimal allocation of the geometric tolerances of each motion axis in multi-axis precision machines by analyzing the influence of geometric errors in the main structural parts on tool positioning errors. Liu et al [27] established an error model of an H-type ultra-precision positioning stage that considers error factors, such as straightness, thermal deformation, structural deformation, and variations in air bearing gaps to evaluate measurement uncertainty.…”