Machining Error Research Articles

Simulation of motion error compensation of CNC with multi-axis linkage

Motion errors can significantly affect the machining accuracy of MACNC. To reduce the motion errors in the operation of CNC, an error compensation algorithm is proposed in the study. Firstly, the motion error measurement and identification method is proposed, and in this way, the MECA based on the third spline fitting is proposed Finally, the effectiveness is verified by simulation experiments. The proposed error measurement method has high measurement accuracy, and the MECA can effectively reduce the machining error value, and its processing time is only 0.012s. The measurement accuracy of the error measurement method proposed in the study reached 98.95%, which was 4.49% and 4.88% higher than the minimum measurement accuracy based on wavelet denoising and genetic algorithm, respectively. When the number of iterations was 5, all three measurement methods achieved the minimum measurement accuracy, and the minimum measurement accuracy of the proposed measurement method was 91.00%, Compared with the minimum measurement accuracy of 93.65% and 94.26% based on wavelet denoising and genetic algorithm, it has decreased by 2.65% and 3.26%, respectively. When disturbed, the accuracy rate of the error compensation algorithm proposed by the research is 93.9%, which is 2%, 2% and 3.5% higher than the minimum values of 91.9%, 91.9% and 90.4% of the three error compensation algorithms based on BP neural network, Particle swarm optimization algorithm and genetic algorithm, respectively. The above results show that the MECA can effectively achieve the error compensation of MACNC, with a fast processing speed, which can effectively workshop Machining efficiency and quality.

Tool path generation with a uniform residual error distribution considering tool contour error for ultra-precision diamond turning

Tool path generation strategy plays a crucial role in improving the efficiency and surface quality of ultra-precision diamond turning mainly by influencing residual errors. However, current approaches in tool path generation have not achieved a uniform distribution of residual error due to the unexplored and significant impact of tool contour error on residual error. Therefore, this study introduces a novel tool path generation algorithm for ultra-precision diamond turning, aiming for a uniform residual error distribution with consideration of tool contour error. The tool contour is measured off-machine, fitted, and modeled to achieve its actual cutting-edge contour at a submicron scale. An estimation model is established to calculate residual error on the machined surface considering the tool contour error. A tool path generation approach is then proposed to achieve uniform residual error distribution by adaptively controlling stepovers. Then the proposed stepover is smoothed by cubic Hermite polynomial interpolation to avoid undesired tool path fluctuations. To further improve the machining accuracy, a spiral tool path is generated with proper discretization parameters to control chord error. Finally, a prototype software system is developed to optimize the tool path as well as predict the residual and machining error across the entire machined surface. Experiment results demonstrate a significant reduction of approximately 40 % in both residual error and machining error compared to unoptimized results.

Deformation prediction in flank milling of thin-walled parts based on cutter-workpiece engagement

Due to the weak rigidity of thin-walled parts, deformation inevitably occurs due to the influence of cutting forces during the machining process, making it challenging to achieve high machining accuracy. To address the above problem, it is necessary to conduct advanced predictions to determine the machining errors in thin-walled parts. This paper proposes a force-induced deformation prediction model based on cutter-workpiece engagement (CWE). The thin-walled structure is divided into two substructures, removal volume and workpiece. The finite element (FE) model of removal volume between any two discrete feed positions can be constructed based on CWE. At each feed position, the birth-death element method is employed to simulate actual material removal, updating the workpiece's stiffness. In the simulation process, utilizing node offset techniques to modify the cutter-workpiece contact zone, ensuring a high level of geometric consistency between the workpiece's FE model and its actual machining shape. Finally, the proposed method is proven via flank milling experiments. The machining errors predicted using the method proposed in this paper exhibit higher accuracy.

