Machining Error Control Research Articles

Quasistatic deflection analysis of slender ball-end milling cutter

In this study, a quasistatic force-deflection interaction model considering cutting and elastic force was proposed to analyze and compensate for the quasistatic deflection of slender ball-end milling cutter. The theoretical waveforms and peaks of cutting force considering the force-deflection interactions were consistent with the experimental results. The deflection of slender ball-end milling cutter significantly reduces the cutting force, and the Pearson's correlation coefficient between the difference in the peak cutting force and maximum deflection in radial is larger than that of in feed directions. An analysis of the machining error of a cylindrical surface revealed that the deflection of slender ball-end milling cutter had a considerable effect on the machining error, and the compensation of slender cutter deflection effectively reduced the amplitude and fluctuation of the machining error. This work can provide a useful guidance for machining error control in the ball-end milling process of mold surfaces with deep cavities.

Research on Machining Error Control Method Driven by Digital-twin Model of Dynamic Characteristics of Machining System

The position of each component structure in the machining space of the machining system directly affects the machining quality of the workpiece. In this paper, RBF neural network is used to study the spatial dynamic characteristics of the translational axes of five-axis CNC machine tools. It is the basis for building an evolvable knowledge base. Process evaluation, information feedback, and iterative optimization of thin-wall parts were carried out using digital-twin technology. Firstly, the model simulation of the translational machining space of a three-dimensional five-axis CNC machine tool is carried out by using the finite element method. The RBF neural network predicts the natural frequency of a translational machining space dynamic characteristics. It is further used to construct the dynamic characteristic spectrum of the translational space of CNC machine tools. Secondly, the machine tool flush space cutting process is built with a digital-twin system to optimize the iterative mechanism for machining process optimization. Finally, the method’s effectiveness was verified by experiments on milling thin-walled parts on a dual-turntable five-axis CNC machine and by contouring error measurement experiments. The results show that the average error of the optimized thin-wall contour is reduced by 26.9%. The iterative mechanism can continuously optimize the machining error and improve the machining accuracy. The iterative mechanism can continuously optimize the machining error and improve the machining accuracy.

Construction Method of Digital Twin System for Thin-Walled Workpiece Machining Error Control Based on Analysis of Machine Tool Dynamic Characteristics

In the intelligent optimization process of aerospace thin-walled parts, there are issues such as solidification of core knowledge base, high system coupling degree, and real-time evaluation and optimization feedback required for the knowledge base. These problems make it difficult to expand the functions of the digital twin system and meet the growing processing needs, ultimately hindering the application of digital twin technology. To address these issues, a digital twin system for controlling processing errors in thin-walled parts was built using a microservices architecture. In addition, a method for building a digital twin system at the processing unit level with the best coupling degree was proposed, mainly targeting the dynamic characteristics analysis knowledge base of thin-walled parts. Furthermore, to meet the requirements for backward compatibility of the processing unit level digital twin system, a comprehensive solution including the construction, operation, evaluation, optimization, and visualization of a knowledge base for the dynamic characteristics of the processing unit was proposed, providing guidance for the digital transformation and upgrading of CNC machine tools and the optimization of processing technology based on digital twin technology.

A Method for Predicting the Boring Topography Error of Shaft Hole on a Thin-Walled Box Based on a Proposed Dynamic Model

Traditional boring dynamic model ignores the effect of the uneven radial stiffness of the shaft hole of the thin-walled box on the boring vibration characteristic, which will lead to the deviation in the prediction for both the cutting force and the surface topography of the machining hole. To address this shortcoming, a new calculation model of the equivalent radial stiffness of the shaft hole is proposed and then validated by the finite element (FE) method. On this basis, a boring dynamic model of the shaft hole is developed, and an experimental study on the dynamic characteristics of the shaft hole boring is carried out to verify the correctness and effectiveness of the proposed dynamic model. Moreover, a prediction method for the surface topography error of the machined shaft hole is presented based on the proposed dynamic model. Finally, the comparison investigation on the hole machined surface topography predicted by the proposed method and measured in practice is carried out. The results show that the proposed theoretical models and method can achieve accurate predictions for both the boring cutting force and the machined surface topography error of the shaft hole on the thin-walled box. This study can provide a theoretical basis for machining error control and process optimization of the fine boring of the thin-walled transmission box.

Machining errors control method for near-net-shape jet engine blade CNC machining process

This study proposes a method based on on-machine measurement to control the machining error in the blade CNC machining process. Firstly, the mechanism of machining error formation of the near-net-shaped jet engine blade are analyzed, and the composition of the machining error source and its impact are qualitatively analyzed. Secondly, the adaptive CNC machining process are introduced. Thirdly, the influence of adaptive CNC machining process on the final machining errors is analyzed. The results show that the adaptive CNC machining process is of the excellent ability of machining errors control, and this method is effective in machining error control.

