High Definition Metrology Research Articles

Sealing analysis of face-milled surfaces based on high definition metrology

Leakage is always a concern in the interface between the cylinder block and the head. Nowadays, sealing of this interface is almost exclusively achieved by multi-layer steel head gaskets. However, it is not only the gasket's quality that is responsible for perfect sealing. An equally important role is played by the surface topography of the mating surfaces. The top surface of the cylinder block and the bottom surface of the cylinder head are machined by face-milling and present periodic tool marks features. Based on the surface topography measured by high definition metrology, this paper presents a novel sealing analysis method to evaluate the sealing performance of the critical topography region around the combustion chambers. The method consists of three modules: tool paths or channels reconstruction, sealing region segmentation based on channels' directions, and sealing analysis on sealing regions with different channel directions. An engineering case demonstrates the sealing analysis procedures and verifies the effectiveness of the sealing analysis method. The analysis of 12 ring-shaped sealing regions found that channels' directions do have a significant impact on surfaces' sealing properties. Circumferential channels or paths are advantageous for surface sealing, while radial channels are prone to leakage.

High Definition Metrology-Based Quality Improvement of Surface Texture in Face Milling of Workpieces With Discontinuous Surfaces

AbstractIn face milling process, the quality of surface texture is vital for mechanical performance of workpieces. The quality of surface texture, especially for waviness, is directly affected by tool marks, a commonly observed phenomenon in face milling. However, appropriate approaches for evaluation and modeling of tool marks are absent to date. Limited to the resolution as well as the efficiency of conventional measurement instruments, the height data of tool marks is hard to be entirely obtained, leading to valuable information omission. Besides, most existing models of tool marks are established for general workpieces with regular geometry and continuous surfaces. Since the cutter-workpiece engagement mode has a significant impact on the generation of tool marks, current models could be inaccurate or invalid when dealing with workpieces with discontinuous surfaces. To overcome this shortage, a novel approach is proposed in this research, aimed at quality improvement of surface texture in face milling of workpieces with discontinuous surfaces. First, the evaluation indexes for tool marks are defined based on the recently developed high definition metrology (HDM). Second, the physical modeling of tool marks is presented, taking the face milling mechanism into account. Third, the physical-informed optimization model is developed to search for the optimal processing parameters for surface quality improvement. At last, the effectiveness of the proposed approach is verified by a face milling experiment on the engine blocks.

Cylinder head gasket design optimization based on high definition metrology

Traditional cylinder head gasket design is based on the assumption that the mating surfaces’ form is ideal plane. However, high definition metrology (HDM) shows that the top surface of cylinder blocks and the bottom surface of cylinder heads are far from ideal plane and presents a macro form error. To compensate the macro form error of the mating surfaces, a gasket design optimization method is proposed in this paper. It mainly consists of two modules. The first module is surface alignment and gap calculation. Measured surface topography is aligned by control point registration and the control points are found by region labelling algorithm. The second module is gasket design optimization based on surface gap. A case study shows the details of the proposed gasket design optimization method.

An extended bi-dimensional empirical wavelet transform based filtering approach for engineering surface separation using high definition metrology

Surface topography is an important index to evaluate product quality, and filtering is a key step to realize the extraction of embedded surface components. Based the reported achievements, bi-dimensional empirical wavelet transform (BEWT) outperforms the traditional filtering approaches in nonlinear and non-stationary signal processing. High definition metrology (HDM) is used to measure the engineering surface, which provides a 3D inspection of the entire surface. This paper proposes a novel extended bi-dimensional empirical wavelet transform (EBEWT) based filtering approach for engineering surface separation. The proposed approach consists of HDM data preprocessing, adaptive spectrum segmentation, EBEWT and areal surface evaluation. To verify the performance, the proposed approach is compared with areal Gaussian filter and the original BEWT by numerical simulations and four real-world cases. The results demonstrate that the EBEWT based filtering approach can separate the engineering surface properly, thereby indicating the irregularities of manufacturing process and function behaviors of the part.

