Accuracy Of Milling Research Articles

Effect of tool orientation on surface roughness and dimensional accuracy in ball end milling of thin-walled blades

AbstractIn the aerospace industry, high-precision components like impellers and blisks present considerable challenges in machining due to their intricate geometries and the need for reliable performance. Blades, often classified as thin-walled parts with complex, free-form surfaces, are crucial in ensuring the efficiency and safety of aircraft engines. Their geometries and materials require strict control over cutting parameters, such as tool path and orientation, to avoid deformation and maintain surface integrity. This study investigates the impact of tilt angle variation during ball end milling of Ti6Al4V thin-walled parts. Four different tilt angles (15°, 30°, 45°, and 60°) were analysed to evaluate their effect on dimensional accuracy and surface roughness. The results show that tilt angle significantly influences surface quality and dimensional precision. A tilt angle of 30° achieved the best balance between surface finish and dimensional tolerances, with flatness values ranging from 0.038 mm at 30° to 0.101 mm at 60°, representing a 152.5% increase in flatness. The findings provide practical guidelines for optimizing ball end milling of thin-walled components, emphasizing the importance of tool orientation in reducing deformation and enhancing surface quality.

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.

Optimization of Robot Milling Accuracy Based on a New Stiffness Performance Index

Optimization of Robot Milling Accuracy Based on a New Stiffness Performance Index

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.

Identification of milling information and cutter-workpiece engagement in five-axis finishing of turbine blades based on NURBS and NC codes

Five-axis milling is frequently used for machining of turbine blades. Cutting force can cause deformation and vibration of blades, which will eventually affect the milling accuracy and surface quality. Modeling of cutter-workpiece engagement (CWE) is a key process to predict cutting force, especially for complex tool geometries and workpiece surfaces. In this paper, a method to modeling ball-end cutter CWE boundaries for blades with free-form surfaces is presented. Firstly, the NURBS theory is employed to get the surfaces before and after finishing. Secondly, milling information such as coordinate systems is extracted from NC codes. Thirdly, the CWE boundaries are extracted based on the NURBS surfaces and extracted information. The proposed model is a combination of discrete and analytical method. Finally, the performance of CWE model is examined from computational accuracy and efficiency, and is verified through milling test. The proposed method provides a visualization of complex engagement between tool and blade in five-axis milling process, and can be further used in simulation of milling force, vibration and deformation.

Robotic Milling: A New Era of Machining

Abstract-Robotic milling is considered as an alternative solution of CNC milling, however there are significant difference between these two processes. CNC milling technologies enabled new possibilities in constructing complex digitally generates architectural shapes but have restricted working area and produce shape limitation. Industrial robot are an appropriate technology for developing flexible and reconfigurable, manufacturing systems. Which contribute to perform various automated operation such as milling, cutting, drilling , grinding, surface finishing etc. the work presented in this paper tackles some specific aspects regarding the cam techniques used for robotic milling to overcome the shape and size limitation of CNC In this paper we provide some comparative study about how changing the various parameter leads to change in functioning of robot to obtain good surface finish and to enhance the accuracy and repeatability of robotic milling. This document is designed to provide nowadays technology, future potential and technical constraints about the robotic milling and its advantages.

Accuracy of on-site teleoperated milling with haptic assistance

PurposeIn bone surgery specialties, like orthopedics, neurosurgery, and oral and maxillofacial surgery patient safety and treatment success depends on the accurate implementation of computer-based surgical plans. Unintentional plan deviations can result in long-term functional damage to the patient. With on-site teleoperation, the surgeon operates a slave robot with a physically-decoupled master device, while being directly present at the operation site. This allows the surgeon to perform surgical tasks with robotic accuracy, while always remaining in the control loop.MethodsIn this study the master- and slave-side accuracy of an on-site teleoperated miniature cooperative robot (minaroHD) is evaluated. Master-side accuracy is investigated in a user study regarding scale factor, target feed rate, movement direction and haptic guidance stiffness. Scale factors are chosen to correspond to primarily finger, hand, and arm movements. Slave-side accuracy is investigated in autonomous milling trials regarding stepover, feed rate, movement direction, and material density.ResultsMaster-side user input errors increase with increasing target feed rate and scale factor, and decrease with increasing haptic guidance stiffness. Resulting slave-side errors decrease with increasing scale factor and are < 0.07 mm for optimal guidance parameters. Slave-side robot position errors correlate with the feed rate but show little correlation with stepover distance. For optimal milling parameters, the 95th percentile of tracked slave-side position error is 0.086 mm with a maximal error of 0.16 mm.ConclusionFor optimal guidance and milling parameters, the combined error of 0.23 mm is in the range of the dura mater thickness (< 0.27 mm) or mandibular canal wall (~ 0.85 mm). This corresponds to safety margins in high-demand surgical procedures like craniotomies, laminectomies, or decortication of the jaw. However, for further clinical translation, the performance and usability of on-site teleoperated milling must be further evaluated for real-life clinical application examples with consideration of all error sources in a computer-assisted surgery workflow.

