Prediction Of Surface Topography Research Articles

Material removal profile and surface topography prediction of ultrasonic vibration-assisted polished based on gray wolf optimization neural network

Material removal profile and surface topography prediction of ultrasonic vibration-assisted polished based on gray wolf optimization neural network

Model for surface topography prediction in the ultra-precision grinding of silicon wafers considering volumetric errors

Ultra-precision grinding machine is widely used for thinning silicon wafers. The wafer surface quality is considerably affected by volumetric errors at the functional point of the ultra-precision grinding machine. Therefore, a volumetric error model is established based on the homogeneous transformation matrix (HTM) method. The model is, then, optimized by considering Abbe and Bryan errors induced volumetric errors during the error measurements process. A prediction model for the wafer surface topography is established by combining the manufacturing mechanism of the ultra-precision grinding and the optimized volumetric error model of the ultra-precision grinding machine. Volumetric errors are detected using an author’s group developed laser measurement system and a commercial spindle error analyzer. The model enables to predict the topography and total thickness variation (TTV) of the wafer surface. A group of grinding experiments are performed to verify the effectiveness of the proposed models.

Prediction of surface topography for the five-axis bull-nose end milling of directional plexiglass considering tool runout and dynamic displacement

Prediction of surface topography for the five-axis bull-nose end milling of directional plexiglass considering tool runout and dynamic displacement

Surface topography prediction of slider races using formed grinding wheel shape and material removal mechanism

Compared with the roughness, the three-dimensional (3D) topography parameters, surface microstructure geometric characteristics and other information can more fully evaluate the grinding quality of the slider raceway surface. In this paper, based on the 3D topography model of the abrasive particle distribution on the surface of the formed grinding wheel, the material removal mechanism between the abrasive particle and the raceway surface is analyzed. With the undeformed chip thickness distribution model as the intermediate variable, the 3D topography model of the slider raceway surface is established, and the model verification is carried out from the roughness and the geometric characteristics of the surface microstructure, respectively. At the same time, the surface microstructure is extracted from the topography model, and the effects of different grinding process parameters on the geometric characteristics such as the height to width ratio, depth to width ratio and distribution density of groove, convex peak and peak valley structures are studied. Results are shown that AS,TH increase from [0.05 0.6 μm] to [0.25 0.8 μm] and FGH grows from [0.11 1.05 μm] to [0.5 1.61 μm] when the grinding depth rises from 1 μm to 4 μm. AS, TH are firstly decreased from [0.17 0.61 μm] to [0.08 0.52 μm] and then increased to [0.26 0.78 μm], and the FGH declines from [0.34 1.01 μm] to [0.16 0.86 μm] and then increases to [0.51 1.38 μm] with the feeding speed is in [25, 28 m/min]. In addition, in the range of grinding wheel linear velocity [28, 34 m/s], the AS,TH decreases from [0.19 0.81 μm] to [0.1 0.55 μm] and the FGH decreases from [0.55 1.6 μm] to [0.2 1.1 μm]. This can prepare for the subsequent research on the impact of the topography characteristics on the friction coefficient and wear amount of the slider raceway surface.

Prediction of Surface Topography in Robotic Ball-End Milling Considering Tool Vibration

Because of their low cost, large workspace, and high flexibility, industrial robots have recently received significant attention in large-scale part machining. However, due to the stiffness limitations in robot joints and links, industrial robots are prone to vibration during milling processes, which leads to poor surface topography. In robotic milling processes, it remains challenging to simulate the surface topography accurately. This paper presents a mathematical model of surface topography combined with the effects of process parameters and tool vibrations in robotic milling. In this method, the kinematic trajectory of the cutting edge is first calculated by considering the cutter geometry, tool eccentricity, tool orientation, and redundancy angle. After that, the posture-dependent dynamic characteristics of the robotic milling system are predicted using an inverse distance-weighted approach. Then, a dynamic model of the robotic milling system is constructed for calculating tool vibration displacements. Finally, the kinematic model of cutting edges is modified using Z-map to incorporate the obtained vibration displacements into the sweep surfaces. In addition, milling experiments are carried out to verify the effectiveness of the proposed method, showing a good agreement between predicted and measured surface roughness. Furthermore, the findings offer valuable insights into the impact of process parameters and robot posture on surface quality.