Beam dynamics design of the superconducting section of a 100mA superconducting linac

A high-power superconducting linac with an energy of 30[Formula: see text]MeV and a beam current of 100[Formula: see text]mA has been proposed and designed. The primary challenge lies in beam loss control and a robust lattice structure to ensure stable operation. This paper discusses the physics design study, design principles and simulation results considering machine errors. Extensive multiparticle simulations (a cumulative statistic of [Formula: see text] macroparticles) demonstrated that this linac, operating at 100[Formula: see text]mA, could maintain beam losses lower than 1[Formula: see text]W/m in error scenarios.

A Mobile Hybrid Robot and Its Accuracy Issue in Machining of Large-Scale Structures

Accurate and efficient manufacturing of large-scale structures is an important and challenging issue. To this end, a mobile hybrid robot named CraftsRobot is designed and developed for in-situ machining the local features of large-scale structures. The mobile hybrid robot comprises a mobile vehicle, a guide rail, a two degree-of-freedom (DoF) mechanical arm, and a five-DoF light-weight parallel module. The interaction mechanism between the parallel module and the mechanical arm is disclosed to build the error prediction model. On this basis, a model predictive compensation method is proposed. In each servo cycle, the command pose of the parallel module is modified and under the effect of the interaction-induced error superimposed on the modified command pose, the parallel module will reach the target pose. The results of simulation and experiment both verify the effectiveness of the compensation model. After compensation, the mobile hybrid robot is applied to machine the features on a typical large-scale structure and the machining errors are within 0.04 mm. The experimental results show that the designed robot can machine local features of large-scale structures in situ and meet the precision requirements. Together, this study provides a novel approach to achieve accurate and efficient manufacturing of large-scale structures.

Computerized Generation and Surface Deviation Correction of Ruled Surface for Face Gear Drives

In order to solve the problems of low efficiency and complex cutting tools in conventional face gear machining, this paper presents a machining method of ruled surface face gears with conical cutters and proposes a new pinion to correct deviation and its machining method. Firstly, the mathematical models of ruled surface face gears and conical cutters are established, the motion rules of the conical cutter are derived, and the influence of basic parameters on the tooth surface deviation between ruled surface and conventional surface is analyzed. Secondly, for the sake of correction of tooth surface deviation, reverse conjugation is applied to the ruled surface to obtain a corrected pinion. On the basis of hobbing cylindrical gears, the purpose of machining corrected pinions is achieved by increasing CNC motions. Finally, the manufacturing process is simulated by VERICUT software, the results demonstrate that the machining error of ruled surface and pinion do not exceed 10 μm, and through LTCA, the meshing performance of the ruled surface face gear pair is basically the same as that of conventional face gear pair, proving the feasibility of replacing the latter with the former. This study provides a new manufacturing method for face gears.

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement.

Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.

Smoothness-oriented motion planning method for milling robot considering workpiece set-up optimization

Robots are attracing increasing attention in the machining field owing to their great flexibility, vast workspace and low cost. Functional redundancy exists when robots are engaged in milling tasks. Redundancy resolution be considered not only in milling robot motion planning, but also in workpiece layout. A two-step motion planning method is proposed to optimize the trajectory and the workpiece set-up simultaneously to improve planning efficiency with less computational burden. In the first stage, a bilevel optimization technique is proposed that can plan the motion from a global perspective and search the optimal workpiece layout. In the second step, based on a novel smoothness index, a modified whale optimization algorithm combined with the differential evolution algorithm (DE-WOA) is proposed to resolve redundancy under robot and machining error constraints. Experiments demonstrate that the proposed method can enhance the quality of surface machining while avoiding the impact of path length on computational complexity.

Electromagnetic performance of generalized deployable log periodic dipole antenna with mechanical errors

The primary contribution of this paper is to investigate the effect of mechanical errors on the electromagnetic (EM) performance of the generalized deployable log-period dipole (GDLPDA) antenna. The concept of the “GDLPDA” is introduced first. Additionally, an electromechanical error analysis model is developed to assess the effects of dipole machining errors and deformations on the EM performance of GDLPDA. Based on this model, positional errors in two directions along the feedline are considered. Furthermore, an electromechanical error analysis model considering dipole rotational errors on cross-polarized electric fields is established. The actual positional and rotational errors are simulated to analyze the EM performance of GDLPDA. The simulation results show that the dipole positional and rotational errors can degrade the EM performance. Specifically, positional errors reduce gain and pointing accuracy, along with an increase in the 3 dB beamwidth. Rotational errors generate cross-polarization fields, decreasing the level of the co-polarization amplitude. This insight can help antenna design engineers to optimize GDLPDA to minimize the adverse effects of error on performance.

