Machining Error Research Articles

Research on frozen sand mold casting technology for complex thin-walled aluminum alloy castings

With the growing emphasis on environmental sustainability and the modernization of the manufacturing sector, the pursuit of eco-friendly practices within the foundry industry has become a critical objective. This paper proposes an innovative solution for environmentally friendly casting: the digital frozen sand mold casting technology. To evaluate its effectiveness, this study analyzed complex thin-walled grid rudder aluminum alloy castings. A comparative analysis was conducted between the binder jetting 3D printing and frozen sand mold (sand mold milling) casting processes. By utilizing Anycasting software, the research explored the filling and solidification processes of ZL101 grid rudder castings under varying mold conditions, aiming to predict the distribution and severity of shrinkage defects. Validation of this approach indicated a machining error of ±0.25 mm for frozen sand molds. The dimensional accuracy of the grid rudder castings achieved a casting tolerance grade of 9 (CT9), and the direct recovery rate of used sand exceeded 98 %, thus positioning this method as a viable alternative to 3D-printed resin sand. Additionally, the study assessed the economic feasibility of frozen sand mold casting technology in terms of resource consumption and production efficiency. The research findings hold significant relevance, offering valuable insights and guidance for traditional casting enterprises seeking to adopt sustainable development practices through the application of leveraging frozen sand casting technology.

Machining error compensation of free-form surfaces by combination of ordinary kriging method and mirror compensation method

ABSTRACT Improving the accuracy and efficiency of free-form surface machining is a common goal of modern manufacturing. In this study, a compensation method combining the ordinary kriging method based on sequential quadratic programming method optimization (SQP-OK) and the mirror compensation method for machining errors of free-form workpieces was applied, with the aim of significantly improving the workpiece machining accuracy. Machining errors on the surface of the workpiece were predicted utilizing the SQP-OK after the workpieces were completed with semi-finishing machining based on the on-machine measurement (OMM) results of the workpiece. Based on the prediction result, the mirror compensation method was applied to calculate the compensation points. Free-form workpieces were compensated for by modifying the NC code for semi-finishing. To verify the effectiveness of the proposed method, two free-form workpieces were machined with computer numerical control (CNC) compensation, applying the proposed method and the method modifying the overall machining allowance. The experimental results revealed that the compensation method proposed in this paper reduced the average value of the machining errors decreased by 28.6% in absolute value, 31.1% in maximum undercut, and 28.8% in maximum overcut for the free-form workpiece, respectively, compared with the compensation method of modifying the overall machining allowance.

Comparison of Citrated Whole Blood to Native Whole Blood for Coagulation Testing Using the Viscoelastic Coagulation Monitor (VCM Vet™) in Horses.

Viscoelastic monitoring of horse coagulation is increasing due to its advantages over traditional coagulation testing. The use of a point-of-care viscoelastic coagulation monitor (VCM Vet™) has been validated for use in horses using native whole blood (NWB) but has not been assessed using citrated whole blood (CWB), a technique that might have advantages in practicality and precision. Blood was collected from 70 horses, tested in duplicate immediately using NWB (T0), and stored at room temperature as CWB for testing in duplicate at 1 (T1) and 4 (T4) hours after venipuncture for comparison to NWB. Of these horses, 20 were classified as clinically healthy and used to determine reference intervals for CWB at 1 and 4 h post-collection. There were clinically relevant differences in all measured viscoelastic parameters of CWB compared to NWB meaning that they cannot be used interchangeably. These differences were not consistent at T1 and T4 meaning the resting time of CWB influences the results and should be kept consistent. The use of CWB in this study also resulted in more machine errors when compared to NWB resulting in measurements that might not be interpretable.

A Study on the Coarse-to-Fine Error Decomposition and Compensation Method of Free-Form Surface Machining

To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation.