The prediction of 3D surface topography in a high-speed micro-milling process with aerostatic spindle

Aerostatic spindle has great importance to ultra-precision machine tools. Under different cutting parameters, the cutting force of aerostatic spindle will fluctuate and change discontinuously, which tends to cause a large vibration response of the spindle and leads to chatter phenomenon and machining surface failure. In order to improve the manufacturing efficiency and surface quality, a five-degree of freedom (5-DOF) dynamics model of aerostatic spindle is established, and the coupling effect between the rotor dynamics and micro-milling process is analyzed. In view of the dynamic vibration response of the tool tip in the micro-milling process, a prediction model of the three-dimensional (3D) surface topography of the workpiece is established. The effects of different cutting parameters (rotating speed, axial cutting depth and feedrate) and tool runout on the surface topography are quantitatively analyzed. The experimental results of 3D surface topography agree well with the simulated results. The results show that during the steady cutting process, the surface roughness is almost constant as the speed and cutting depth expand, but increases with the increase of the feed rate. When the tool tip runout amplitude is less than half of the feed amount per tooth, the workpiece surface roughness increases, otherwise, the workpiece surface roughness remains unchanged.

Multimodal data-driven machine learning for the prediction of surface topography in end milling

Multimodal data-driven machine learning for the prediction of surface topography in end milling

A novel surface topography prediction method for hybrid robot milling considering the dynamic displacement of end effector

A novel surface topography prediction method for hybrid robot milling considering the dynamic displacement of end effector

Prediction of surface topography in face gear grinding based on dynamic contour interferometric sampling method

Prediction of surface topography in face gear grinding based on dynamic contour interferometric sampling method

Modeling of high-feed milling and surface quality applied to Inconel 718

In modern manufacturing, machining remains a vital process for complex mechanical components. In particular, the aerospace industry extensively employs high-feed milling techniques to machine complex geometries from nickel-based superalloys. This study focuses on the analysis and modeling of high-feed milling for Inconel 718 in 2.5-axis machining. Its objective is to develop a generalized model of high-feed milling that enables the prediction of surface topography. The proposed model integrates crucial geometric parameters of the tool and its exact kinematic within the machine, along with tool and machine deflections caused by cutting forces. A key novelty of this research lies in its capability to determine surface topography and its quality based on a generalized model, representing significant progress in the field of high-feed milling. To validate the model, experimental efforts are measured to characterize the cutting forces and system deflections during machining. The developed approach demonstrates its ability to model surface topography and to predict surface roughness. It also highlights the influence of tool and machine deflection on surface quality. This research contributes to the advancement of the application of high-feed milling in aerospace manufacturing by enhancing machining capabilities and improving part quality.

Simulative surface topography prediction of tribological surfaces on whirled thread flanks

This paper investigates the surface texture on trapezoidal thread flanks manufactured by whirling, which is used for machining long threaded spindles. An analytical and numerical approach for describing surface texture is compared, and texture parameters are developed based on existing methods. Although the analytical model provides a good description of texture height and length, it has limitations in representing the surface as a topography. In contrast, the numerical approach enables detailed modeling of the surface topography on the thread flank as a function of the process parameters. The effective number of flank cutters is found to significantly influence the resulting topography, leading to differences between the measured values and the analytical model within a specified investigation range. Using a material removal simulation, this effect could be explained by the flank cutter position error. In addition, material removal simulation offers great potential for optimizing the surface texture on thread flanks and for tailoring tribologically optimized surfaces in the whirling process.

A high efficiency 3D surface topography model for face milling processes

Predicting the 3D surface topography in milling is the basis of analyzing its surface contact properties. However, most studies have been devoted to improving the accuracy of models and ignoring the efficiency of models, which has resulted in predicting the surface topography of large-size face milling becoming a time-consuming task. In this paper, a high efficiency 3D surface topography prediction model was proposed for face milling processes. The high efficiency of the proposed model was demonstrated by reducing the data volume and optimizing algorithms. First, a traditional 3D surface topography model was developed. Then, a high efficient 3D surface topography model was proposed based on the optimization of the data volume and algorithms. Next, the validity of the proposed model was verified by face milling experiments. Finally, compared with traditional models and other models, the proposed model had the highest computational efficiency. The proposed model provides a method for surface topography analysis of large-size face milling and an insight for optimizing other models.

Comprehensive evaluation of surface parameter correlation in running-in wear process

The first wear process is running-in, which is a stage of each machine system must go through. The friction pair’s surface topography is an important feature of the running-in process. After running-in, the surface topography directly affects the performance in stable working state. The wear performance of the contact pair was focused on by mostly existing studies after a time; however, we should consider the correlation of the surface topography before and after running-in. In fact, exploration of this problem is significantly important to extend the service life of machine parts and improve the running performance. Based on the three-dimensional (3D) surface topography parameter evaluation method, in this study, experiments were designed to analyze the effect of the unworn surface topography on that after running-in, and investigated the correlation of 3D surface topography parameters. The results demonstrated that the topography characterization parameters, i.e., Sa, Sq, Sv, Sdc, Str, Sdq, and Sdr under different working conditions maintained a strong autocorrelation (the correlation coefficient of the same parameter). Moreover, the cross-correlation (the correlation coefficient between different parameters) among height parameters (Sq), hybrid parameters (Sdq, Sdr), and functional parameters (Sdc, Sk) was strong. The results of the surface roughness parameters found in this study can be used as input and output feature selection research based on working conditions and surface topography prediction after the modeling research of running-in surface topography.

