Machining Error Research Articles

Research on Machining Error Control Method Driven by Digital-twin Model of Dynamic Characteristics of Machining System

The position of each component structure in the machining space of the machining system directly affects the machining quality of the workpiece. In this paper, RBF neural network is used to study the spatial dynamic characteristics of the translational axes of five-axis CNC machine tools. It is the basis for building an evolvable knowledge base. Process evaluation, information feedback, and iterative optimization of thin-wall parts were carried out using digital-twin technology. Firstly, the model simulation of the translational machining space of a three-dimensional five-axis CNC machine tool is carried out by using the finite element method. The RBF neural network predicts the natural frequency of a translational machining space dynamic characteristics. It is further used to construct the dynamic characteristic spectrum of the translational space of CNC machine tools. Secondly, the machine tool flush space cutting process is built with a digital-twin system to optimize the iterative mechanism for machining process optimization. Finally, the method’s effectiveness was verified by experiments on milling thin-walled parts on a dual-turntable five-axis CNC machine and by contouring error measurement experiments. The results show that the average error of the optimized thin-wall contour is reduced by 26.9%. The iterative mechanism can continuously optimize the machining error and improve the machining accuracy. The iterative mechanism can continuously optimize the machining error and improve the machining accuracy.

Mathematical Models and System of Intelligent Servo for High-Efficiency Electrical Discharge Assisted Arc Milling on Difficult-to-Cut Materials

Difficult-to-cut materials, such as superalloys and titanium alloys, have been increasingly used in aerospace and other fields. However, commonly used mechanical milling methods for difficult-to-cut materials have problems, including low processing efficiency and severe tool wear. Electrical discharge assisted arc milling (EDAAM) is a novel high-efficiency machining method proposed by the authors to solve these processing problems. The machining efficiency achieved by EDAAM when processing difficult-to-cut materials is much higher than that achieved by mechanical milling and conventional electrical discharge machining (EDM) milling. However, if EDAAM adopts the traditional EDM servo control method, it will easily form deep and large discharge craters on the surface of the workpiece, which may lead to high machining errors and cause the workpiece to be scrapped. To ensure normal processing, low-energy discharge parameters that may reduce the processing efficiency have to be adopted. To address the above issues and achieve the optimal machining condition of difficult-to-cut materials, mathematical models and system of intelligent servo for EDAAM are proposed. An EDAAM discharge information dataset is obtained, and the intelligent recognition and classification method for discharge states is proposed. The EDAAM intelligent servo control system is proposed based on mathematical models created. The experimental results of superalloy Inconel 718 by EDAAM show that, compared with the nonintelligent servo control system, the intelligent servo control system can increase the material removal rate by up to ten times, reduce the relative electrode wear rate by 37%, and avoid the scrapping of the workpiece.

Splicing Measurement and Compensation of Straightness Errors for Ultra-Precision Guideways.

The straightness error of guideways is one of the key indicators of an ultra-precision machine, which plays an important role in the machining accuracy of a workpiece. In order to measure the straightness error of a long-distance ultra-precision guideway accurately, a splicing measurement for the straightness error of a guideway using a high-precision flat mirror and displacement sensor was proposed in this paper, and the data splicing processing algorithm based on coordinate transformation was studied. Then, comparative experiments on a splicing measurement and direct measurement of the straightness error were carried out on a hydrostatic guideway grinder. The maximum difference between the two measurements was 0.3 μm, which was far less than the straightness error of 5.8 μm. The experiment demonstrated the correctness of the proposed splicing measurement method and data processing algorithm. To suppress the influence of the straightness error on machining accuracy, a straightness error compensation algorithm based on error rotation transformation and vertical axis position correction was proposed, and the grinding experiment of a plane optics with a size of 1400 mm × 500 mm was carried out. Without error compensation grinding, the flatness error of the element was 7.54 μm. After error compensation grinding, the flatness error was significantly reduced to 2.98 μm, which was less than the straightness errors of the guideways. These results demonstrated that the straightness error of the grinding machine had been well suppressed.