Physics-informed and data-driven hybrid method for transmission accuracy design optimization of planetary roller screw mechanism

The planetary roller screw mechanism (PRSM) faces an ever-increasing precision transmission demand in current advanced equipment. The relationship between machining errors and transmission accuracy remains elusive due to the over-simplified physical models and small-sample experimental datasets. This work proposes a physics-informed and data-driven hybrid strategy for PRSM transmission accuracy evaluation and tolerance optimization. In the physical model, a PRSM transmission accuracy model is developed to calculate transmission error that considers 16 machining errors in eccentric, nominal diameter, pitch, flank angle, and roller consistency. In the dataset establishment, thread profile measurements and dynamic leadscrew inspections are conducted for the machining error and transmission accuracy data acquisition. A data augmentation approach combining the physical model with the generative adversarial network is utilized to predict travel deviation, variations, and axial backlash and estimate machining error contribution with the small-sample experimental dataset. It is firstly found that the roller consistency of nominal diameter significantly affects PRSM travel variation V2π with a 17.3 % importance value. With the developed framework, the key tolerances for screw, roller, nut, and roller consistency are optimized toward a typical precision transmission requirement using the non-dominated sorting genetic algorithm. It also provides a tolerance grade recommendation table with PRSM transmission accuracy level in engineering practice.

Thermal environment optimization and process product design of CNC grinding machine based on improved neural network

In the machining process of CNC grinding machine, the non-uniformity of heat source often leads to the deformation of the workpiece, the intensification of wear and the machining error, so the optimization of thermal energy environment has become an important research direction to improve the machining performance. The aim of this study is to optimize the thermal energy environment of CNC grinding machine by improving the neural network algorithm, so as to achieve the goal of improving the machining precision and surface quality. We hope that by establishing thermal environment model, we can deeply analyze the characteristics of thermal energy distribution and put forward effective optimization measures. The improved neural network model is used to model the heat energy distribution of CNC grinding machine. The accuracy of the model is improved by introducing multi-layer perceptron and convolutional neural network. At the same time, combined with the experimental data, training and testing are carried out to verify the effectiveness of the model. Genetic algorithm and fuzzy control are used to adjust the thermal energy environment and obtain the best process parameters. The experimental results show that the prediction accuracy of the improved neural network model is significantly improved, and the prediction error of the model is significantly reduced compared with the traditional method. After thermal optimization, the surface roughness of the workpiece is reduced, the dimensional accuracy is significantly improved, and the overall processing efficiency is significantly improved. By improving the neural network model, the thermal energy environment of CNC grinding machine is effectively optimized, and the machining precision and surface quality are successfully improved.

Experiences of Therapeutic Plasma Exchange in geriatric patients at a tertiary care hospital in western Maharashtra: An observational study

BackgroundGeriatric patients undergo Therapeutic Plasma Exchange (TPE) for variety of indications that poses its unique challenges and complications. We present our experience with TPE in geriatric patients in terms of diseases encountered and adverse events, if any, witnessed during the procedure. MethodsA prospective analysis of TPE procedures was done for a period of 01 year. Patients above 60 years of age with a range of 01–02 volume exchange per procedure were enrolled in the study. Baseline procedure investigations such as complete blood count, serum electrolytes, coagulation profile, and transfusion-transmitted infection screening were carried out before the TPE procedure. All TPE procedures were performed using a continuous-flow cell separator. All these patients were followed-up, and findings related to patients and procedures were documented. ResultsA total of 21 eligible enrolled patients underwent 85 procedures in the present study. Neurological disorders (80.9%, n = 17) were most common indication, with Guillain–Barre syndrome (42.8%, n = 9) being tthe most replaced by second common, followed by myasthenia gravis (23.8%, n = 5). Barring one, all other patients showed significant clinical response. The overall incidence of complications during the study was 80.9% (n = 17), among which majority were patient-related (94.11%, n = 16). However, only one procedure was terminated due to a machine error, and there were no procedure-related deaths. ConclusionThe current study showed that TPE in geriatric patients gives substantial results in terms of clinical improvement but is fraught with multiple complications. However, in trained and expert hands, these complications can be managed effectively.

Measurement and machining error assessment method for cycloid pump rotor profile based on STEP