Longitudinal-Torsional Ultrasonic Grinding of GCr15: Development of Longitudinal-Torsional Ultrasonic System and Prediction of Surface Topography.

The common material of bearing rings is GCr15 bearing steel which is a typical difficult-to-machine material. As an important working surface of the bearing, the inner surface of the raceway plays a vital role in the performance of the bearing. As an important means to solve the high-performance manufacturing of difficult-to-machine materials, longitudinal-torsional ultrasonic processing is widely used in various types of processing. In the presented work, the basic size of the horn is obtained from the wave equation of the forced vibration, and the modal analysis and amplitude test are carried out to verify the rationality of the LUTG structure. Then, according to the probability density function of cutting thickness and the overlapping effect of adjacent abrasive trajectories, the LUTG surface topography prediction model is established by using the height formula of the surface residual material, and the model reliability is verified by using the orthogonal test. The error between the test results and the prediction model is within 13.2%. Finally, based on the response surface method, the optimal process parameters that can meet the requirements of low roughness (Ra) and high material removal rate (MRR) are screened, and the optimal combination of process parameters is obtained as follows: A = 4.5 μm, n = 6493.3 r/min, ap = 28.4 μm, and vf = 21.1 mm/min.

Prediction and optimisation for surface roughness distribution in external spline shaping based on micro-element method

Previous surface topography prediction models for external spline shaping do not consider the relationship between the tool and workpiece geometry and cutter edge wear. In this work, by considering the tool–workpiece geometry correlation, a model for the tooth surface topography of the external spline is established based on the micro-element method. Combined with adhere model, a gear shaper wear model is founded on the wear mechanism of cutter edge micro-element derived from the abrasive wear mechanism. The distribution characteristics of tooth topography are analysed through an external spline shaping experiment, which verifies the validity of the presented model. For the uneven distribution of tooth surface topography, a shaping process optimisation algorithm is proposed based on the advance-and-retreat method, which can obtain tooth surfaces with uniform surface topography distribution and reduce tool wear. This work can guide the optimisation of the external spline shaping process, improve the integrity of the tooth surface and prolong the serviceable life of gear shaper.

Modeling and prediction of surface topography and surface roughness in magnetic-field-enhanced shear-thickening polishing of SiC mold

To fill the gap in the research on the surface morphology formation theory of small aspheric magnetic-field-enhanced shear-thickening polishing (MESTP), a theoretical framework for surface morphology formation during the MESTP process was proposed for the first time. Based on the macroscopic computational fluid dynamics and convolution iteration theory, a multiscale surface morphology prediction model was established. This model considers the mechanical mechanism of the ductile-brittle transition of hard and brittle materials and effective cutting abrasive distribution law. Through a series of experiments and simulations, the maximum relative error between the measured Sa data and theoretical prediction data was 9.1%, and the accuracy of the model was analyzed based on RMSE. The proposed model can predict the microsurface morphology in MESTP.

3D surface topography prediction in the five-axis milling of plexiglas and metal using cutters with non-uniform helix and pitch angles combining runout

3D surface topography prediction in the five-axis milling of plexiglas and metal using cutters with non-uniform helix and pitch angles combining runout

Modelling and prediction of surface topography on machined slot side wall with single-pass end milling

Modelling and prediction of surface topography on machined slot side wall with single-pass end milling

Theoretical and experimental investigations of surface generation induced by ultrasonic assisted grinding

Ultrasonic assisted machining has been proved to have good performance for difficult to cut materials. Due to the complex contact between abrasives and workpiece, in-depth insight of material removal process of ultrasonic assisted grinding (UAG) is still challenging. In this investigation, ultrasonic surface texture is studied considering the effects of the grinding wheel topography, brittle-ductile transition behavior and ultrasonic vibration. A novel theoretical model of the grinding wheel topography is reconstructed considering the shape, size and random arrangement of abrasives. Based on this, surface topography prediction is conducted by the numerical method. The simulation analysis demonstrated that interference and self-interference of abrasive grits induced by ultrasonic vibration are the primary patterns, which explains the material removal mechanism in the UAG process. Furthermore, the grinding damages of the brittle and ductile grinding are parameterized based on different brittle and hard nature. Surface topography and surface roughness are predicted and analyzed by comparisons of simulation and experimental results. Those models proposed in this paper have potential for in-depth understanding the material removal process and optimizing the process parameters of the UAG process.

Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects. • Multi-modal in-situ melt pool (MP) monitoring. • Data registration framework for large scale multilayer MP data. • In-situ MP signature significance, quantification, and analysis. • Accurate surface topography prediction using the registered MP data. • Applicability in in-situ part qualification.