Effects of build orientation and hatch spacing on high-speed milling behavior of L-PBF 316L stainless steel

Abstract Due to the philosophy of the process, the surface roughness is usually high for the parts produced with laser powder bed fusion (L-PBF) additive manufacturing (AM). Parts produced by this method need surface quality improvement processes for many applications. One of the methods used for this purpose is high speed machining (HSM). HSM is a modern manufacturing technique that offers several benefits, including improved productivity, enhanced product quality, and reduced production costs. In addition, HSM can improve the quality of finished products by reducing machining errors. In this study, samples produced with 316L powder in size of 10 × 10 × 5 mm using three different hatch spacings (60, 70, 80 µm) and building orientations (0°, 45°, 90°) were produced by L-PBF method, and HSM process was applied to these samples. In this context, the present study aimed to investigate the effects of porosity, microstructure and microhardness properties of 316L samples produced by L-PBF method using different hatch spacings and build orientations on cutting forces, surface roughness and burr formation in HSM. When the numerical values of the cutting forces were analyzed in both x and y directions, it was observed that the greatest cutting force occurred in the x direction. While the Fx force ranged from 6.23 to 9.35 N, the Fy force ranged from 4.88 to 8.27 N. It has been determined that as the build orientation increases at the same hatch spacing value, the cutting forces increase due to the increased porosity ratio.

Thermal deformations analysis of large-scale vertical Lathe

This article presents an analysis of thermal deformations in a large-scale vertical Lathe, examining the sources of heat and their impact on the machine’s thermal deformations. The analysis was conducted using both experimental measurements and numerical simulations based on the finite element method. The study highlights the critical role of geometric accuracy, stiffness, and thermal distortions in machine tool design, affecting machining errors. Several machine tool manufacturers implement systems that minimize the impact of thermal distortions using numerical control. However, custom production lacks extensive possibilities, posing challenges in achieving requirements for predictable thermal distortions. This article analyzes the thermal distortions of a custom-produced machine tool and examines their values and consistency with the machine tool’s working components’ movement. The study emphasizes the predictability and directionality of thermal distortions in large-sized machine tools to maintain geometric accuracy and stiffness. Additionally, the study identifies non-technical aspects related to the division of the worktable into component parts, disrupting the axial symmetry of the construction and causing potential deformations of the workpiece due to thermal deformations.

Exploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems

The global automotive manufacturing industry's growth in downtime reductio is substantial, valued at $3272.6 billion USD with a 3.01% growth rate.This growth in downtime reduction underscores the industry's commitment to enhancing efficiency, quality, and overall productivity across its diverse range of operations. Downtime in the braking system assembly line can lead to utilization loss or technical availability loss. In this context, many proactive maintenance strategies are explored but there's limited focus on addressing error-prone machines and utilizing Machine Learning for predicting downtime. The objective is to prioritize downtime reduction through error analysis, critical machine identification, and implementing ML-based solutions.
 This comprehensive research delved into the significance of minimizing downtime in the braking system final assembly line through meticulous data analysis, visualization techniquesand targeted interventions and then identified key issues and achieved tangible improvements in operational efficiency. The analysis revealed significant findings and then employed the Pareto Principle to identify top downtime machines, illustrating their distribution through a Pareto chart of machine defects. Furthermore, Exceptions and Problem Areas were identified utilizing statistical process controls, offering insights into critical error contributors. Notably, a comprehensive exploration of the most prominent downtime machine was undertaken, evidenced by LCL and UCL charts and a Fishbone Diagram detailing causal relationships. The research leverages real-world data involving dates, machine names, and downtime durations to develop a predictive model that aids in proactively managing production disruptions.
 The application of RPN calculations before and after error correction demonstrated a substantial reduction from of 432 to 75, validating the efficacy of the corrective actions. The real-time data was used to build a model that can predict when production machines downtime might happen. This helps us be prepared and manage any possible disruptions in production.The outcome of the project highlights the intrinsic link between downtime reduction and assembly line efficiency, emphasizing the importance of data-driven interventions. This culminated in the resolution of key issues, illustrated by the mitigation of the PCBA Pressin machine and LVDT sensor errors, yielding tangible reductions in downtime and notable productivity improvements. In this direction, exploring AI-driven predictive maintenance holds immense potential for advancing downtime reduction strategies. Leveraging AI algorithms to analyse live data streams from machinery and sensors can enable the detection of patterns indicating imminent failures and can pre-emptively prevent downtime.

