Machining Quality Research Articles

Research on Machining Quality Prediction Method Based on Machining Error Transfer Network and Grey Neural Network

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data.

Influence of feed entrance angle on transverse tearing burr formation in the milling of superalloy honeycomb with ice filling constraint

In the end milling of superalloy honeycomb cores, the contact form between the cutting tool and honeycomb wall and the force state of the honeycomb wall are variable, there will be difficulties in chip removal. The irregular extension of transverse tearing burrs can deteriorate the machining quality of honeycomb core surfaces, thereby adversely affecting the assembly and service performance of sandwich structures. Based on kinematic analysis, modeling of the removal and deflection process of honeycomb walls was conducted. The feed entrance angle in the case of machining thin walls with high feed speeds was characterized, and the force state of the honeycomb wall and the degree of shear or compression it bears were analyzed. Analytical models of damage scale and single-tooth cutting state were established under different feed entrance angles. The impact of feed entrance angle on the minimum burr length and the minimum constraint failure width was analyzed. The milling experiments of GH4099 superalloy honeycomb wall with ice filling constraint were designed and conducted to clarify the influence of different feed entrance angles on cutting force and transverse tearing burr length. The morphology of burrs and the wear form of the cutting tool were analyzed, and the formation mechanism of transverse tearing burr on the honeycomb sidewalls was revealed. The research results indicate that the feed force Fx is 2.1 to 4.7 times greater than the main force Fy or the back force Fz. When the feed entrance angle is <90°, transverse tearing burrs either do not occur, or occur crimped burrs that are gradually removed. Particularly within the range of 0° to 30°, the machining quality is better. The cutting force has a significant shearing effect on the honeycomb wall, making it susceptible to plastic deformation and reaching the fracture strain. The cutting edge can effectively remove material from the honeycomb wall, resulting in normal wear on the minor flank face and tip of the cutting tool. When the feed entrance angle is greater than 90°, it is easy to form large banded burrs, and the burr length is relatively similar to about 1500 μm. The cutting force has a significant extrusion effect on the honeycomb wall, resulting in minimal deflection deformation of the honeycomb wall, and making it difficult to fracture. The cutting tool fails to perform effective cutting, leading to increased friction between the cutting tool and honeycomb wall, which continuously induces irregular extension of the burrs. The research findings are of great significance for achieving damage control in metal honeycomb core machining and guiding process and clamping optimization.

Research on Critical Quality Feature Recognition and Quality Prediction Method of Machining Based on Information Entropy and XGBoost Hyperparameter Optimization

To address the problem of predicting machining quality for critical features in the manufacturing process of mechanical products, a method that combines information entropy and XGBoost (version 2.1.1) hyperparameter optimization is proposed. Initially, machining data of mechanical products are analyzed based on information entropy theory to identify critical quality characteristics. Subsequently, a quality prediction model for these critical features is established using the XGBoost machine learning framework. The model’s hyperparameters are then optimized through Bayesian optimization. This method is applied as a case study to a medium-speed marine diesel engine piston. After the critical quality characteristics in the machining process are identified, the machining quality of these vital characteristics is predicted, and the results are compared with those obtained from a machine learning model without hyperparameter optimization. The findings demonstrate that the proposed method effectively predicts the machining quality of mechanical products.

Optimization of Machining Parameters for Product Quality and Productivity in CNC Machining of Aluminium Alloy

This study focused on optimizing the process of CNC machining to enhance productivity and product quality of surface finish Ra via the process parameters of the cutting speed (vc), feed rate (vf), and cutting depth (doc). Experimentation was performed on workpieces of AA-6061 to investigate the response Ra through variation of the process parameters to analyze their best fit using RSM with 23 full factorial designs L-8 of DOE. The analysis of variance (ANOVA) was then used to find the major contributors among them that were responsible for the Ra. Based on the result, better Ra was obtained at 0.103 μm using the best fit of vf (150 mm/min), vc (220 m/min), and doc (0.1 mm). ANOVA shows vf contributed better Ra followed by vc and doc respectively. In addition, the level of Ra’s was analyzed through contour plots represented by different colours. It continued to analyze the effect of the process parameters via the main effects plot, Pareto chart, and the contour plot in the predictive desirability model, which indicated that the plots and chart confirmed the vf had more influence compared to others. The studyconfirmed that the low-level parameters provided better Ra to be used for polishing.