Abstract The contour shape and machining precision of the rotor of an cycloid pump directly determine the sealing performance of the pump cavity. Precise measurement of contour machining errors is a crucial step in the manufacturing process. However, rotor contour curves are often designed by enterprises based on actual engineering needs, leading to inconsistency between the measured baseline contour and the designed contour, thus failing to accurately reflect the actual machining condition of the rotor. Therefore, this paper proposes a method for measuring the contour of the cycloid pump rotor and evaluating machining errors based on the standard for the exchange of product (STEP) files necessary for product design in manufacturing. Firstly, utilizing NX/OPEN API functions, contour coordinate points and unit normal vectors are obtained from the rotor’s STEP design data. Subsequently, by planning the process of measuring the closed continuous contour of the rotor, the transformation between the workpiece coordinate system and the measurement coordinate system is achieved to position the tested rotor, followed by analyzing the influence of different starting points on error measurement and evaluation determines the optimal starting point. Finally, a measurement sampling point strategy is established to collect data points of the actual machining contour of the rotor. By achieving the best matching between the actual machining contour and the measured baseline contour, a contour deviation calculation model is established. Utilizing gauge blocks enables accurate assessment of pitch deviation, radial runout, and tooth-to-tooth distance deviation, resulting in assessment results that better fit the actual meshing situation of the rotor. Experimental results verifies consistency between the measurements of the one-dimensional probe and Gleason measurements. This theory and method not only expand the measurement range of the one-dimensional probe but also provide more accurate and efficient guidance for the flexible machining of the rotor.

Finite element analysis for the measurement error of electrostatic accelerometer due to the electrode misalignment

Abstract The electrostatic accelerometer is the key payload of many space missions, such as satellite gravity measurements, space gravitational experiments, with a typical precision requirement from nano-g to femto-g. The measurement error model analysis is an important issue for the electrostatic accelerometer with numerous error sources. The traditional method by theoretical analysis is complicated to evaluate the complete measurement error models quantitatively especially when the machining and assembly errors of the sensor head are considered. Limited by the earth gravity and seismic noise which is much higher than the intrinsic noise, the complete error model of electrostatic accelerometers can also hardly be tested on ground. In this paper, the method by using finite element analysis of the sensor head is studied. The simulation model for an electrostatic accelerometer sensor head is built, and the multi-conductor capacitance matrix data is simulated. The accuracy of the capacitance simulation is evaluated in three ways including the convergence check of the capacitance with the mesh size, the symmetry verification, and the sensitivity comparison with the theoretical model. Finally, the accelerometer measurement model is analyzed based on the simulation data, using the case of rotating installation misalignment of the upper electrode as an example. The measurement model and error items of one horizontal axis and one rotating axis are quantitatively evaluated. The method proposed in this paper provide a new effective approach to the measurement model analysis of the electrostatic accelerometers in complex working scenarios both in orbit and on ground, which could be helpful to enhance the performance and the efficiency of application.

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.

Equivalent Error Based Modelling for Prediction and Analysis of Measuring Accuracy in 3-Axis FXYZ Coordinate Measuring Machines from Position, Repeatability and Reversibility Errors

AbstractThe measuring accuracy of coordinate measuring machines (CMMs) will be affected by the different geometrical and dynamic errors, including the deviations associated to the axis displacement, the working table and the part to be measured. This work is focused on the analysis of the influence of the position errors, repeatability errors and reversibility errors in 3-axis FXYZ coordinate measuring machines, and it will be developed by a numerical model that is known as EE-based stochastic model. This model implements a new error index that is named equivalent error (EE), which will integrate the totality of machine errors of the CMMs and will allow a global description of all these error sources by means of a unique error parameter. The results obtained by this numerical model have been compared with the application of a traditional method, and it was probed that the EE-based model makes possible an increase of a 13.29% in the linear modelling of the performance of CMMs from the machine errors considered in this work, which implies a relevant improvement for the analysis and description of the effect of the distinct error sources on the achievable measuring accuracy of CMMs. For this reason, the EE-based model will be of special interest for industrial applications such as the quality control to be applied inside the production systems dedicated to manufacture mechanical components of high dimensional accuracy.

Optimum error design and 5-axis CNC machining of preloaded roller-gear-cam in roller-drive system

Roller-gear-cam (RG-cam) drive systems in machine tool rotary tables provide enhanced rotation accuracy and superior transmission efficiency due to their preloaded design. However, achieving a controllable preload engagement between the roller and RG-cam requires the use of 5-axis CNC machining using a non-formed tool, which inevitably introduces machining errors. Accordingly, this study proposes a comprehensive method for the design, analysis, and manufacture of a precision RG-cam system on a conventional 5-axis CNC machine tool. Notably, the proposed method enables the machining errors to be controlled and minimized. The feasibility of the proposed approach is demonstrated by machining a prototype roller-drive system on a 5-axis CNC machine. The accuracy of the machined RG-cam is evaluated in accordance with the ISO 230-2 standard. It is shown that the forward and reverse rotational errors of the RG-cam over a single revolution are less than 10 s.