A novel method to minimize the five-axis CNC machining error around singular points based on closed-loop inverse kinematics

A novel method to minimize the five-axis CNC machining error around singular points based on closed-loop inverse kinematics

Design and Experiment of MEMS Solid-State Wave Gyroscope Quadrature Error Correction System

A novel gyroscope quadrature error correction system based on quadrature force correction is introduced in this paper. The source of quadrature error of MEMS solid-state wave gyroscope is analyzed. The main source of quadrature error is the inevitable machining error. The gyroscope system model is established, the quadrature error correction method is proposed, and the quadrature error correction system is simulated. Through the comparative test, the gyroscope with quadrature error correction system has better performance. With the addition of quadrature error correction system, the scale factor of the gyroscope changes from 1.13 mV/°/s to 1.02 mV/°/s, the nonlinearity is increased from 1382.7ppm to 795.4 ppm, and the asymmetry is improved from 1532.4ppm to 945.1ppm, the angle random walk (ARW) from 269.1 °/h/√Hz to 39.45 °/h/√Hz, and the bias stability from 46.62 °/h to 6.785 °/h.

Comparison of imputation methods for missing production data of dairy cattle

Nowadays, vast amounts of data representing feed intake, growth, and environmental impact of individual animals are being recorded in on-farm settings. Despite their apparent use, data collected in real-world applications often have missing values in one or several variables, due to reasons including human error, machine error, or sampling frequency misalignment across multiple variables. Since incomplete datasets are less valuable for downstream data analysis, it is important to address the missing value problem properly. One option may be to reduce the dataset to a subset that contains only complete data, but considerable data may be lost via this process. The current study aimed to compare imputation methods for the estimation of missing values in a raw dataset of dairy cattle including 454 553 records collected from 629 cows between 2009 and 2020. The dataset was subjected to a cleaning process that reduced its size to 437 075 observations corresponding to 512 cows. Missing values were present in four variables: concentrate DM intake (CDMI, missing percentage = 2.30%), forage DM intake (FDMI, 8.05%), milk yield (MY, 15.12%), and BW (64.33%). After removing all missing values, the resulting dataset (n = 129 353) was randomly sampled five times to create five independent subsets that exhibit the same missing data percentages as the cleaned dataset. Four univariate and nine multivariate imputation methods (eight machine learning methods and the MissForest method) were applied and evaluated on the five repeats, and average imputation performance was reported for each repeat. The results showed that Random Forest was overall the best imputation method for this type of data and had a lower mean squared prediction error and higher concordance correlation coefficient than the other imputation methods for all imputed variables. Random Forest performed particularly well for imputing CDMI, MY, and BW, compared to imputing FDMI.

Reliability analysis of reusable turbine rotor blisk: An application of parametric modelling method under multi-field coupling

As a critical component of reusable rocket engines (RRE), the cyclic life of the turbine rotor blisk often varies due to uncertainties such as machining errors and other factors. However, considering the difficulty of structural reconstruction and the large number of parameters related to the blade configuration, few studies have considered the blade configuration variation due to machining errors in the reliability analysis process. To investigate the influence of factors in reliability analysis, a reliability analysis framework is established by combining parametric modelling methods with finite element analysis methods under fluid-thermal-solid sequential coupling. The influence of blade lattice changes due to machining errors on the reliability of turbine rotor blisks in reusable rocket engines (RRE) is investigated. The effects of machining errors, material, and load variability on the cyclic life reliability of rotor blisks are effectively quantified. With the suggested method, a reliability analysis is conducted for an RRE turbine rotor blisk, and the effect of the machining error range on the analysis results is investigated. It is found that the tolerance level to meet the high reliability requirements is at least medium for the basic cyclic life requirements of the model. In terms of cyclic life reliability, the more influential random factors are blade axial chord, low-cycle fatigue plasticity coefficient, and low-cycle fatigue ductility coefficient, with sensitivity factors of 29.09%, 24.4% and 22.13%, respectively. This study provides an efficient and reliable modelling method for the reliability analysis of reusable turbine rotor blisks based on machining errors, investigates the influence of the range of machining errors, quantifies the ability of the random factors to influence, and provides valuable insights and recommendations for the reliability design and optimization of reusable turbine blisks.

