Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

Probe Path Planning for Flatness Measurement on Coordinate Measuring Machine Using Ant Colony Optimization

Optimization of a flatness error inspection activity on coordinate measuring machine (CMM) is a very crucial problem which demands minimization of a probe path for productive inspection. In the present work, the approach is explained to minimize the total probe travelling length and hence, the time of flatness inspection. Three sampling methods with eight sample sizes have been considered for this work. The ant colony optimization (ACO) algorithm based on travelling salesman problem (TSP) approach was developed in MATLAB environment to find the shortest probe paths. It was verified that the probe path depends on the sampling method used to measure the flatness. The sampling method giving the shortest probe path was selected as the best-suited method for a particular sample size. The results obtained by analyzing an illustrative example shows that the proposed approach is both effective and optimum.

Numerical and experimental investigation of notch-induced high-speed precise shearing of 42CrMo bar

A notch-induced high-speed precise shearing method was developed for high-strength metal bars, which prefabricated V-shape circumferential notches in batch on the bar surface to make stress concentration, and applied a high-speed load to complete separation on a new type of electric-pneumatic counter hammer. The FE simulation and experimental tests were conducted; the influences of loading speed, notch depth, and axial clearance were analyzed on the fracture behavior and blank quality; the microfracture mechanism was further investigated. The results showed that the circumferential notch inhibited the plastic distortion and obtained high precision chamfered billets, with a roundness error of 1.34%, flatness error of 0.34 mm, and incline angle of 0.87°. Besides, the surface notch effectively reduced Max. impact force and fracture energy. The fractography revealed that: for the notched bar, the cracks initiated from the thin extrusion layers at the bilateral-notch tips, and from micro extrusion and intrusion at the top-notch tip. The predominant microfracture mechanism involves microvoid coalescence and forming of quasi-parabolic dimples along with the shear stress.

Effects of Position and Orientation Errors of Linear and Rotary Axis Average Lines of Five-Axis Controlled Machining Centre Onto its Motion Errors in Testing Method With Truncated Square Pyramid

In this paper, ‘position and orientation errors of linear and rotary axis average lines’ is newly named ‘geometrical mechanism deviations.’ This paper presents suggestive simulation results of tool motion error caused by geometrical mechanism deviations of a five-axis controlled machine tool. Firstly, there were assumed seven geometrical mechanism deviations consisting of three positional and four angular deviations. As positional deviations, the error of intersection is set to be 0.01 [mm] off-centre, and the squareness errors of the cross axes as angular deviations are 0.01 [°]. Secondly, there was simulated theoretically the shape of machined pyramidal surface according to the virtual cutter movement of a flat end mill along contouring tool paths. Thirdly, the correspondence of geometrical mechanism deviations and simulated flatness error was analysed and found to have two regularities. One of the two indicated that four pyramidal surfaces wave similarly with left half surface up and right half surface down. The other indicated that the centre of a specific pyramidal surface should be concave in the cases of squareness error between B-Z axes. Through the analysis of grouped flatness error, specific geometrical mechanism deviations seem to cause a particular deformation of pyramidal surface due to the misalignment of tool position and orientation.

Modeling of flatness errors in end milling of thin-walled components

Machined components deviate in size, form, and orientation in comparison to actual features realized by the designer. The deviations originate from several process-related factors and can be specified as per the Geometric Dimensioning and Tolerancing standards (ASME Y14.5-2009 or ISO 1101:2017). According to these standards, the deviation of planar or flat components is expressed in the form of flatness error. This article presents an overall framework to estimate static deflection–induced flatness errors during end milling of thin-walled planar components. The framework incorporates the Mechanistic force model, finite element analysis–based workpiece deflection model, and particle swarm optimization–based algorithm to estimate flatness-related parameters. The individual elements of the proposed framework are implemented in the form of computational tools, and a set of experiments are conducted on thin-walled parts. It has been observed that the static deflections of the thin-walled component have considerable influence on flatness error, and the same can be captured effectively using the proposed framework. The study also investigates the effect of inevitable aspects of the thin-walled machining, such as workpiece rigidity and thinning on the flatness error. The findings of the present study aid process planners in devising appropriate machining strategies to manufacture thin-walled components within tolerances specified by the designer.