Design and Machining of a Spherical Shell Rotor for a Magnetically Levitated Momentum Ball

Ball-shell rotors with non-standard shapes, non-uniform conductive coatings, and eccentric masses machined by conventional processes constrain the improvement of levitation and torque accuracy of magnetically levitated momentum balls. This paper focuses on the machining method of multilayer ball-shell rotors to develop a ball-shell rotor with a standard shape and uniform conductive coating, which can improve the levitation and torque accuracy of magnetically levitated momentum balls. In this paper, a machining method for multi-coated ball-shell rotors is proposed. The machining process combining hemispherical surface and workpiece is adopted, and the whole sphere is assembled by threading, which effectively reduces the machining error. The influence of the cutting depth and feed rate of the tool on the cutting force of the ball shell was analyzed through the cutting force model. The effect of cutting force on the deformation of the ball shell was analyzed by the finite element method. The superiority of the machining method was verified by measuring the dimensions of the ball shell with a coordinate measuring machine. Compared with the traditional machining process and assembly method, the proposed ball-shell rotor machining method effectively improves the dimensional accuracy, reduces the center of mass to center of mass deviation, and ensures the levitation accuracy and output torque accuracy of the magnetically levitated momentum ball. Measurement results show that the diameter values of the pure iron ball shell are between 98.694 and 98.707 mm with a machining error of ±0.007 mm, and the diameter values of the spray-painted ball shell are between 99.490 and 99.510 mm with a machining error of ±0.01 mm. The machining static equilibrium of the pure iron ball shell and the spray-painted ball shell is good by the static equilibrium test method.

Workload Analysis Pada Operator Mesin Trulaser Trumpf 3030 Di Pt. Xyz Menggunakan Metode Full Time Equivalent (Fte)

This research aims to analyze the workload of TruLaser Trumpf 3030 machine operators at PT. XZY using the Full Time Equivalent (FTE) Method. Data collection was carried out through interviews, direct observation, and lead time measurements in the Preparation Department, especially on the Trumpf TruLaser 3030 Machine. The results of the analysis showed variations in FTE values ​​over 3 days for 1 machine operator for 8 working hours. Although the majority of FTE values ​​are below the overload value, there is one case where the FTE value on September 20 2023 is 0.861, below the required standard. The conclusion shows that there is no problem of work overload for the Trumpf TruLaser 3030 machine operator, however, overtime or overtime is planned by the PPIC/Planner team to achieve production targets. Factors such as machine speed, machine errors, equipment delays, and hampered production processes cause operator idle time. The recommended solution is to maintain and repair machines regularly, plan a more efficient work flow, prepare materials well, improve facilities for material handling, and carefully plan the production process. To overcome idle time, it is recommended to provide additional work such as training for interns or administrative work during shift changes or the next day.

Research on the Hobbing Processing Method of Marine Beveloid Gear

Due to the particular structure of the beveloid gear, it cannot be directly hobbed by an ordinary gear hobbing machine. The existing processing method is complex and has a high cost. Therefore, the mass production and industrialization of beveloid gears are limited. To improve the machining efficiency and accuracy of processing beveloid gears, we proposed a hobbing method via the modification of ordinary hobbing machines. At first, we completed the derivation and calculation of the relevant processing parameters of the beveloid gear based on the study of the structural characteristics of the beveloid gear and the principle of hobbing machining. Then, we proposed and designed a beveloid gear hobbing method, and the modification of the ordinary hobbing machine was completed by using a hanging wheel mechanism in synchronous belt type. Finally, we completed the actual hobbing of the beveloid gear, and the feasibility of the proposed method was verified. After that, we analyzed the machining error of the trial-produced beveloid gear; the results showed that the accuracy of the trial-produced beveloid gear met the 6-level standard, which also verified the accuracy of the proposed method.