Processing of Layered Composite Products Manufactured on the Basis of Bioresin Reinforced with Flax Fabric Using Milling Technology.

In this work, a laminate based on bioresin and natural fibers was produced. Flax fabric was selected as the natural fiber. The biocomposite was subjected to strength tests. Stress-strain characteristics and strength indicators were determined. The workability of the laminate produced was also tested using milling technology. The tests were carried out using five carbide shank cutters for different purposes. The cutters with the geometry used in the processing of polymer materials and composites, general purpose cutters, and cutters with the geometry for aluminum and with different numbers of blades were analyzed. In order to obtain information on the workability of the prepared material, machining tests with different configurations of technological parameters were carried out. For each cutter, the effect of cutting speed and feed rate on the quality of the machined surface was tested. Due to the small thickness of the laminate, the machining was carried out in one pass, as a result of which the cutting depth in each case was constant. Changes in cutting speed and feed were evenly distributed over five levels. The quality of machining was assessed in two stages. The first stage included a visual assessment of the machined surface, involving a preliminary qualification of the machining parameters. The criterion was the amount of chips, frays, burrs, etc., remaining after machining that adhered to the surface. The next stage was the measurement of the geometric structure of the surface, during which the roughness parameters were analyzed using an optical microscope with a roughness analysis attachment. Quantitative analysis was performed for the best quality composite surfaces from each measurement series. The studies showed a dependence of the quality of machining on the technological parameters used. High tool speed, regardless of the type, especially at low feed, led to the sticking of chips, which had a very delicate form. In turn, low tool speed and high feed, due to the chip thickness, favored the formation of burrs. Machining with different types of tools showed that the process progresses better for tools with sharp blade geometry. Machining with a regular and polished cutter did not show any differences in the scope of the process progress.

Optimization of Tool Wear and Cutting Parameters in SCCO2-MQL Ultrasonic Vibration Milling of SiCp/Al Composites

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finishes. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. The results show that under the condition of SCCO2-MQL ultrasonic vibration, proper control of milling parameters can significantly reduce tool wear, extend tool service life, improve machining quality, and effectively reduce blade breakage and spalling damage to the tool, reduce abrasive wear and adhesive wear, and thus significantly improve the durability of the tool. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model’s relevance and accuracy were confirmed using F-tests and t-tests. The results show that the model can effectively predict tool wear, among which cutting velocity vc and feed rate fz are the key parameters affecting the prediction accuracy. Finally, a genetic algorithm was used to optimize the milling parameters, and the optimal parameter combination (vc = 60.00 m/min, fz = 0.08 mm/z, ap = 0.20 mm) was determined, and the optimized milling parameters were tested. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning

Multivariate machining quality prediction of thin-walled parts with multiple machining features is a complex problem due to different data distribution between training and unlabelled test samples. Traditional quality prediction methods ignore the correlation of multiple quality labels and do not consider changes in data distribution, resulting in low accuracy of multivariate quality prediction. Therefore, a multivariate quality prediction method using multi-task parallel deep transfer learning is proposed to solve this problem. Specifically, a multi-output quality prediction model of cross-machining features is constructed through the joint design of multi-task parallel learning and deep transfer learning. Furthermore, a domain matcher is designed to form multiple transfer strategies, which can be used for dynamic matching of multi-source and multi-target machining features with multiple quality labels. The domain invariant data features through dynamic domain adaptation are extracted to deal with data distribution discrepancy between the source and target domains. Finally, the results of multiple comparison experiments show that the proposed method can effectively achieve the accurate quality prediction of the target domain with unlabelled labels and different distributions. Compared with the traditional methods, the proposed method has improved by 8.34%, 7.14%, and 9.09%, respectively, in MAE, RMSE, and score on average.