Wheel path planning for flute grinding of non-cylindrical solid end-mills based on circular arc projection

Flute grinding is a crucial process in solid end-mill manufacturing, because the generated flute parameters, i.e. helix angle, rake angle, core radius, and flute width, have significant effects on the cutting performance of end-mills. However, the flute grinding of non-cylindrical solid end-mills is still challenging due to the variation of the flute parameters along the tool axis. This paper proposes an accurate and efficient method of wheel path planning for flute grinding of non-cylindrical solid end-mills based on circular arc projection. The wheel path determined by the new method can accurately generate the designed helix angle, rake angle, core radius, and flute width, even if all of these parameters vary along the tool axis. The helix angle and rake angle are ensured by the conjugate condition between the wheel surface and the cutting-edge curve. Based on circular arc projection, equivalent errors of the core radius and flute width are introduced to quantify the machining errors of the two parameters, without the need to predict the flute profile. Simulations and experiments are conducted to validate the proposed method. Three types of non-cylindrical end-mills i.e. tapered end-mill, tapered barrel end-mill, and barrel end-mill, are considered in the validation. The measured results indicate that the generated flute parameters coincide well with the designed ones at different axial positions of the end-mills.

Strategy for Compensation of Thermally Induced Displacements in Machine Structures Using Distributed Temperature Field Control

Thermal deformation is a major source of machining errors in modern machine tools. In addition to optimising the machine structure, correcting the axis position values in the numerical control is a common measure to reduce these errors. Another possibility is to directly influence the temperature field of the machine tool in the process, which requires a complex thermo-elastic modelling approach as well as appropriate thermal actuation and measurement capabilities. This paper presents a strategy for controlling the temperature field based on the eigenmodes of the thermal system. The various aspects of the concept are explained using a finite element model of an exemplary structural component. The basis is the modal analysis of the thermal system, which allows the temperature field to be described by independent discrete states. In addition to the placement of thermal sensors and actuators, this work focuses on the design of a suitable control approach. Transient simulation results are used to clearly demonstrate the performance of this method.

Integrated error modeling and evaluation of the effect for the Wolter-I grazing incidence focusing mirror imaging quality

The imaging quality determines the performance of the Wolter-I grazing incidence focusing mirror. When the design parameters are specified, the factors affecting the imaging quality are mainly machining and assembly errors; finding the error sources and evaluating the main errors are critical fundamental issues to achieve process optimization and control and improve imaging quality. This paper constructs a model of surface distribution error based on Nurbs surface reconstruction by non-contact measurement. It proposes an integrated error modeling method based on the surface distribution error model, taking into account the typical assembly errors such as decenter error, defocus error, and tilt error formed during the assembly process. First, the theoretical modeling and analysis of imaging quality based on ideal surface shape are carried out, and the sensitivity analysis of each error is carried out by using the Monte Carlo ray tracing method; then, an integrated optical simulation model containing assembly errors based on the surface distribution error is established, and the imaging quality of the focused spot is evaluated; finally, the experimental validation is carried out. The results of the experiment and the integrated optical simulation model show overall consistency, where the decenter error and defocus error have less influence, and the tilt error has the greatest impact, with the 10′ tilt error causing a decrease in the angular resolution of the focused spot of about 50″. The method can provide effective modeling and computational approaches to support the optimization of the precision assembly process and quality control of the Wolter-I grazing incidence focusing mirror.

Computer Numerical Control Machining Simulations and Experimental Analysis of a Novel C-Gear

C-gear is a novel gear transmission system, with no existing special machining and cutting tools to realize its construction. In this study, we employed a 5-axis computer numerical control (CNC) machining center to construct a C-gear, while solving the prototype manufacturing problem at the experimental research stage. We deduced the C-gear mathematical model and studied the three-dimensional (3D) modeling methods based on the analysis of its generation principle. The C-gear processing technique using the 5-axis CNC machining center and the tool path designs were established using the VERICUT and MASTERCAM softwares, and the C-gears were experimentally constructed. The maximum overcut and residual of the gear positioning hole surface were both found to be 0.094 mm. Moreover, the maximum overcut on the tooth surface and root were 0.02 and 0.03 mm, respectively, and the maximum residual on both the tooth surface and root was 0.03 mm. The machining overcuts and residual amounts of the proposed method were better than those of reference [14]. The existing overcut and residual errors included the errors generated by surface fitting during the 3D modeling of the gears. Therefore, the machining errors of the physically constructed tooth surface are expected to be smaller than the theoretical value, suggesting a high feasibility of the proposed method to realize the machining of C-gears.

