Actual Machining Process Research Articles

Material corrosion characteristics of heat-treated SLM-ed Hastelloy X in electrochemical machining process

Selective laser melting (SLM) technology and electrochemical machining (ECM) offer new methods for producing nickel-based superalloys with complex structures and high surface quality, including Hastelloy X parts. However, SLM-fabricated parts exhibit significant anisotropy, and there are no effective criteria for evaluating material corrosion characteristics in actual ECM due to the detachment of research and application, making the parameter selection challenging in actual ECM. To address this issue, this article proposes a comprehensive evaluation system based on the corrosion characteristics in the actual machining process and investigates the effect of heat treatment temperature on the corrosion characteristics of Hastelloy X in ECM. The results show that at a heat treatment temperature of 1000°C, machining efficiency, accuracy, and localization can all be achieved at a good level with good consistency in different directions at the expense of a certain machining accuracy. The 800℃ heat-treated specimen exhibits strong corrosion resistance, providing a significant advantage in machining localization and accuracy. When the heat treatment temperature exceeds the solid solution temperature (more than 1175℃), the mechanical properties and corrosion characteristics of the specimen are significantly degraded. The results of various material testing methods, including electrochemical testing, EBSD, XRD, and EDS indicate that changes in grain size due to recrystallization and the galvanic effect of precipitates are the primary causes of these changes.

Research on identification of key processes in machining process based on PageRank algorithm

Aiming at the difficulty of quantitatively evaluating the critical processes in the manufacturing process of complex mechanical products, a critical process identification method based on the PageRank algorithm is proposed with the goal of identifying key processes in the machining process. Based on the complex network theory, the error transfer network model of the machining process is established in this paper. Adopting the actual machining process as the data set of the complex network, the weights of the machining feature nodes are calculated by the PageRank ranking algorithm, and the nodes are ranked according to the weight values to assess the influence and importance of the nodes in the network model. Finally, taking the connecting rod machining process of a medium-speed marine diesel engine as an example, the results show that the method can quickly and effectively identify the key processes in the machining process.

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

Research on thermal error compensation strategy of CNC machine tools based on full working area modeling

Abstract Traditional manufacturing equipment is developing in the direction of precision, intelligence and integration, and the precision requirements of CNC machine tools are getting higher and higher. Compensating and controlling the thermal error of the machine tool is an important part of improving machining accuracy. This paper uses a cuckoo search algorithm to optimize the BP neural network to get the CSBP neural network model and inputs the temperature value of the CNC machine tool into the CSBP model to get the thermal error prediction results. Combined with the thermal error values of each measurement point on the CNC machine table, the B-spline function is used to fit the thermal error prediction model for the entire working area. Finally, the thermal error compensation system of the CNC machine tool has been established. Simulation and empirical studies show that the fitting and prediction accuracy of the model in this paper are better, and the maximum error value of the prediction is reduced by 20.27% compared to the LSTM model. After the application of the thermal compensation system in this paper, the thermal error of the CNC machine tool is greatly reduced, and most of the offset caused by the thermal deformation of the machine tool is eliminated through compensation. In this paper, the design of a thermal error compensation system based on full work area modeling can effectively reduce the thermal error of CNC machine tools, which can be applied to the actual machining process of machine tools to improve the machining accuracy of CNC machine tools.

Study on the Ball-End Milling Cutter Design and the Performance of Milling SiCp/Al

The ball-end milling cutter is widely used in the machining of various complex curved surfaces and grooves. In order to meet the machining requirements of SiCp/Al, the mathematical model of ball end milling cutter, grinding track of grinding wheel and parameters in the actual machining process are optimized, and the experiment of milling SiCp/Al is designed. The results show that the surface roughness processed by the grinding tool is small. In the process of milling SiCp/Al, the main wear position of ball end milling cutter is the flank.

