The localized faults of rolling bearings can be diagnosed by the extraction of the impulsive feature. However, the approximately periodic impulses may be submerged in strong interferences generated by other components and the background noise. To address this issue, this paper explores a new impulsive feature extraction method based on the sparse representation. According to the vibration model of an impulse generated by the bearing fault, a novel impulsive wavelet is constructed, which satisfies the admissibility condition. As a result, this family of model-based impulsive wavelets can form a Parseval frame. With the model-based impulsive wavelet basis and Fourier basis, a convex optimization problem is formulated to extract the repetitive impulses. Based on the splitting idea, an iterative thresholding shrinkage algorithm is proposed to solve this problem, and it has a fast convergence rate. Via the simulated signal and real vibration signals with bearing fault information, the performance of the proposed approach for repetitive impulsive feature extraction is validated and compared with the noted spectral kurtosis method, the optimized spectral kurtosis method based on simulated annealing, and the resonance-based signal decomposition method. The results demonstrate its advantage and superiority in weak repetitive transient feature extraction.

A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

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American Society of Mechanical Engineers (ASME)

This paper presents a new S trajectory kinematic measurement method to evaluate the dynamic accuracy of five-axis machine tools based on R-test measurements. The S trajectory is obtained by scaling the machining path of the S-shaped test piece to the measuring stroke of the R-test. A geometric and dynamic accuracy simulation model is established to analyze the influence of various error factors on the S trajectory test and compare with the conical kinematic test described in ISO10791.6 quantitatively. The simulation results shown that the S trajectory test is more sensitive to most geometric errors and dynamic errors than the conical test. Particularly, to reflect the dynamic error factors such as poor rigidity of machine tools, servo mismatch, reverse error and nonlinear error, the S trajectory test has obvious advantages. Meanwhile, compared with the actual S-shaped piece machining, which is under development for inclusion in ISO 10791.7, the proposed non-cutting kinematic test method can simply reflect the dynamic accuracy of five axis machine tools itself without the interference of other factors and does not require additional workpiece and testing equipment. The proposed method is verified through experiments on a five-axis machine tool.

Dynamic accuracy evaluation for five-axis machine tools using S trajectory deviation based on R-test measurement

The modelling of volumetric errors of machine tools has been widely used by the method of Homogeneous Transformation Matrix (HTM) based on rigid body kinematics analysis for a long time. Without spindle induced errors and thermal errors, to establish a closed-loop HTM for a three-axis machine tool, the total of 21 geometric errors are the primary elements to be known. It is well known that the measured points of translational errors are directly related to the volumetric error at the tool cutting point through rigid body kinematics. In the generalized HTM method, however, this relationship is missing. This report, therefore, proposes a new comprehensive approach to formulate the volumetric errors based on the famous Abbe principle in order to derive the error term in the motion direction, and Bryan principle to derive the error terms in orthogonal to the motion direction. The proposed methodology is simple in concept, rational in physical meaning and easy in implementation. Experimental results, including faster multi-degree-of-freedom error measurements, volumetric error analysis and compensation on a testbed of small machine tool, validate the correctness and effectiveness of this method.

A novel modeling of volumetric errors of three-axis machine tools based on Abbe and Bryan principles

Geometric errors have a comprehensive influence on the volumetric error of the five-axis machine tool. The identification of the vital geometric errors that have major effect on the volumetric error is the key problem to improve the machine tool accuracy. Therefore, a sensitivity analysis method to identify the vital geometric errors is proposed in this paper. The volumetric error model of a five-axis machine tool with 41 geometric errors is established based on the multi-body system method. Through the projection of the error vectors and the introduction of the effective cutting length, LSIL and GSIL are defined as the sensitivity indices for the local and global sensitivity analysis, respectively. Simulations are conducted for the local and global sensitivity analysis in which the LSIL and GSIL are used. And the analysis results have proven the validity of the proposed sensitivity analysis method. Compared with the conventional sensitivity analysis method, the proposed sensitivity analysis method has considered the position and posture error of the cutting tool simultaneously, which is more effective and efficient. The analysis results are helpful to improve the accuracy of the five-axis machine tool.