Prediction of machining errors for free-form surfaces based on an improved grey model (1,N)

An improved grey model (GM) for predicting free-form surface machining errors was established to address the low measuring efficiencies of coordinate measuring machines (CMMs) and the low prediction accuracy of the GM(1,1) model. A number of points on a free-form surface are measured with a CMM, and machining errors are obtained. Ideas from metabolic methods and the GM(1,N) prediction model were combined. To reduce the impact of random fluctuations of the machining errors, a Markov prediction model was then used to correct the fitted results for the residuals and obtain the predicted results of the machining errors for free-form surfaces, thus improving the prediction accuracy of the model. The predicted results of the metabolic GM(1,1) and metabolic GM(1,N) models were then compared. The experimental results showed that the combination of metabolic theory, a grey model, and Markov theory effectively improved the prediction accuracy.

Optimization of wire spark erosion machining of Grade 9 titanium alloy (Grade 9) using a hybrid learning algorithm

Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi’s approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model’s capability by demonstrating that the model’s predicted values were in close confirmation to the actual values.

Voxel-based end milling simulation of machining error induced by elastic deformation of tool and workpiece

Voxel-based end milling simulation of machining error induced by elastic deformation of tool and workpiece

A high-precision and efficient adaptive alignment method for Cassegrain dual mirror optical system based on machine learning algorithms

The Cassegrain telescope is widely used in aerospace exploration equipment, characterized by compact structure, complex optical path, and high imaging quality. However, due to the difficulty in establishing an accurate correspondence between the relative pose and imaging quality of a multi mirror group with real machining errors, the current Cassegrain telescope assembly process is very difficult, with blind operation, time-consuming, and low accuracy. This article proposes a high-precision adaptive alignment method for Cassegrain dual mirror optical systems based on machine learning algorisms, and an experimental adaptive alignment system with uncoupled degrees of freedom is developed. Firstly, the overall architecture of the adaptive alignment method is proposed consists of detection, calculation and alignment modules. In the detection module, the Zernike polynomial coefficient of wavefront aberration of the optical system is detected online through the interferometer, meanwhile the pose coordinates of the secondary mirror is accurately fed back through a 6-DOF nanoscale micro mechanism. In the calculation module, machine learning algorithm is applied to build a nonlinear mapping model between the Zernike coefficient and the pose coordinates of the secondary mirror. In the alignment module, the pose coordinates of the secondary mirror can be forced to adjust to the target position. Then, during the real alignment process, the Zernike coefficient test data of the optical system alignment process is monitored in real time, and the nonlinear mapping model is employed to calculate the pose coordinates and then the misalignment of the secondary mirror. Finally, the alignment module is driven to execute the pose correction according to the calculated misalignment value, realizing a high-precision adjustment of the Cassegrain dual mirror optical system. Experimental results shows that the average alignment time cost can be dramatically reduced from around 7 days using the current manual alignment method to just 30 minutes using the proposed adaptive alignment method for achieving a current standard alignment accuracy of wavefront aberration root mean square (RMS) less than 0.1λ, which greatly improves the assembly efficiency. This study proposes a new method for high-precision and efficient alignment of optical systems based on artificial intelligence and contributes to the efficiency improvement for optical assembly.