Part machining deformation prediction based on spatial-temporal correlation learning of geometry and cutting loads

The machining deformation prediction and control is the key issue to ensure the dimensional accuracy of parts and guarantee the quality of products. However, with the material removal and residual stress release, the part geometry and cutting loads keep changing during the machining process, and the complex superposition of the two eventually affects the machining deformation, making it difficult to establish a theoretical prediction model. Moreover, due to the limitation of the theoretical approach, the model cannot be continuously modified with the change of the cutting loads affecting the machining deformation during the actual machining process, which leads to low prediction accuracy. In this paper, we propose a prediction method for machining deformation based on spatial-temporal correlation learning of geometry and cutting loads. Firstly, the process of geometrical state change of workpiece considering material removal was modeled, and then the time-varying process model of external loads including cutting force and clamping force was established. Next, according to the tool trajectory during machining, the theoretical geometric variation and the actual cutting loads variation are fused in temporal and spatial terms, and the fused information also reflects the deformation state and internal stress variation of the workpiece. Finally, a deep spatio-temporal learning network is proposed to model the complex geometry-loads-deformation relationship for deformation prediction. The proposed method was validated in the actual part machining process, and compared with the end-to-end model as well as the simulation model, results proved that the method can accurately predict the machining deformation. The proposed method combines the theoretical variation of the machining process with the actual physical quantity variation data, and in terms of the loads variation predicted by the model and the actual collected loads, the model is more consistent with the actual machining state.

Multiple axis ultra-precision freeform deviation control using encoder data analysis

Keen interest in the positioning accuracy of the slow slide servos in ultra-precision machining has allowed for complex and creative features to be machined with optical quality in recent times. Currently, block processing techniques are commonly used to control the position of the tool-workpiece interface when fabricating such intricate features. Using a point cloud to obtain a deterministic position and direction of the tool movement, the machine uses block-processing to “look ahead” and predict the position of multiple axes in the next few points in preparation to move at the appropriate speeds and times. However, with larger buffer sizes, inconsistencies in the forward read speeds would be directly reflected upon the surface workpiece because of varying positioning velocities. Thus, in this paper, the parameters in block processing and ideal point cloud developments are studied using complex freeform curvilinear features to understand the implications and achieve the desired effects in the actual machining process using block processing. By directly obtaining the encoder positioning data, analysis is done to obtain the machining characteristics. This allows for a model to be developed for deviation control, increasing the overall accuracy of the machined parts.

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing.

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

Effects of Machining Parameters on Electrochemical Multi-Field Coupling

Electrochemical machining involves three couplings between electric field, flow field and thermal field. The precipitation of hydrogen on the surface of the cathode will affect the entire electrochemical machining process and the final machining quality of the workpiece. Finite element software is used to analyze the effects of different voltages, electrolyte inlet pressure and interelectrode gap on current density, hydrogen volume fraction, conductivity and temperature distribution in this article. The research results show that the increase of processing voltage will increase the current density, hydrogen volume fraction and temperature, and decrease the conductivity of the solution. As the pressure of the electrolyte increases, the current density and conductivity increase, but the hydrogen volume fraction and temperature decrease. The current density, hydrogen volume fraction and temperature decrease, and the conductivity increases when the gap between electrodes increases. At the inlet, the current density and conductivity are relatively large, and gradually decrease along the electrolyte flow direction, while the hydrogen volume fraction and temperature are the smallest at the inlet, and gradually accumulate along the electrolyte flow direction, and reach the maximum at the outlet. Through multi-physics coupling simulation, the current density, temperature, conductivity and bubble distribution in electrochemical machining can be predicted, which can provide a theoretical basis for actual electrochemical machining process parameter selection.

Optimal design of an opposite aerostatic thrust bearing considering rarefaction effect

Aerostatic bearings have been widely used in ultra-precision machine tools. As the key component, the performance of the air bearing has a dramatic impact on the machining accuracy. Traditional static design often takes load-carrying capacity and static stiffness as optimization objectives, while the vibration of the spindle is mainly resisted by the dynamic stiffness in the actual machining process. In this paper, the air film thickness and orifice size are optimized to improve the dynamic stiffness of the aerostatic thrust bearing by using the non-dominated sorting genetic algorithm-II (NSGA-II). The orifice diameters of upper and lower thrust plate are independent. As the optimal solutions tend to low air film thickness, the hypothesis of continuum flow between spindle and plate is no longer applicable. Thus, the rarefaction effect is considered by integrating a slip flow model into the Reynolds equation. It is found that the preferable parameters corresponding to static design and dynamic design are different. Compared with the conventional design with same orifice size, both static stiffness and dynamic stiffness can achieve a considerable improvement with independent orifice diameters. The research results are expected to provide suggestions for the design of opposite air thrust bearings.