Reducing micro-machining errors during electric discharge machining of titanium alloy using nonionic liquids

ABSTRACT The study employs nonionic liquids with cryogenically treated tool electrodes to investigate micro-machining errors. An extensive Full Factorial-based experimental campaign is executed involving unmodified process: 12 experiments (kerosene dielectric as baseline for improvement) and modified process: 108 experiments. In modified process, three distinct nonionic liquid types, their concentration, cryogenically treated electrodes, and pulse ON:OFF time are used. All the nonionic liquid-mixed dielectrics have shown noticeable improvement in terms of achieving low machining errors as compared to the conventional dielectric system. In unmodified process, the aluminum and graphite have produced micro machining errors 151.9 µm and 75 µm, respectively. The Desirability Function has recommended (experimentally validated 17.75 µm micro-machining error) the use of cryogenically treated graphite electrode with pulse ON:OFF time of 50:50 µSec and nonionic liquid S-60 with 25 g/L concentration to achieve minimum micro-machining errors with the highest desirability index of 98.60%.

A novel assembly method of aero-engine rotors with minimized spindle inertia

Assembly is an essential part of aero-engine manufacturing, and the assembly quality will affect the engine’s stability and service life. Spindle inertia is an essential factor affecting aero-engine dynamic performance. Assembly can affect the spindle inertia of the aircraft engine, which in turn affects the dynamic performance of the engine. This paper proposes a transfer model of spindle inertia based on machining error transmission, studying the variation law of spindle inertia of multi-stage rotors under the influence of assembly, providing an optimized assembly method with minimized spindle inertia. The influence law of machining errors on spindle inertia is analyzed through the simulation and probability density method. The assembly results of the designed rotors verify the effectiveness of the inertia transfer and assembly model. The results show that the spindle inertia of multi-stage rotors is affected by machining errors, and the spindle inertia can be adjusted by changing the assembly methods. This paper provides a new idea for the transmission and adjustment of spindle inertia of assembly rotors.

Surface texture topography evaluation and classification by considering the tool posture changes in five-axis milling

Surface texture topography affects the performance of industrial parts, such as precision and service life. Different tool postures may lead to various texture topographies in five-axis milling. In this paper, surface texture topography quality and texture trend evaluation indexes are proposed considering the influence of tool posture in five-axis milling. Based on the evaluation, the ranges of tool posture angles corresponding to surface texture topographies of different characteristic are obtained through surface texture topographies classification. The result can provide reference for singularity avoidance in path planning and machining error compensation in five-axis milling. The influence of tool posture change on the surface texture quality could be avoided while adjusting the tool posture angle in the determined range. Firstly, the general tool cutting edge point expression with the typical structure parameters of the end mills is proposed. The general digital model of surface texture topography is established with the improvement of the unit time calculation and computing vertex judgment in simulation. Secondly, several evaluation indexes are proposed to represent the quality and the overall trend of the surface texture topography with digital extraction of indexes realized on the surface digital model. On this basis, the classification of the surface texture topographies and the determination of the range of tool posture angle for each class of textures are proposed. Thirdly, the evaluation and classification of the ball-end milling plane texture topographies are carried out, which divide the texture topographies into zero-inclination angle textures and non-zero inclination angle textures. The surface textures are further divided to detailed five categories with the tool posture angle range determined. Different tool posture angles of the same class are used to generate a texture topography for the comparison to verify the evaluation indexes and the classification result. Finally, the plane cutting orthogonal experiments are carried out. The general surface digital model is verified by the comparison in surface texture characteristic of different types of mills between digital model and machined surface. Based on the ball-end milling plane classification result, the machined surface textures and extracted indexes are compared to verify the effectiveness of the proposed evaluation indexes and the classification result. The fillet-end milling surface textures are further classified and verified based on the method above. In addition, the spherical surface textures of ball-end milling are carried out for further verification.