Investigation of Multivariate Profile Monitoring on Complex Thin-walled Components Batch Machined using a Sliding Time Window Cluster Method

ABSTRACT Aiming at solving the problems of low precision of quality monitoring and low sensitivity of fluctuation detection in the batch machining process of complex thin-walled parts, the multivariate profile monitoring approach was proposed. The extended error stream method was used to establish profile representation of the multi-operation machining processing fluctuation, which could describe the fluctuation of multi-part production cycle and the decline of production line represented by error sources. On this basis, the monitoring statistics of the batch complex thin-wall part machining process were built based on one-step ahead forecast error and Tsquare (OSFE-T square). Then, the profile monitoring method was developed based on the OSFE-T square monitoring model of batch parts on the different operation using the sliding time window cluster-based method. Through the Monte-Carlo simulation, the fraction correctly classified, sensitivity, specificity, false positive and false negative of the quality monitoring method were analyzed respectively, which proved that the proposed method had better performance. Finally, amachining simulation instance was presented including ten operations demonstrated the effectiveness of the machining error control method. Compared with other alternative methods, the proposed method in this paper has more advantages in the sensitivity of fluctuation detection.

A method to predict the cutting force for end mills with variable-pitch and variable-helix angle

Cutting force is the most fundamental, and essential for machining error control, chatter stability analysis and milling process planning. In this paper, a method to predict the cutting force for the milling with variable-pitch or variable-helix cutter is provided. The method can take into account the piecewise continuous regions of the cutting that is attributed to the helix angle and the pitch variations between two subsequent teeth along the length of the axis. The piecewise character related to cutting force is deeply analysed and the effective depth of cut which seriously influences the maximum cutting force is discussed as well as its expression. Finally, a series of cutting tests and simulations are carried out to validate the present method.

Research on the Machining Error Control and Simulation of the Motor Frame in Vertical Lathe Machining

It's commonly existed in electric machine industry that the coaxiality of processing the rabbets of two ends of the motor frame is hard to guarantee, which is studied in this paper. An analysis of the causes of the coaxiality error of the rabbets of two ends of the motor frame in vertical lathe machining is made and strategies are proposed to improve the machining precision of the motor frame. Based on mode analysis of the motor frame in vertical lathe machining by ANSYS finite element software, the reasonable clamping method is determined and new process plan and technological equipment are designed for machining of the motor frame. The research would demonstrate great significance in improving the performance of the motor, prolonging the service life and reducing the production cost.

Spline-constrained tool-path planning in five-axis flank machining of ruled surfaces

Previous optimization-driven tool-path planning methods provide an effective approach to machining error control in five-axis flank milling of ruled surfaces. However, the finished part typically exhibits poor surface roughness. This paper proposes a new planning method for overcoming this problem. The proposed method generates a spline-curve-constrained tool path that produces minimized geometrical deviations on the machined surface, maintains satisfactory surface quality, and reduces the dimensionality in the solution space. A particle swarm optimization scheme was developed to determine the coefficients of the curve equations defining the constrained tool motion. A machining experiment was conducted and the measurement results validated the proposed method.

The machining error control of blade shape based on multivariate statistical process control

Due to the high demand on blade shape quality and accuracy, it is essential to control the quality fluctuation in blade machining process. Since there are multiple correlative quality characteristics on blade shapes, multivariate statistical process control is adopted to monitor blade quality fluctuation. Meanwhile, blades, which are designed to satisfy better aerodynamics, always have complex and special shape, and the high dimensionality problem hinders the implementation of multivariate control charts. In order to deal with this problem, blade shape profiles are first divided into several segments according to the geometrical shape and the variable tolerance requirements of blade section curves, which are monitored simultaneously. Then, the quality control vector can be built, which comprises profile error and bending–torsion deformation. Because of the high dimensionality of data points on blade section curves, the equal error discretization method is used to deal with the error samples in a computationally lightweight manner. Due to the requirement for high machining accuracy, these proposed statistics that form the quality control vector can be monitored by a small shift multivariate control chart such as the multivariate exponentially weighted moving average control chart. The mean distance difference vectors for each segment, which are designed into the multivariate exponentially weighted moving average control chart, can not only detect shifts but also provide diagnostic information when a blade shape profile is out of control. Finally, the validity of the proposed method is proved by a simulation study applied to a blade machining process.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control (MSPC) and stream of variations (SoV) is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error (OSFE) is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