Evaluation of surfaces complementarity based on high definition metrology

The gap between two mating surfaces has a direct influence on sealing performance. However, traditional surface metrology rarely characterizes the gap between two mating surfaces. To solve this problem, a novel concept of surfaces complementarity is proposed in this paper. Surfaces complementarity measures how well two rough surfaces fit into each other. To make this concept applicable in engineering practices, a virtual assembly algorithm is developed. The automatic virtual assembly algorithm aligns the mating surfaces by maximizing the overlap ratio of the surface masks. Then, a sum surface which is complementary to the surface gap is constructed to represent the mating states. The top surface of a cylinder block and corresponding cylinder head surface measured by high definition metrology is mated by the virtual assembly algorithm. The differences of functional parameters between the mating surfaces and the sum surface are discussed thoroughly. Due to surfaces complementarity, parameters of the sum surface has a certain deviation from expected combined parameters from two individual surfaces. A case of square surface shows the practical application potential of the virtual assembly algorithm to optimize the sealing performance of the mating surfaces.

Segmentation of workpiece surfaces with tool marks based on high definition metrology

Abstract Tool marks on face-milled surfaces contain huge information about the manufacturing processes. Direct surface segmentation based on tool marks is in favor of surface error sources diagnoses. However, traditional surface segmentation methods are prone to over-segmentation when partition surfaces with tool marks. This paper proposes an improved segmentation approach to solve this problem. Based on surface topography measured by high definition metrology (HDM), the surface segmentation methodology mainly involves four steps: automatic subsurface selection, local thresh-holding, broken tool marks repairing and water segmentation (abbreviated as “STRW” methodology). A novel concept called “periodic degree” is proposed and used as the criteria of subsurface selection. A binary image of tool marks is created by an adaptive local threshold. Broken tool marks are identified by a distance threshold and repaired by a convex-hull based tool marks repairing algorithm. Finally, water segmentation is applied to divide the surface into different regions and each region belongs to a unique tool tooth trajectory. Three real cases from powertrain plant demonstrate the procedures of the methodology and verify the effectiveness of the proposed methodology.

A Surface Connectivity-Based Approach for Leakage Channel Prediction in Static Sealing Interface

Leakage susceptibility is significant for the functionalization of engineering products, and surface topography plays a crucial role in forming the leakage channel in static sealing interface. This paper proposes a surface connectivity-based approach to predict the leakage channel in static sealing interface. The proposed approach consists of three modules including contact surface generation, leakage parameters definition, and leakage channel prediction. A high-definition metrology (HDM) instrument is adopted to measure the three-dimensional (3D) surface. The contact surface that can be considered as the sealing interface is generated by assembling the virtual gasket surface and waviness surface. Considering the spatial connectivity, two kinds of leakage parameters including connectivity parameters and correlation parameters are proposed to describe the characteristics of the contact surface. Meantime, a novel prediction algorithm is developed to directly indicate the potential leakage channel of the surface. Experimental results demonstrate that the proposed approach is valid to be accurate and effective, which can provide valuable information for surface topography and static sealing performance.

Surface Variation Modeling by Fusing Multiresolution Spatially Nonstationary Data Under a Transfer Learning Framework

High-definition metrology (HDM) has gained significant attention for surface quality inspection since it can reveal spatial surface variations in detail. Due to its cost and durability, such HDM measurements are occasionally implemented. The limitation creates a new research opportunity to improve surface variation characterization by fusing the insights gained from limited HDM data with widely available low-resolution surface data during quality inspections. A useful insight from state-of-the-art research using HDM is the revealed relationship and positive correlation between surface height and certain measurable covariates, such as material removal rate (MRR). Such a relationship was assumed spatially constant and integrated with surface measurements to improve surface quality modeling. However, this method encounters challenges when the covariates have nonstationary relationships with the surface height over different surface areas, i.e., the covariate-surface height relationship is spatially varying. Additionally, the nonstationary relationship can only be captured by HDM, adding to the challenge of surface modeling when most training data are measured at low resolution. This paper proposes a transfer learning (TL) framework to deal with these challenges by which the common information from a spatial model of an HDM-measured surface is transferred to a new surface where only low-resolution data are available. Under this framework, the paper develops and compares three surface models to characterize the nonstationary relationship including two varying coefficient-based spatial models and an inference rule-based spatial model. Real-world case studies were conducted to demonstrate the proposed methods for improving surface modeling.