Uncertainty-aware error modeling and hierarchical redundancy optimization for robotic surface machining

Industrial robots are commonly utilized in in-situ machining, but their inherent limitations in stiffness and positioning accuracy can pose challenges in achieving precise freeform surface milling. Previous research primarily focused on robot stiffness optimization and positioning error prediction separately, neglecting the uncertainty of robot errors and the modeling of surface profile errors. To address these issues, this work introduces a novel evaluation method for surface profile errors in robotic milling. It combines geometric error prediction models, based on kinematic and stiffness models, with non-geometric errors using Gaussian process regression. Moreover, collaborative optimization of multiple redundancies in robotic surface milling systems is crucial for reducing machining errors effectively. To achieve this, this work proposes a collaborative optimization method for optimizing the robot’s posture change sequence and the workpiece’s orientation, considering the motion constraints and error requirements of the robot. Among them, this work introduces the improved Whale Optimization Star Algorithm (WOA*) method to address the optimization problem of coupling robot posture and workpiece’s orientation. The proposed method’s effectiveness is confirmed through simulations and experiments, which clearly demonstrate its capability in improving error prediction and machining accuracy in robotic milling.

Thermal analytical modeling of machine tool structural components via dual-layer equivalence

Precision machine tools are sensitive to thermal effects. Thermal deformation of machine tool structural components can lead to changes in the relative pose between the spindle tool and the workpiece, causing thermally induced machining errors. Gaining insight into the mechanistic correlation between critical thermal condition parameters of heat sources and thermal errors in machine tools is essential to elevate the design of machine tool thermal attributes. To address this issue, this paper proposes a dual-layer equivalent analytical modeling method for characterizing the thermal properties of structural components. The construction of the temperature field analytical model for the dual-layer equivalent structure is achieved by setting and handling different interlayer heat flows and temperature boundary conditions. The dual-layer equivalent approach simplifies complex structural components into top and bottom layers, providing a more flexible and accurate capability for characterizing the thermal properties of structural components. This modeling method enables rapid prediction and evaluation of machine tool thermal characteristics. Furthermore, based on this analytical approach, the calculation method of sensitivity coefficient of thermal power to thermal error is proposed to elucidate the mapping relationship between different thermal condition parameters and thermal errors. This method facilitates the fast and accurate prediction of six thermally induced errors under various thermal conditions. Experimental results demonstrate that the key point temperature deviation predicted using the dual-layer analytical modeling method is less than 0.8 °C, and the thermally induced axial linear error deviation is less than 1.8 μm. The dual-layer analytical modeling approach introduced in this study presents a versatile technique applicable to enhancing the design and control of machine tool thermal characteristics, holding significant potential for advancing the stability of machine tools thermal accuracy.

Optimized Surface Adjustment Method for Large Deployable Cable Network Antennas

Large deployable mesh reflectors typically require high surface accuracy and have large apertures. The accumulation of machining errors in a large number of cables can lead to significant deviations between the final manufactured surface and the theoretical surface, particularly due to the large number of cables involved. Adjusting the mesh reflector is often necessary to meet the surface accuracy requirements. However, traditional surface adjustment methods can be challenging to converge and time-consuming. To address this issue, our paper proposes an engineering surface based on measured manufacturing errors as a substitute for the theoretical surface. This method can save time on the coarse adjustment of surface accuracy for large deployable mesh reflectors, reducing the overall surface accuracy adjustment time by 30%.

Detection of Intelligent Machine Fault using Deep Learning Classification

Researchers have long been interested in developing fault detection methods for rotating machinery, and engineers and scientists are increasingly focusing on artificial intelligence-based approaches. When using other signal processing techniques to extract fault characteristics or categorize fault features, artificial neural networks especially deep learning-based techniques—are widely employed. The methodologies and these studies are closely related. This deep learning classification to detect intelligent machine errors. This developed a deep learning algorithm’s technique for the classification of machine faults. To facilitate the process, the input dataset is pre-processed. Researching the observed data is part of the data analysis process known as data pre-processing. The dataset is fed into the segmentation block following pre-processing. K-value to precisely identify the size and shape of the faults, morphological operations are employed in conjunction with segmentation for image segmentation. It attempts to maintain as much difference between the clusters as well as much similarity between the intra-cluster data points. The deep learning method includes the classifications of long-short-term memory networks (LSTM). When it comes to fault diagnosis in that area, LSTM classifier is designed to categorize faults according to their type.