Dynamic Modeling and Stability Prediction of Robot Milling Considering the Influence of Force-Induced Deformation on Regenerative Effect and Process Damping

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping.

Ensuring accuracy of contour milling on CNC machines

Contour milling is characterized by quasi-stationary, which leads to the occurrence of a machining error caused by elastic deflections of the machining system. Moreover, such an error cannot be eliminated by sub-adjusting the control program "for size", since it not is constant and changes along the contour in a wide range. To ensure the accuracy of contour milling, a new method of combined control is proposed, which uses a posteriori information of machine verification measurements of the machined surface and a priori information of modeling the material removal process. In case of single manufacturing, the allowance on the last pass is divided into two parts, and after machining the first part, verifications are performed, then modeling and machining of the second half according to the corrected control program. In mass manufacturing, verifications are performed on the first part, and all subsequent ones are machined according to the adjusted control program. To obtain the necessary a posteriori information, the technology of machine verifications on CNC-machine with a three-coordinate probe according to standard control programs is proposed, and it is enough to perform two measurements or one, in the presence of surfaces inclined to the coordinate axes of the contour. A computer program was created, the core of which is a mathematical model of the process, which reproduces the structural diagram of the machining system closed behind two elastic deflections. The primary control file is loaded in G-codes with measurement data, and as a result of the process simulation, a new control file for the CNC-machine tool is formed, which eliminates the error from elastic deflections of machining system. Preliminary tests showed the possibility of increasing the accuracy of contour machining by more than 3 times.

A Multi-Objective Optimization Method of a Mobile Robot Milling System Construction for Large Cabins

Constructing mobile robot milling systems with multiple mounting surfaces for large cabins still has several unsolved issues, such as huge economic and time costs, unpredictable milling accuracy and milling time. Hence, a multi-objective optimization method for constructing a mobile robot milling system of large cabins is proposed in the current paper. Firstly, mathematical models of constructing the system and the optimization objective function are established. Thereafter, a multi-objective optimization method for the mobile robot milling system construction based on NSGA-II (Fast Non-dominated Sorting Genetic Algorithm) is proposed. Finally, feasibility and validity of the proposed method are verified through comparing the optimization result with two practical mobile robot systems. Results show that the proposed method is able to estimate different combinations’ milling accuracy, cost and time consumption.

An accuracy evolution method applied to five-axis machining of curved surfaces

Currently, some high-value-added applications involve the manufacturing of curved surfaces, where it is challenging to achieve surface accuracy, repeatability, and productivity simultaneously. Among free-form surfaces, curved surfaces are commonly used in blades and airfoils (with a teardrop-shaped cross-section) and optical systems (with axial symmetry). In both cases, multi-axis milling accuracy directly affects the subsequent process step. Therefore, reducing even insignificant errors during machining can improve the accuracy in the final production stages. This study proposes an “evolution” method to improve the machining accuracy of curved surfaces. The key is to include compensation for the machining error after the first part through profile error measurement. Thus, correction can be applied directly after the manufacturing programming is fully developed, achieving the product with the minimum number of iterations. Accordingly, this method measures the machining error and changes only one key parameter after the process. This study considered two cases. First, an airfoil in which the clamping force was corrected; the results were quite good with only one modification in the blade machining case. Second is an aspherical surface where tool path correction in the Z-axis was applied; the error was effectively compensated along the normal vector of the workpiece surface. The experimental results showed that the surface accuracy increased from 44.4 to 4.5 μm, and the error was reduced by 89.9%, confirming that the accuracy of the machine tool and process had achieved “evolution.” This technical study is expected to help improve the quality and productivity of manufacturing highly accurate curved surfaces.

The influence of CAD model continuity on accuracy and productivity of CNC machining

Efficient and productive manufacturing of freeform shapes requires a suitable three-dimensional CAD model at the entrance to the CAM system. The paper deals with the impact of NURBS or B-spline CAD model geometric continuity on the accuracy and productivity of 5-axis ball-end milling of freeform surfaces. The relationship between a different order of CAD model geometric continuity and the quality of the toolpath generated in CAM system is analysed and demonstrated on an example of a Blisk blade profile. In order to reveal the effect of CAD geometry on the quality of the machined surface, linear interpolation of cutter location points, i.e. piecewise linear discrete toolpath, is considered. Also, no further smoothing of the toolpath is applied. The distance of the cutter location points is commonly used as the indicator of toolpath quality. In addition, the discrete curvature of a linear discrete toolpath is introduced here, and its dependence on the curvature and continuity of the underlying CAD model is demonstrated. In this paper, it is shown that increasing the order of CAD model geometric continuity significantly eliminates sharp changes in the distance of cutter location points, and smoothes the discrete curvature of the toolpath. Finally, it is experimentally verified that increasing the continuity of the CAD model from G0 to G3, while maintaining the same cutting conditions, leads to an increase in workpiece accuracy and a reduction in machining time, without the need to smooth the toolpath generated in the CAM system.