Analysis of models of measurement and correction of volumetric error of three-axis metal-cutting machine tool

The paper considers the improvement of the quality of products of machined production in connection with the quality of machining of various products and, accordingly, with the accuracy of the equipment used. It is noted that geometrical errors, which account for up to 40 % of the total error of a multi-axis, especially three-axis, metal-cutting machine tool, have the greatest influence on the accuracy of product machining. In order to improve the quality of product machining, it is necessary to correct its parameters according to the measurement information received during machine tool operation, for which the methods of mathematical modelling are used. Models of the volumetric error of a three-coordinate metal-cutting machine tool are developed, taking into account the summands of the first (linear) and second (quadratic) order of smallness. A comparative analysis of the developed models is carried out. The results of experiments on measurement of volumetric errors of the STAN S500 complex and their correction on the basis of the nonlinear theoretical model are given. It is found that in the range of angular errors 0–2.5′ the consideration of quadratic terms of the model in addition to linear ones does not lead to a significant reduction of the volumetric error. It is shown that when processing measurement information of multi-axis metal-cutting machines it is sufficient to limit the consideration of components of the volumetric error not higher than the first order of smallness. The research results are useful for the acceptance and periodic control of the volumetric error of metal-cutting machines, as well as for the programme reduction of the volumetric error.

Intelligent tool wear prediction based on deep learning PSD-CVT model

To ensure the reliability of machining quality, it is crucial to predict tool wear accurately. In this paper, a novel deep learning-based model is proposed, which synthesizes the advantages of power spectral density (PSD), convolutional neural networks (CNN), and vision transformer model (ViT), namely PSD-CVT. PSD maps can provide a comprehensive understanding of the spectral characteristics of the signals. It makes the spectral characteristics more obvious and makes it easy to analyze and compare different signals. CNN focuses on local feature extraction, which can capture local information such as the texture, edge, and shape of the image, while the attention mechanism in ViT can effectively capture the global structure and long-range dependencies present in the image. Two fully connected layers with a ReLU function are used to obtain the predicted tool wear values. The experimental results on the PHM 2010 dataset demonstrate that the proposed model has higher prediction accuracy than the CNN model or ViT model alone, as well as outperforms several existing methods in accurately predicting tool wear. The proposed prediction method can also be applied to predict tool wear in other machining fields.

A Direct Contour Control Method for Free-Form Surface Machining Trajectories Based on Coordinate Transformation

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

EDM-Grinding composite machining and drilling of polycrystalline diamond tool

ABSTRACT Polycrystalline diamond (PCD) is widely used for micro tools due to its high hardness and wear resistance. However, micro PCD tools face challenges in machining with high accuracy and efficiency. Therefore, electrical discharge machining grinding (EDM-grinding) composite machining was proposed. The effects of pulse discharge parameters on surface quality and microhole drilling performance of PCD tool were investigated. The results show that the surface roughness of helix flute and flank face of the fabricated PCD tool were 0.125 µm and 0.158 µm, respectively, as well as the inner wall roughness achieved 0.331 µm after drilling of microhole. The average current displayed a greater effect on the machining quality of PCD tools, with tool breakage in excessive average current. The preferred pulse discharge parameters were determined as an open-circuit voltage of 95 V, an average current of 1400 mA, a pulse width of 400 ns, and a pulse interval of 2000 ns.

A biomimetic micro-texture based on shark surface for tool wear reduction and wettability change

Aiming at the problems of severe tool wear and difficulty in ensuring machining surface quality during the cutting process of Ni-base superalloy, a micro-texture design method resembling a shark surface is proposed, which simplifies the skin teeth on the shark surface into a diamond shaped structure with ribs. Different micro-textured samples were prepared using femtosecond laser, and surface wettability characterization and friction and wear experiments were conducted to study the wear reduction and anti wear effects of micro-texture. The parameter combinations with the maximum contact angle and the minimum coefficient of friction were selected. The wear mechanism of YG8 carbide alloy and GH4742, as well as the wear reduction mechanism of microstructure were revealed. Biomimetic cutting tools were prepared and their effectiveness in controlling tool wear, reducing cutting forces, and improving machining quality was verified through milling experiments.