High definition metrology enabled three dimensional discontinuous surface filtering by extended tetrolet transform

Surface topography is of great importance for the functional behavior of a part, and filtering is a key step in achieving topography analysis. The traditional filtering methods such as Gaussian and spline filter have been effectively applied in the continuous surface, but most parts have a great quantity of crucial discontinuous surfaces in engineering practice. A new filtering method based on extended tetrolet transform is proposed for the three dimensional (3D) discontinuous surface in this paper. The proposed filtering method consists of 3D discontinuous surface measurement and point cloud conversion, edge detector generation, extended tetrolet transform and 3D characterization parameters evaluation. It overcomes the drawback of edge effect which occurs in the discontinuous surface using traditional filtering methods. The 3D discontinuous surface is measured by high definition metrology that can generate millions of data points representing the entire surface. To verify the effectiveness of the extended tetrolet transform, it is compared with the areal Gaussian filter and areal spline filter both for the simulated continuous and discontinuous surface. The low frequency and high frequency components of engineering surfaces are separated exactly, and these results demonstrate that the proposed method is effective for the 3D discontinuous surface filtering without edge distortions.

Leakage Monitoring in Static Sealing Interface Based on Three Dimensional Surface Topography Indicator

Leakage directly affects the functional behavior of a product in engineering practice, and surface topography is one of the main factors in static seal to prevent leakage. This paper aims at monitoring the leakage in static sealing interface, using three-dimensional (3D) surface topography as an indicator. The 3D surface is measured by a high definition metrology (HDM) instrument that can generate millions of data points representing the entire surface. The monitoring approach proposes a series of novel surface leakage parameters including virtual gasket, contact area percentage (CAP), void volume (VV), and relative void volume (SWvoid) as indicators. An individual control chart is adopted to monitor the leakage surface of the successive machining process. Meantime, based on the Persson contact mechanics and percolation theory, the threshold of leakage parameter is found using finite element modeling (FEM). Experimental results indicate that the proposed monitoring method is valid to precontrol the machining process and prevent leakage occurring.

A fast and adaptive bi-dimensional empirical mode decomposition approach for filtering of workpiece surfaces using high definition metrology

The surface topography of workpieces has an important influence on the final performances of the product. The digital filtering is a critical step to analyze the surface topography of workpieces. Bi-dimensional empirical mode decomposition (BEMD) approach is superior to conventional filtering approaches in the analysis of non-stationary and non-linear data. High definition metrology (HDM) can generate massive point cloud data to represent the three-dimensional (3D) surface topography of workpieces, which provides a new opportunity for surface topography analysis. This paper develops a fast and adaptive bi-dimensional empirical mode decomposition (FABEMD) approach for filtering of workpiece surfaces using HDM. Firstly, the neighboring window algorithm is presented to extract local extrema and draw the extrema spectrum. Secondly, the adaptive window algorithm is developed to automatically select the optimal window size of the order statistics filter, and plot the envelope spectrum. Finally, the average smoothing filter is presented for smooth filtering and generating of the mean envelope. The performance of the proposed FABEMD-based filter is validated by a simulated surface data and three real-world surface data. Compared with Gaussian filter (ISO 11562:1996, ASME B46.1-2002), the BEMD-based filter and the recent shearlet-based filter in the qualitative and quantitative analysis, the proposed FABEMD-based filter is superior for the separation and extraction of different surface components.

A Systematic Approach for Online Minimizing Volume Difference of Multiple Chambers in Machining Processes Based on High-Definition Metrology

The volume variation of multiple chambers of a workpiece is one of the most important factors that can directly influence the performance of the final product. This paper presents a novel systematic approach for online minimizing the volume difference of multiple chambers of a workpiece based on high-definition metrology (HDM). First, the datum of high-density points is transformed by a random sample consensus (RANSAC) algorithm due to its good robustness in fitting. Second, a procedure containing reconstruction of interior curved surfaces of chambers, boundary extraction, and projection is developed to calculate the accurate volumes of the multiple chambers. Third, a model for obtaining an optimized machining parameter for depth of chambers is explored to minimize the volume difference of any two ones of all the chambers. The model is formulated as a multi-objective optimization (MOO) problem, and a new procedure of multi-objective particle swarm optimization (MOPSO) algorithm is developed to solve this problem. Finally, a milling depth is output as the optimal milling parameter for controlling the volume variation of multiple chambers. The results of a case study show that the proposed approach can minimize the volume difference of four combustion chambers of a cylinder head and it can be well applied online in volume variation control of multiple chambers in machining processes.