Comparison of the accuracy of resin-composite crowns fabricated by three-dimensional printing and milling methods.

This study aimed to compare the dimensional accuracies of three-dimensional (3D)-printed and milled resin-composite crowns, and to determine acceptable abutment-tooth shapes for printing. Four first-molar abutment models were prepared: the master model form and three models with sharp occluso-axial line angles. Crowns were designed on each abutment using computer-aided design software. The drill-offset value was set at 0.0 or 0.5 mm to evaluate the effect on the dimensional accuracy of milling. A digital light processing-based 3D printer was used to fabricate 3D-printed crowns. Milled crowns were fabricated by wet-milling. The trueness was evaluated by superimposing the design data. Regardless of the abutment form, 3D-printed crowns showed higher accuracy with fewer marginal discrepancies than milled crowns. Milled crowns showed significant dimensional deviations, especially at cusps. Moreover, offset correction resulted in grooves on the internal surface of milled crowns with negative deviations, which were especially evident in crowns for the sharp models.

Effectiveness Of Cad Cam Milled Vs Dmls Titanium Framework for Hybrid Denture Prosthesis - A Systematic Review

Objectives: The main objective of this study is to critically review articles that have used printed and milled CAD CAM designed metal as a framework for hybrid denture and evaluate its clinical effectiveness.Search Strategy: An electronic search was performed in PubMed, Google scholar and Cochrane Library until December 2021. The assessment of the articles was done using selection criteria.Selection Criteria : All prospective/retrospective studies on conventional, milled and printed metal frameworks for implant supported hybrid dentures were included in this systematic review.Case series, case reports, conference paper, animal studies were excluded from this review.Data Collection: We used standard methodological procedures to select studies and collect the data. The risk of bias was evaluated and findings were synthesised.Results: Out of the 22 articles selected 5 were excluded based on title and abstract. Out of the remaining 17 studies, 10 were excluded based on inclusion and exclusion criteria, and finally 7 were selected on the basis of core data. Based on the observation from these 7 studies we were able to substantiate the difference between the conventional, milled and printed metal frameworks for hybrid denture prosthesis.Conclusion: DMLS as well as CNC milling are viable and better alternatives for production of titanium bar for implant supported hybriddenture frameworks, when compared to the conventional production methods. DMLS and CNC milling both have a few pros and cons over each other. While the accuracy of CNC milling is greater, its fabrications costs make it difficult to bring into daily practice. DMLS is cheaper, faster and although less accurate, clinically acceptable. Thus further research in this topic is imperative to best understand its potential.

Optimal tool path generation and cutter geometry design for five-axis CNC flank milling of spiral bevel gears

Abstract Computer numerical control milling provides greater flexibility and universality for machining complex gears compared to dedicated gear manufacturing. A critical challenge in popularizing the use of five-axis flank milling to spiral bevel gears is to achieve acceptable machining accuracy that ensures the meshing performance of the finished gears. Previous studies, which used approaches such as gear design modification, using multiple tool paths, and end milling, failed to resolve this issue. Thus, this paper proposes a computational scheme to improve the machining accuracy of five-axis flank milling of spiral bevel gears by optimizing the tool path and cutter geometry. The scheme minimizes the geometric deviations between the machined surface and original design using heuristics and optimization algorithms. A simplified tooth contact analysis method was developed to quantitatively evaluate the contact path of the meshing gears. The simulation results of real gears show that the proposed scheme outperforms previous methods in reducing machining errors and further enhance the meshing performance by optimizing the design of form cutters. This work developed an effective approach for flexible and low-cost manufacturing of complex gears.

X-ray absorption measurements at a bending magnet beamline with an Everhart–Thornley detector: A monolayer of Ho3N@C80 on graphene

X-ray Absorption Spectroscopy (XAS) is used for measuring monolayer quantities of Ho3N@C80 endofullerene molecules on graphene at a low flux bending magnet beamline. The total electron yield is measured with an Everhart–Thornley detector. In comparison to sample current measurements with the same noise level, our approach reduces data acquisition time and radiation dose by a factor of 25. As the first application of this setup, we report temperature-dependent measurements of the Ho M45 edge with per mille accuracy. This documents the advantages and capabilities of an Everhart–Thornely detector for XAS measurements under low x-ray flux.