Research on Tool Wear Monitoring Based on Enhanced Convolutional Neural Networks

Abstract Identifying the wear status of cutting tools during the machining process is essential because failure to promptly replace severely worn tools can significantly impact the quality of workpiece machining. Presently, machine learning methods are predominantly utilized for monitoring cutting tool wear status. However, these methods rely on manual feature extraction and exhibit low accuracy. This study introduces a novel RRP-Net model, built upon the RepVgg and ResNet frameworks, integrating a parameter-free self-attention mechanism called SimAM to expedite the model’s solving speed without increasing parameters. Within the foundational module of the model, a structural reparameterization approach is employed to transform the multi-branch structure during training into a single-branch structure during validation. This method not only enhances model accuracy but also accelerates the model validation process. The publicly available cutting data from PHM2010 is employed for model training and validation. The findings demonstrate that RRP-Net surpasses classical convolutional neural network models in identifying cutting tool wear status within the PHM2010 dataset, achieving an average accuracy of 98.65% and enhancing recognition accuracy on relevant datasets by 2.41%. To verify the model’s practical applicability, specificity and recall during the Break stage are computed at 99.73% and 98.18%, respectively, affirming the model’s exceptional robustness and stability. The heightened accuracy and efficiency of RRP-Net further broaden its applicability within the industrial domain.

Multi-physics field coupling analysis of in-situ laser-assisted micro-imprinting of micro tapered holes

Micro tapered holes find extensive use in various domains including detection, infusion, lighting, etc. This study has investigated the deformation behavior and machining quality of micro-tapered holes generated by in-situ laser-assisted micro-imprinting (In-LAI) using a diamond indenter. The effects of in-situ laser-assisted action on the edge bulge at the inlet, residual stress at the outlet, and microcracks at outlet of a micro tapered hole have been analyzed by using a coupled optical-thermal-force multiphysics field model. Replacing conventional Gaussian heat sources with laser light intensity distributions obtained from optical-thermal coupling model analysis. Compared to ambient temperature, Laser intensity at the tip of the diamond indenter under operating conditions decreased by 11.59 %. The in-situ laser-assisted action has reduced the force by 13 %, reduced the edge bulge at the inlet of the micro tapered hole by 57.3 %, increased the residual compressive stress at the outlet by 47.6 %, and suppressed the generation of microcracks at the outlet. The results show that In-LAI is an effective technique for micro-tapered hole machining, and the multi-physical field coupling research method also provides implications for other in-situ laser-assisted machining (In-LAM) techniques.

Study on cell wall deformation in ultrasonic cutting aluminum honeycomb by straight-blade knife

Aluminum honeycomb sandwich structure has been widely used in the aeronautic and astronautic fields. As the core part, aluminum honeycomb needs to be machined but defects are easily generated. Ultrasonic cutting is an advanced machining technology for honeycomb materials due to improved machining quality. However, ultrasonic cutting aluminum honeycomb by straight-blade knife is usually accompanied by cell wall deformation, which results in poor machining quality. To facilitate the industrial use of ultrasonic cutting aluminum honeycomb with a straight-blade knife, a finite element (FE) model was developed, and experimental studies had been performed. The effects of the blade-inclined angle and lead angle of the straight-blade knife were studied by analyzing the cutting force, the stress and deformation in the cutting zone. Results showed that the cell wall deformation was significantly suppressed when cutting with a corresponding blade-inclined angle and a lead angle. Meanwhile, effects of ultrasonic cutting parameters on the cell wall deformation were also studied, indicating that a well-machined cell wall could be obtained when cutting with large ultrasonic amplitude.

Research on High-Precision Measurement Method for Small-Size Gears with Small-Modulus.

Small-modulus gears, which are essential for motion transmission in precision instruments, present a measurement challenge due to their minuscule gear gaps. A high-precision measurement method under the influence of positioning errors is proposed, enabling precise evaluation of the machining quality of small-modulus gears. Firstly, a compound measurement platform for small-modulus gears is developed. Using a 3D model of the measurement system, the mathematical relationships governing motion transmission between various components are analyzed. Secondly, the formation mechanism of gear positioning error is revealed and its important influence on measurement accuracy is discussed. An optimization method for spatial coordinate transformation matrices under positioning errors of gears is proposed. Thirdly, the study focuses on small-sized gears with a modulus of 0.1 mm and a six-level accuracy. Based on the aforementioned measurement system, the tooth profile measurement points are collected in the actual workpiece coordinate system. Then, gear error parameters are extracted based on the established models for tooth profile deviation and pitch deviation. Finally, the accuracy and effectiveness of the proposed measurement method are verified by comparing the measurement results of the P26 gear measuring center.