An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology

The shape of a machined surface significantly impacts its functional performance and exhibits different spatial variation patterns that reflect process conditions. Classification of these surface patterns into interpretable classes can greatly facilitate manufacturing process fault detection and diagnosis. High-definition metrology (HDM) can generate high density data and detect small differences of workpiece surfaces, which exhibits better performance than traditional measurement methods in process diagnosis. In this paper, a novel adaptive support vector machine (SVM)-based workpiece surface classification system is developed based on HDM. A nonsubsampled contourlet transform is used to extract features before classification with its characteristics of multiscale, multidirection, and less dimension of feature vectors. An adaptive particle swam optimization (APSO) algorithm is developed to search the optimal parameters of penalty coefficient and kernel function of SVM and is helpful to escape from the local minimum by its strong ability of global search. A varied step-length pattern search algorithm is explored to optimize the global point in every iteration of the APSO algorithm by its good performance in local search. These two algorithms are combined with their relative merits to find the optimal parameters for building an adaptive SVM classifier. The results of case studies show that the proposed adaptive SVM-based classification system can achieve a relatively high classification accuracy in the field of workpiece surface classification.

High-Definition Metrology Enabled Surface Variation Control by Cutting Load Balancing1

This paper presents a method to reduce surface variation in face milling processes based on high-definition metrology (HDM) measurements. Our previous research has found and established the relations between surface variation patterns, cutting forces, and process variables. Based on the findings, this paper compares potential machining methods and finds that the approaches of varying feed rate and lateral cutter path planning are most feasible for surface variation control. By combining the two approaches, an algorithm is developed to reduce cutting force variation along the feed direction and circumferential direction, respectively, thereby reducing the surface variation. The varying feed method can effectively eliminate the surface variation along the feed direction, while the optimal cutter path approach balances the cutting loads on the cutter and contributes to reducing cutting force variation along feed direction. Case studies were conducted based on a cutting experiment to demonstrate that the proposed method can improve the surface flatness by 25%. The cutter path adjustment algorithm was also implemented in an automotive engine plant leading to 15–25% improvement in surface flatness.

A diffusion filter for discontinuous surface measured by high definition metrology

High definition metrology can measure three-dimensional surface topography of discontinuous surfaces that have holes and grooves with large field of field and high lateral resolution. However, when separating discontinuous surfaces in to various scale-limited surface such as form, waviness and roughness, filtering techniques such as Gaussian filters cause “boundary distortion problems. This is because the discontinuous surfaces do not have large enough continuous evaluation area for Gaussian filtering. Therefore, this research proposes a modified anisotropic diffusion filter, which is formulated in terms of heat diffusion, to separate discontinuous surfaces into various scale-limited surfaces. An edge detector is proposed to ensure that the diffusion process only takes place inside the surface but not blur the surface boundaries. In addition, the diffusion time is specified considering the linkage with the cutoff wavelength of Gaussian filters. The performance of the proposed filter is validated by simulated and practical discontinuous surfaces. Moreover, the three dimensional areal surface texture features specified in ISO 25178 can be evaluated from the filtered scale-limited surfaces. Results have demonstrated that the proposed approach is an effective tool for separating and evaluating areal surface texture for discontinuous surfaces regardless of the continuity of the evaluation surface.