Post Optical Freeform Compensation Technique for Machining Errors of Large-Aperture Primary Mirror

Large-aperture space optical systems are important tools for observing our planet and conducting deep-space scientific research. More requirements have been put forward for large-aperture optical systems as the depth and breadth of related applications continue to increase. However, achieving the desired surface accuracy on lightweight materials for large-aperture mirror brings higher demands on relevant processing technologies, which increases the processing difficulty, cycle, and cost of large-aperture optical systems. Therefore, it is necessary to develop a new optical system technique with high tolerance for primary mirror machining errors to significantly reduce its machining accuracy requirements. This paper proposes a new optical system technique which introduces a small-aperture free-form surface into the large-aperture optical system’s post optical path. By combining the freeform correction and its misalignment on the system’s aberrations, the system’s wavefront can be adjusted to correct the wavefront distortion caused by the primary mirror’s machining errors. This reduced the machining accuracy requirements of the large-aperture primary mirror by about one order of magnitude, and high-quality imaging with a low-precision primary mirror is achieved.

Research into Dynamic Error Optimization Method of Impeller Blade Machining Based on Digital–Twin Technology

A TC4 impeller blade is a typical weak, rigid, thin–walled part. The contact area between a cutting tool and a workpiece has strong time–varying characteristics. This leads to a strong non–linear variation in cutting load. So, in this kind of part, the processing error is difficult to control. To solve this problem, a method of processing error prediction and intelligent controlling which considers the effect of tool wear time variation is proposed by combining digital–twinning technology. Firstly, an iterative model for digital–twin process optimization is constructed. Secondly, an iterative prediction model of the machining position following the milling force and considering the effect of tool wear is proposed. Based on these models, the machining error of the TC4 impeller blade under dynamic load is predicted. Dynamic machining error prediction and intelligent control are realized by combining the digital–twin model and the multi–objective process algorithm. Finally, the machining error optimization effect of the proposed digital–twin model is verified via a comparison experiment of impeller blade milling. In terms of the precision of milling force mapping, the average error after optimization is less than 8%. The maximum error is no more than 14%. In terms of the optimization effect, the average error of the optimized workpiece contour is reduced by about 20%. The peak contour error is reduced by approximately 35%.

A highly efficient semi-implicit two-field modeling for thermal contact problems with non-Fourier effects

In this paper, we revisit the thermal interface condition of thermal contact prob- lems with a nonzero thermal resistance. We point out that continuity conditions of the flux at the interfaces, which usually adopt the form of temperature gradient, leads to a paradoxical result if one of the adjacent bodies has zero temperature gradient. Specifically, that using the temperature gradient instead of the real heat flux yields a nonphysical temperature profile and fails to depict the thermal jump in cases with zero temperature gradients. We then show that the two-field (tem- perature and flux) formalism, in lieu of the classical single-field (temperature) naturally resolves this paradox. We propose an easy-to-implement semi-implicit scheme for this two-field formulation. The thermal interface problem is formu- lated in the fashion of differential algebraic equations (DAEs), in which the inter- face conditions are treated as algebraic equations constraining the associated heat process governed by differential equations. Our method can precisely fulfill the interface condition with the magnitude of machine error (10−16) and is computationally more efficient (around 8 to 10× faster) since it only needs to solve a very small Poisson-like system. End-to-end 1D and 2D simulations are presented to demonstrate the versatility and efficacy of our new method.