Corrective capability analysis and machining error control in ion beam figuring of high-precision optical mirrors

In deterministic ion beam figuring (IBF) technology, the application of small ion beam enhances the corrective capability for mid-to-high spatial frequency errors on the optical surface, which directly determines the surface accuracy of the figuring process. But when the diameter of the ion beam becomes smaller, the machining errors will have a stronger influence on the final figuring result, so these errors must be controlled through corresponding methods. We investigate the corrective principle in IBF for surface errors of different spatial frequencies and establish the selection criterion for removal function in different figuring stages to realize the rapid convergence of surface accuracy. Then, through analyzing and controlling the machining errors in the figuring process, high-precision mirrors can be rapidly obtained. Finally, experiments on fused silica planar and spherical samples are conducted on our self-developed IBF system, and their final surface accuracy are both smaller than 1.1 nm RMS (root-mean-square) and 12.0 nm PV (Peak-to-Valley) after several iterations within 20 min.

Ion beam machining error control and correction for small scale optics

Ion beam figuring (IBF) technology for small scale optical components is discussed. Since the small removal function can be obtained in IBF, it makes computer-controlled optical surfacing technology possible to machine precision centimeter- or millimeter-scale optical components deterministically. Using a small ion beam to machine small optical components, there are some key problems, such as small ion beam positioning on the optical surface, material removal rate, ion beam scanning pitch control on the optical surface, and so on, that must be seriously considered. The main reasons for the problems are that it is more sensitive to the above problems than a big ion beam because of its small beam diameter and lower material ratio. In this paper, we discuss these problems and their influences in machining small optical components in detail. Based on the identification-compensation principle, an iterative machining compensation method is deduced for correcting the positioning error of an ion beam with the material removal rate estimated by a selected optimal scanning pitch. Experiments on ϕ10 mm Zerodur planar and spherical samples are made, and the final surface errors are both smaller than λ/100 measured by a Zygo GPI interferometer.

Particle swarm optimisation (PSO)-based tool path planning for 5-axis flank milling accelerated by graphics processing unit (GPU)

Multi-axis machining offers higher machining efficiency and superior shaping capability compared to 3-axis machining. Machining error control is a critical issue in 5-axis flank milling of complex geometries and there is still a lack of solutions. Previous studies have shown that optimisation-based tool path planning is a feasible approach to reduction of machining error. However, the error estimation is time-consuming in the optimisation process, thus limiting the practicality of this approach. In this work, we apply graphics processing unit (GPU) computing technology to solve this problem. A particle swarm optimisation (PSO)-based optimisation scheme is developed to generate a series of cutter locations (CLs) that produce a minimised error on the machined surface. The error amount induced by each CL is simultaneously calculated by the parallel processing units of GPU. The PSO search process driven by the aggregated result is effectively accelerated. Test results show that our approach outperforms previous optimisation methods in both solution quality and computation efficiency. This work demonstrates a novel application of GPU on Computer Aided Manufacturing/Computer Numerical Control.

Efficient tool path planning for 5-axis flank milling of ruled surfaces using ant colony system algorithms

Five-axis CNC flank milling has recently received much attention in industry. The flank milling operation is efficient in shaping ruled geometry, but introduces a challenging task in machining error control. Previous work proposed a dynamic-programming based scheme for generating optimal tool path by minimising the machining error. However, to compute the tool path takes a considerable amount of time. This paper presents a new scheme using meta-heuristics, ant colony system (ACS), for tool path planning in 5-axis flank milling, with a focus on improving the computation efficiency. The path planning problem is first formulated as mapping two boundary curves of a ruled surface. An ACS-based optimisation algorithm is then applied to search for the mapping that minimises the machining error. The solution is nearly as good as using dynamic programming but takes only half of the computational time. The results from machining experiment and 3D measurement validate the effectiveness of the proposed scheme.

Optimized tool path generation based on dynamic programming for five-axis flank milling of rule surface

This paper presents a computation scheme that generates optimized tool path for five-axis flank milling of ruled surface. Tool path planning is transformed into a matching problem between two point sets in 3D space, sampled from the boundary curves of the machined surface. Each connection in the matching corresponds to a possible tool position. Dynamic programming techniques are applied to obtain the optimal combination of tool positions with the objective function as machining error. The error estimation considers both the deviation induced by the cutter at discrete positions and the one between them. The path planning problem is thus solved in a systematic manner by formulizing it as a mathematical programming task. In addition, the scheme incorporates several optimization parameters that allow generating new patterns of tool motion. Implementation results obtained from simulation and experiment indicate that our method produces better machining quality. This work provides a concise but effective approach for machining error control in five-axis flank milling.