Bubble flushing effect in micro EDM drilling and its relation with debris

In micro electrical discharge machining (EDM) drilling with a liquid dielectric, the bubble flushing effect is significant to the dielectric exchange and debris evacuation. Clarifying its mechanism is critical to guild the machining and avoid the deterioration of machining environment, which is a common problem in deep hole drilling with micro EDM. However, the investigation of bubble flushing is always disturbed by debris, which affects bubble production through disturbing the discharge process, because the debris is always conductive as a byproduct of metal materials. In this paper, the bubble flushing effect is systematically investigated from the in situ observation to statistical analysis to clarify the driven force for bubble movement or escape in the narrow gap and assess the evolution of bubble flushing effect with increasing hole depth. Moreover, the influence of debris on the bubble flushing effect is quantitatively analyzed through the contrast experiments in single crystal SiC and stainless steel SUS304 (SS304). It is confirmed in this paper that the debris of SiC has nearly no effect on the discharge due to its special material property. The visible and quantitative investigations reveal the nature bubble flushing effect in micro EDM drilling. The contrast analysis of bubble flushing effect under two cases (with/without influence of debris) clarifies how bubble and debris affect one another and their influences on the drilling process. This study provides new insights into the mechanism of micro EDM drilling and benefits to optimize the actual machining processes for the industry and manufacturer.

The Swept Volume of a Circular Paraboloid End Mill Moving Along a Helical Path

To obtain complex part geometries at one pass using machining processes, it is important to employ the tools with non-conventional geometries. A circular paraboloid is a solid of revolution, which can be obtained by rotating a parabola. The swept volume of an end mill can be defined as the unification of all sets of points on the tool for every instant as it moves, and its derivation is an obligation to determine the machined part geometry prior to an actual machining process. After derivation of the swept volume of the tool, machined part geometry is obtained by subtracting the swept volume of the tool from the volume of the initial workpiece. However, derivation of the swept volume of the tool is not a straightforward task. In this work, an analytical model was introduced to derive a complete set of points on the machined part by means of well-defined and constrained tool geometry and tool path. In the model, a plane that passes through the screw axis was used to observe the instant cross-section of the tool as it moves along the helical path. By overlapping the instant cross-sections of the tool in the plane, the final cross-section was derived. Since all cross-sections that pass through the screw axis are identical, the method gives an entire set of points on the machined surface. To validate the model, a computer-aided design program was utilized.

An Initial Residual Stress Inference Method by Incorporating Monitoring Data and Mechanism Model

Initial residual stress is the main reason causing machining deformation of the workpiece, which has been deemed as one of the most important aspects of machining quality issues. The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control. Due to the principle error of existing residual stress detection methods, there are still challenges in practical applications. Aiming at the detection problem of the initial residual stress field, an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper. Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress, and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish; the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model. Finally, the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model. Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263

In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L27 orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses.

Threshold speed of short journal bearings considering the non-Gaussian longitudinal surface roughness

During the actual machining process of journal surface, such as milling and fine turning, the surface roughness has strong non-Gaussianity. Thus, the effect of non-Gaussian longitudinal roughness on threshold speed of short journal bearings is investigated in this study. Introducing the parameters of kurtosis and skewness, the model of non-Gaussian surface roughness is established by modifying the Gaussian distribution. It is found that the non-Gaussianity of surface roughness cannot be neglected especially for the journal rotating with a larger eccentricity ratio, the error of Gaussian distribution surface reaches 19.33% when the eccentricity ratio equal 0.7. The increasing of kurtosis would reduce the threshold speed, while the increasing of skewness would enhance the threshold speed. For a larger eccentricity ratio, the effect of kurtosis and skewness on threshold speed is more distinctly.

Broad Learning System Based on Binary Grey Wolf Optimization for Surface Roughness Prediction in Slot Milling

Surface roughness is considered to be one of the most significant indicators for evaluating machining quality. Manufacturing industries generally aim to achieve high product quality while considering reducing production costs and time. Therefore, an accurate and reliable surface roughness prediction model plays a key role in the production process. Recently, a broad learning system (BLS) has attracted great attention due to its outstanding performance. In order to improve the prediction accuracy of surface roughness and reduce the influence of uncertain factors, sensors are necessarily added to monitor the product processing. Consequently, the surface roughness data obtained in the actual machining process tends to have a small sample size and a high feature dimension. In addition, due to the structural characteristics of the BLS, its hidden layer neuron nodes often carry some irrelevant or redundant features during the training process, which affects the prediction performance of the model. To overcome these problems, this article proposes a novel BLS architecture for surface roughness prediction, in which binary grey wolf optimization (BGWO) is used for the selection of neurons in the hidden layer. To verify the effectiveness of the proposed method, a slot milling surface roughness experiment was performed and compared with other methods for different evaluation indicators. The results show that the proposed method provides an effective solution for surface roughness prediction.