A sensitivity method to analyze the volumetric error of five-axis machine tool

The invention discloses a five-axis machine tool cutter posture and cutter point position error synchronous detection mechanism. The five-axis machine tool cutter posture and cutter point position error synchronous detection mechanism comprises a tilt angle measurement device, a spatial displacement measurement device, a rotating angle measurement device, an installation device and a follow-up platform, wherein the tilt angle measurement device, the spatial displacement measurement device, the rotating angle measurement device and the follow-up platform are all installed on the installation device, the spatial displacement device is used for measuring the three-axial-direction micrometric displacement of a cutter point when the rotating center of a cutter of the machine tool to be detected moves along a set track, and the tilt angle measurement device and the rotating angle measurement device are used for measuring the tilt angle and the rotating angle of the cutter of the machine tool to be detected respectively. By the adoption of the five-axis machine tool cutter posture and cutter point position error synchronous detection mechanism, real-time synchronous measurement of the tilt angle and the rotating angle of the cutter of the five-axis machine tool and the three-axial-direction micrometric displacement of the cutter point is achieved. The five-axis machine tool cutter posture and cutter point position error synchronous detection mechanism has positive significance in detecting high-precision machine tools.

Five-axis machine tool cutter posture and cutter tip position error synchronous detection mechanism

The position-dependent geometric errors (PDGEs) of the rotary axes have a critical influence on the accuracy of the five-axis machine tool. It is necessary to measure, identify, and further compensate the PDGEs to improve the accuracy of the five-axis machine tool. The present study presents a method to identify the PDGEs of the rotary axes using the double ball bar (DBB). The presented method requires eight measurement patterns based on four different installation positions of the DBB. All 12 PDGEs of the rotary axes can be identified, especially the angular positioning error that cannot be identified in most of the other presented methods. Experimental verification is carried out, and the measurement uncertainty and the limitations of the presented method are analyzed. Compared with some other reported studies, the advantage of the presented method is that it can identify all PDGEs of the rotary axis and requires less measurement patterns.

All position-dependent geometric error identification for rotary axes of five-axis machine tool using double ball bar

Since their ability to machine complex curved surface, five-axis CNC machine tools have been widely used in the machining field. To guarantee the accuracy of the complex surface machining, multi-axis linkage performance detection of five-axis machine tools is necessary. RTCP (rotation tool center point) is one of the basic essential functions for five-axis machine tools, which could effectively reduce the nonlinear errors caused by rotation axes when five axes move synchronously. On the basis of RTCP function, a way to detect multi-axes linkage performance of five-axis machine tools is introduced in this paper. Some simulations that exhibit the relationship between the tool center error trajectories and the factors influencing the linkage performance of the machine tool are established based on the linkage error model. Analyzing the rule of tool center error trajectories, a linkage error tracing method is proposed based on feature extraction and trajectory similarity. The experiments based on RTCP are carried out, and the tool center point error variation is shown to be consistent with the simulation result. Furthermore, the error of the error tracing method is less than 5%, which means the error tracing method could find the mismatch parameters effectively. This attempt provides a theoretical support for linkage performance detection and error tracing of five-axis CNC machine tools.

Research on error tracing method of five-axis CNC machine tool linkage error

The ruled surface is an important modeling method of the product. With high-performance requirement, more and more complex ruled surfaces with the characteristic of variant curvature are used in auto, aviation, or die mold. The time-varying tool position and orientation make the prediction of surface topography very difficult in the five-axis machine tool processing. In this paper, a three-dimensional surface topography prediction method is proposed. The point cloud of cutting edge is obtained after a series of matrix transformation by calculating the instant information of the local tool coordinate. Then, lots of tiny bounding boxes are constructed based on the profile of the workpiece. The 3D surface topography is obtained by the Boolean operation between the enveloping body of cutting edge and the tiny bounding boxes. The geometric error of the machine tool, the vibrations, and the deformation of the tool is also considered in the generation of the surface topography. The presented method is validated by a case study of the S test piece. The prediction results are verified by the measuring experiment at the same time. Finally, a partition optimization method on the surface topography for the complex surface of the S test piece is presented. The dynamic modification of the cutting parameters in different partitions of the milling process can greatly improve the surface quality without reducing efficiency.

Wei Wang

Papers

A sensitivity method to analyze the volumetric error of five-axis machine tool

Five-axis machine tool cutter posture and cutter tip position error synchronous detection mechanism

All position-dependent geometric error identification for rotary axes of five-axis machine tool using double ball bar

Research on error tracing method of five-axis CNC machine tool linkage error

A novel 3D surface topography prediction algorithm for complex ruled surface milling and partition process optimization