Strategies for translating machine errors in automatically generated texts (using GPT-4 as an example)

The article discusses the strategies of translation of «machine texts» on the example of generative transformers (GPT). Currently, the study and development of machine text generation has become an important task for processing and analyzing texts in different languages. Modern technologies of artificial intelligence and neural networks allow us to create powerful tools for activities in this field, which are becoming more and more effective every year. Generative transformers are one of such tools. The study of generative transformers also allows developers to create more accurate and efficient machine translation algorithms, which improves the quality of translations and improves the user experience. In this context, the features of machine texts created by generative transformers, their patterns, errors and imperfections, which require special translation strategies, deserve special interest. Today we can say that the generation of unique and relevant texts is a routine task that has been automated. Nevertheless, certain restrictions for the use of such texts still exist, in particular, their use requires the use of appropriate translation strategies. The paper proposes the author’s typology of translation strategies, where, taking into account the features of AST, it is proposed to add a substrategy of tertiary-moderation translation.

Research on the method of judging the docking quality of aviation components based on attitude measurement

Butt assembly plays a crucial role in the manufacturing process of aviation products, as its assembly quality directly impacts product performance. Current research on the butt assembly of large cylindrical components mainly focuses on attitude measurement or precise cabin attitude adjustment. However, these studies often overlook the influence of the cabin machining error and the attitude deviation during the docking process, as well as the prediction and assessment method of the docking quality. In this paper, the docking force in the assembly process is used as the evaluation basis of docking quality. The actual geometric docking data model is measured and constructed by a 3D scanner. Through a series of docking experiments, the coupling force data under different spatial attitudes are obtained. The docking quality assessing method of aviation components based on attitude measurement is established by examining the internal relationship between the docking area deviation, the maximum docking deviation distance, the space deviation angle and the maximum relay. This approach advances the quality prediction and assessment technology for large aviation components, offering a novel perspective on the assembly and docking of large aviation structures.

Stacking ensemble transfer learning based thermal displacement prediction system

In the precision machining industry, machine tools are usually affected by various factors during machining, and various machining errors generated accordingly. Where thermal error is one of the most common and difficult to control factors for machine tools. Therefore, in this study, six temperature sensors and an eddy current displacement meter are provided in a machine tool with 4-axis for dataset collection required for the model training, then data are organized and normalized. Next, data are introduced into a variety of learning models and validated by k -Fold cross-validation for predicting those nonlinear factors that affect the errors. At the end, predicted results are summarized and compared to find out the best two model with better predictive performance for pre-trained model of transfer learning. It observes better predicted results from a retraining conducted through applying Multilayer Perceptron (MLP) on these two pre-trained models, wherein MAE value as 0.40, RMSE as 0.52625 and R 2 score as 0.99696 respectively.

Application of fuzzy control based feed rate strategy design in NC machining error automatic compensation

With the continuous development of manufacturing industry, the application range of NC machining technology has been further expanded. The contour accuracy is strongly related to the NC machining quality as a key machine tool performance indicator. Its application efficiency is plainly low as the majority of offline compensation-based contour accuracy adjustments rely heavily on manual experience. Moreover, the isolated research on automatic error compensation and its combination with algorithms does not start with the characteristics of contour accuracy in data processing. Therefore, based on the advantages of strong the robustness of the fuzzy algorithm and the high effectiveness of parameter adjustment, an automatic compensation method for NC machining contour error based on fuzzy control is proposed. The contour error prediction model is designed according to the machining path, and then the automatic compensation strategy for contour error under fuzzy control is designed based on the feed speed. The results showed that under this method, the contour error can reach a maximum of 0.06 and a minimum of 0.025, which was 0.015 lower than the minimum contour error of genetic algorithm. This indicated that the method greatly reduced the CNC machining contour error and improved the contour accuracy, as well as reducing the time cost of contour error compensation, improving the efficiency of contour error compensation, and realizing the automation of error compensation control capability. This is helpful for advancing CNC machining automation technology and supporting the intelligent development of machinery manufacturing.