Genetic programming approach to predict the performance characteristics of WEDM taper cutting process

One of the unique applications of the wire electrical discharge machining (WEDM) method is taper cutting process. This process is ideal for numerous intricate, difficult to machine materials with complex profiles, deep slots with tight corners and features at different angles, those are basically used in defense and aerospace applications. According to the literature, numerous mathematical models based on physics and data may be created to identify the optimum parameter settings throughout the taper cutting process. However, it is a challenging process since it necessitates extensive understanding of how to do tapering operations with WEDM. As a result, a genetic programming (GP) model is created in this study to anticipate the angle inaccuracy during WEDM taper cutting. Six process parameters including workpiece thickness, taper angle, pulse duration, discharge current, wire speed and wire tension were considered at three level each for minimizing the angular error and surface roughness during WEDM taper cutting process. The L27 orthogonal array of Taguchi’s design of experiment is utilized to collect data on the procedure. Three assessing criteria are used to assess the correctness of the suggested model: root mean square error (RSME), mean absolute percentage error (MAPE) and co-efficient of determination (R2). In compared to typical prediction approaches, the results suggest that MGGP is a successful strategy. The model is cost effective and time saving way to investigate angular error and surface roughness in taper cutting before performing the actual machining process.

Optimization of the processing parameters in laser sharpening of bronze-bonded diamond grinding wheels

In this paper a new contribution to the optimization of processing parameters in the process of laser sharpening grinding wheels is presented. In the establishment of the three-dimensional heat conduction model, the laser heat source model is improved and the temperature field of the pulsed laser ablation material is obtained. The effects of laser power, laser line spot overlap ratio and laser scanning track overlap ratio on the sharpening efficiency and accuracy have been studied through the established numerical model. Like what happened in the actual machining process, the morphology of the bond material surface after laser sharpening was obtained by the simulation. The results show that the optimal values of laser power, laser line spot overlap ratio and laser scanning track overlap ratio are 20 W, 70%, and 70%, respectively. The simulation results have been verified and compared with the experimental results, revealing a good correlation between the simulation and experimental data. In a word, the numerical model established in this paper can be conveniently used in the optimization of the process parameters in the laser sharpening grinding wheel.

Grain refinement mechanism of metamorphic layers by abrasive grinding hardening

The abrasive grinding hardening is not only a high precision material removal process, but also generates metamorphic layers to improve surface strength, which is affected by surface material grain size. As the grain size is decided by dynamic recrystallization phenomenon under grinding thermal-stain coupling, the grain refinement mechanism should be studied. The full scale thermal-strain coupled finite element method (FEM) considering wheel's profile and abrasive distribution is established firstly. Afterwards, the governing equation on the increase of dislocation density is set up by three dimensional cellular automata (CA). And the refinement process of metamorphic layers is modeled. Meanwhile, the influence mechanism of dynamic thermal-strain effect on austenite grain in metamorphic layers is studied. Finally, with the help of grinding experiments, the variation regulation on austenite grain size and its content along the surface layers is compared and validated under different feeding rates and grinding depths. The obtained conclusion can not only improve the transformation mechanism of metamorphic layers in abrasive grinding hardening, but also give a parametric reference for grain refinement in actual machining process.

ForceNet: An offline cutting force prediction model based on neuro-physical learning approach

Accurate cutting force prediction serves as an important reference to the optimization of numerically controlled machining process. Traditional cutting force modeling via theoretical cutting mechanism hampers accurate prediction for actual machining process due to its highly suppressed modeling flexibility. On the other hand, machine learning based modeling approaches demand large amount of diversified labeled samples to achieve comparable prediction results, while collecting these samples can be tedious and costly because the cutter workpiece engagement (CWE) keeps changing during actual process. This paper presents a cutting force prediction model, named ForceNet, which incorporates elementary physical priori into structured neural networks to predict cutting force for end-milling process of complex CWE. The main idea is to use grayscale images to represent CWE geometry, providing a universal input to the ForceNet. Unlike traditional deep neural networks served as an unexplainable black box, the core of the ForceNet is constructed by the vector summation of directional primitive cutting force elements, which are approximated using elementary neural networks. Preliminary results indicate that ForceNet outperformed existing methods not only with greater prediction accuracy in unseen cutting situations, but also with less training data needed thanks to its inherent neuro-physical structure.