Tool Wear Monitoring of Wiper Inserts in Multi-Insert Face Milling Using Three-Dimensional Surface Form Indicators

The wear of wiper inserts directly affects the finishing surface quality in multi-insert face milling. This research aims at monitoring the wear of wiper inserts, using 3D surface form as tool wear indicators. 3D surface height map of the face-milled surface is measured by a high definition metrology (HDM) instrument and converted into height-encoded and toolmark-straightened gray images. 3D surface form indicators, including entropy and contrast, are extracted from the converted images with a modified gray level co-occurrence matrix (GLCM) method. Meanwhile, the wear of wiper inserts is measured using a tool presetter and measuring machine without dismounting wiper inserts from the cutter. Experimental results indicate that entropy shows a strong correlation with average axial wear of the wiper edges and contrast reflects the evolution of axial offset between wiper inserts.

A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology

The surface appearance is sensitive to change in the manufacturing process and is one of the most important product quality characteristics. The classification of workpiece surface patterns is critical for quality control, because it can provide feedback on the manufacturing process. In this study, a novel classification approach for engineering surfaces is proposed by combining dual-tree complex wavelet transform (DT-CWT) and selective ensemble classifiers called modified matching pursuit optimization with multiclass support vector machines ensemble (MPO-SVME), which adopts support vector machine (SVM) as basic classifiers. The dual-tree wavelet transform is used to decompose three-dimensional (3D) workpiece surfaces, and the features of workpiece surface are extracted from wavelet sub-bands of each level. Then MPO-SVME is developed to classify different workpiece surfaces based on the extracted features and the performance of the proposed approach is evaluated by computing its classification accuracy. The performance of MPO-SVME is validated in case study, and the results demonstrate that MPO-SVME can increase the classification accuracy with only a handful of selected classifiers.

Predicting machined surface topography based on high definition metrology

This paper aims to predict the three-dimensional surface topography for future machined parts based on high definition metrology (HDM). HDM can measure the 3D topography of previously machined surfaces with high resolution and large field of view, containing rich spatial-temporal information of the machined surfaces. Therefore, a space-time autoregressive integrated moving average (STARIMA) model is adopted to describe the spatial-temporal characteristics and predict the future surface. The model was validated by an experiment on the engine block face milling process. The results show a good agreement between the predicted surface and the real surface with an average forecast error of 0.181 micrometer for an area with 80×80 sites. The proposed surface topography prediction method can be used for the early warning of surface quality deterioration and pre-control of the manufacturing process.

A shearlet-based separation method of 3D engineering surface using high definition metrology

The 3D engineering surfaces are considered as having a range of spatial frequency components including roughness, waviness and form. Before the characterization of a 3D engineering surface, filtering is done first to separate these different components. To overcome the shortcomings of the traditional filtering methods, this paper presents a shearlet-based separation method using high definition metrology (HDM) that has the ability to measure a huge number of data points to represent a 3D surface. The 3D engineering surface is decomposed into different sub-bands of coefficients with non-subsampled shearlet transform (NSST). Then the surface components at different levels are reconstructed by applying an inverse NSST on the shearlet coefficients and combined into the above three surface components. The performance of the proposed method is validated by both simulated surface data and real-world 3D surface data, and the results demonstrate that the proposed shearlet-based method is effective for the separation and extraction of different surface components.

Modeling cutter tilt and cutter-spindle stiffness for machine condition monitoring in face milling using high-definition surface metrology

In face milling, the spindle is intentionally tilted to avoid backcutting. The cutter tilt during machining is a combined effect of the intentional initial tilt and cutter-spindle deflection, which varies with cutting load during machining. An accurate estimation of the cutter tilt is critical to surface quality control and machine health monitoring. However, due to the small magnitude, the spindle tilt and deflection are difficult to measure in real time. Conventionally, the cutter tilt can be obtained through in-line sensors mounted on the machine tool but such in-line measurement is greatly influenced by the dynamic machining conditions such as vibration. This paper proposes a method to monitor the spindle setup tilt and deflection using surface data measured by high-definition metrology (HDM). Two parameters are proposed to characterize the cutter tilt, i.e., cutter tilt at idle state (initial cutter tilt) for spindle setup and cutter-spindle stiffness for the cutter-spindle deflection. Cutting force modeling is conducted to estimate these two parameters in conjunction with statistical procedures that fit the model to HDM surface data. The estimated cutter-spindle stiffness variation is also correlated to machine conditions such as a loose or worn bearing for process diagnosis. The method is demonstrated via experimental data from a machining process for automotive engine heads.