Research on Tool Wear and Machining Characteristics of TC6 Titanium Alloy with Cryogenic Minimum Quantity Lubrication (CMQL) Technology

Titanium alloys are crucial in precision manufacturing due to their exceptional properties, but traditional machining methods lead to tool wear, deformation, and high costs. Conventional cooling fluids reduce heat but cause environmental issues, necessitating more sustainable solutions. Cryogenic Minimum Quantity Lubrication (CMQL) technology, using liquid nitrogen or carbon dioxide with minimal amounts of cutting fluid, offers an eco-friendly alternative that reduces machining temperatures and friction. This study tested the TC6 titanium alloy under conventional and CMQL conditions, focusing on tool wear, surface quality, and machining efficiency. Results showed that CMQL significantly decreased tool wear and surface roughness, with a 42% reduction in surface roughness during drilling and a 20–30% efficiency increase. The findings highlight CMQL’s potential to improve machining quality and efficiency while promoting environmentally friendly practices in the industry.

FRKVF: High-accuracy motion interpolation for polishing operations using fourth-order Runge-Kutta and velocity flexibility planning

Traditional interpolation algorithms often fail to meet the precision requirements of ultra-precision machining when applied to super-precision polishing machines. Moreover, the processing of fused silica glass optical components is frequently threatened by mechanical impacts due to their fragility. In order to address this issue, this paper proposes a high-precision motion interpolation method based on fourth-order Runge-Kutta and velocity-flexible planning. This method is designed for open-architecture small multi-axis optical polishing machines to polish quartz glass. The algorithm initially employs composite Simpson's rule to calculate the lengths of sub-paths within the polishing trajectory. Based on these length values, flexible velocity planning is executed to ensure the smooth continuity of velocity, acceleration, and jerk during motion interpolation. This reduces the risk of mechanical impacts that could damage the components during the machining process. The introduction of the adaptive fourth-order Runge-Kutta method significantly enhances the parameter point calculation accuracy of NURBS curves. The incorporation of adaptive principles also maintains a higher processing speed, thereby greatly improving processing efficiency. This method comprehensively addresses both the precision of curve interpolation and execution efficiency. Finally, experimental validation is conducted on an open-architecture small multi-axis optical polishing machine. The proposed method based on FRKVF not only mitigates mechanical impacts resulting from discontinuous acceleration, thereby ensuring the machining quality of optical components, but also satisfies the high-precision requirements for processing fused silica optical elements.

A variable modal decomposition and BiGRU-based tool wear monitoring method based on non-dominated sorting genetic algorithm II

ABSTRACT In the metal cutting process, it is important to realize the effective monitoring of tool wear to ensure the quality of parts machining. A variable modal decomposition (VMD) and bi-directional gated recurrent neural network (BiGRU) based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for tool wear monitoring (TWM) problem. The method takes the envelope entropy and kurtosis of the decomposed signal as the objective function, and iteratively optimizes the number of decomposition layers k and the penalty factor α of the VMD by NSGA-II to obtain the optimal solution set. Meanwhile, an evaluation method of entropy weight- technique for order preference by similarity to ideal solution (EW-TOPSIS) is proposed to obtain the optimal parameter combinations in VMD. The VMD decomposed signals are screened by calculating the correlation coefficients, the time domain and frequency domain features of the screened signals are extracted, and the screened signals and features are mirrored. To realize the in-depth mining of the feature information of time series data, a BiGRU deep learning monitoring model is established. By comparing three classification methods, it has been proven that this method has achieved better results in monitoring tool wear state.

Development of an analytical model to investigate the effect of tool geometry and cutting parameters on the milling performance of Inconel 718

This research proposes a new analytical method to predict cutting forces of end milling considering various tool geometry and cutting parameters, aiming to achieve enhanced stability in machining processes and superior quality in machined surfaces. The end-milling process was modeled using the unequal shear zone theory of oblique cutting, where the helical cutting edges were discretized into multiple oblique cutting edges, and the coordinate transformation method was employed to analyze the geometric and force relationships among them. Then, the influence of helix angles on cutting force, machined surface quality, tool life, and wear patterns in the milling process of Inconel 718 was investigated. These findings provide valuable insights that can be utilized to optimize the design of variable helix tools.