Accuracy analysis and improvement methods for revolving parts in counter-rotating electrochemical machining with continuous radial feeding

Counter-rotating electrochemical machining (CRECM) stands out as an efficient and cost-effective method for machining aero-engine casings. To maintain stability in the CRECM process equilibrium state, continuous feeding of the cathode tool is essential. However, this continuous feeding approach with single direction of rotation may result in poor roundness and reduced machining accuracy. This paper aims to analyze the sources and rules of roundness error and asymmetry in CRECM. Based on simulation results, a method involving periodic reverse rotation of electrodes with continuous feeding is proposed for CRECM. This approach aims to mitigate the cumulative machining error, thereby significantly improving machining accuracy. Experimental results validate the efficacy of the proposed method at a rotation speed of 0.2 rpm. The roundness error is markedly reduced from 0.19 mm to 0.04 mm, resulting in a more symmetric convex structure. Additionally, the sidewall taper angle is reduced from 14.5 degrees to 6.4 degrees. This underscores the effectiveness of the proposed method in enhancing machining accuracy.

Sensitivity Analysis of Injection Mass Flow to the Inlet Orifice Radius of a GDI Injector Nozzle

It is more difficult to accurately control the cycle fuel mass of a gasoline engine as the injection pressure increases, and the difference in the injection mass flow will become intense due to the machining error of GDI injector structural parameters. In order to guarantee the uniform cycle fuel mass of the GDI injector, this paper investigates the effect of the inlet orifice on the injection mass flow and the sensitivity of inner-flow characteristics in the GDI injector nozzle to the inlet orifice radius by the single factor analysis method. The results show that the local resistance of the injector nozzle will decrease and the discharge coefficient will rise due to the increase in the inlet orifice radius; the sensitivity of cycle fuel mass to the inlet orifice radius is most strong at r = 0.02 mm, and its accuracy should be improved when grinding the chamfer of the nozzle inlet; the sensitivity coefficient of the inlet orifice radius will rise up, and the machining accuracy of the inlet orifice radius should be improved as there is an increase in injection pressure.

Neural-network-based trajectory error compensation for industrial robots with milling force disturbance

ABSTRACT Industrial robots are increasingly used in advanced manufacturing fields such as aerospace due to their high efficiency and low cost. In robotic machining applications, deviation of the tool centre point trajectory from the desired path due to load disturbances acting on the end-effector of an industrial robot can result in poor dimensional accuracy and surface quality of products. Therefore, improving robot trajectory accuracy under external load disturbances is extremely important. This study proposes an error compensation methodology using neural networks optimized by a hybrid marine predators-grid search algorithm to compensate for robot trajectory error. Two neural networks are developed: one for predicting load disturbances and the other for predicting trajectory errors in robotic machining. The milling experiment results show that the compensated robot trajectory errors in x, y, and z directions are reduced by 65%, 76%, and 77% respectively, which proves the effectiveness of this method in improving the robotic milling accuracy.

Exploratory factor analysis for machining error data of compressor blades based on dimensionality-reduced statistical analysis method

Abstract Various machining errors inevitably occur on aero-engine compressor blades, including leading-edge contour error, trailing-edge contour error, camber contour error, and more. The current complexity surrounding the numerous ma-chining error types and their obscure interrelationships imposes immense effort for aerodynamic analysis and hin-ders overall error control. Thus, elucidating error correlations to achieve error dimensionality reduction is imperative. This study pioneers a dimensionality reduction approach via factor analysis to conduct a comprehensive statistical analysis of 13 types of blade machining errors. The proposed technique can categorize the 13 errors into three groups, each dominated by a distinct common factor. To validate the accuracy of the extracted factors, the factor scores of the three identified latent factors are computed. Cluster analysis is then performed on the factor scores, and the clustering results are visualized by t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, boot-strap resampling establishes the 95% confidence intervals for the factor scores. Capitalizing on the grouping struc-ture uncovered by factor analysis, multiple linear regression models are built for the errors within each group, and then, a preliminary conjecture is made about the potential key error types for each group of errors based on the re-gression coefficients. This hypothesis is then evidenced by the statistical analysis of cross-section profile error data of 28 blades. The present work can not only optimize machining processes but also relax tolerance requirements and diminish the effort of aerodynamic analysis.