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components

High-accuracy prediction and compensation of industrial robot stiffness deformation

Industrial robots (IRs) are promising options for machining large complex structural parts due to the higher flexibility, larger operating space, and lower cost compared with multi-axis machine tools. However, the relatively low posture-dependent stiffness and large stiffness deformation of IRs significantly deteriorate the contour accuracy of milling in which the cutting force is large generally. It is very complex to achieve a precise stiffness model and predict stiffness deformation of IRs because of the joint clearance, drift of zero-position, and other nonlinear factors. The conventional stiffness model of IRs only takes each joint as a constant linear torsion spring into consideration and ignores other difficult-to-model factors, which leads to low-accuracy identified results and thereafter induces deformation prediction errors. The data-driven approach can be used to obtain an accurate stiffness and deformation model, but a large amount of experimental data is required and it will cost enormous time and effort. In order to circumvent the experimental data deficiency and difficult-to-model issue, a simulation-driven transfer learning method named Adaptive Domain Adversarial Neural Network with Dual-Regressions (ADANN-2R) is designed for robot deformation prediction. Amounts of coarse deformation data, which are generated by the conventional stiffness model, are regarded as source data. And few real deformation data, which are obtained by deformation experiments, are regarded as target data. The Dual-Regressions are designed after the feature extractor, and the weighting parameters are adjusted adaptively to tackle the different magnitude of the regression loss and domain discrimination loss. The ADANN-2R aligns the simulated source data and real target data to perform adversarial training, and an accurate target deformation predictor is achieved. Experimental results indicate that the proposed ADANN-2R can obtain high-accuracy prediction with few real data compared with the conventional stiffness model. Compared with the path without deformation compensation and the pre-compensated path using the conventional stiffness model, the maximum position error of the pre-compensated path using the proposed ADANN-2R is reduced by 78.12% and 32.45%, respectively.

Linear Motion Error Evaluation of Open-Loop CNC Milling Using a Laser Interferometer

Abstract The usage of computerised numerical control (CNC) machines requires accuracy verification to ensure the high accuracy of the processed products. This paper introduces an accuracy verification method of an open-loop CNC milling machine using a fringe counting of He–Ne laser interferometry to evaluate the best possible accuracy and functionality. The linear motion accuracy of open-loop CNC milling was evaluated based on the number of pulses from the controller against the actual displacement measured by the He–Ne fringe-counting method. Interval distances between two pulses are also precisely measured using the He–Ne interferometry. The linear motion error and controller error can be simultaneously evaluated in sub-micro accuracy. The linear positioning error due to the micro-stepping driver accuracy of the mini-CNC milling machine was measured with the expanded uncertainty of measurement and was estimated at 240 nm. The experimental results show that linear motion error of the open-loop CNC milling can reach up to 50 μm for 200 mm translation length.

Contour error-based optimization of the end-effector pose of a 6 degree-of-freedom serial robot in milling operation

• A pose optimization method is proposed to improve the contour accuracy of robotic milling by minimizing contour errors induced by the low posture-dependent stiffness. • The deformation errors induced by cutting forces are predicted based on the robot stiffness model and simulated cutting forces. • A comprehensive contour error index is defined by integrating the statistical properties of contour errors. • Under the constraints of kinematics and smoothness, the optimal tool poses are obtained by minimizing contour error index through a discrete searching algorithm . • Simulation and experimental results show that the contour accuracy can be significantly improved using the proposed strategy when compared with traditional methods. Robot manipulators with 6 degree-of-freedom (DOF) have advantages of good flexibility and large working space. However, the relatively low stiffness deteriorates the machining accuracy and stability in robotic milling tasks, which restricts the widely application of robotic milling in industry. To increase the machining accuracy, taking advantage of a redundant degree of freedom of the robot, this paper presents a method to optimize the pose of the milling robot by taking the contour error as the optimization index. Firstly, the cutting forces are predicted in advance for a given milling task, and the deformations of the robot end-effector (EE) are calculated based on the stiffness model and cutting forces at all cutting locations (CLs). Secondly, the contour errors of the machined product are calculated based on the deformation errors and the tool path profile. Finally, the optimal tool poses are obtained by minimizing contour error through a discrete searching algorithm under the kinematic and smoothness constraints. Compared with optimization indexes used in existing studies, the main contributions of the proposed index lie on two aspects. First, the cutting force variances at different CLs are considered. Second, the contour accuracy is taken as a direct optimization index. Therefore, the proposed method is able to improve the form accuracy of robotic milling better than existing studies which use stiffness as the evaluation index. Simulation and experiments show that the robot trajectory optimized by the proposed method can significantly improve the machining accuracy compared with un-optimized results and existing studies.