Tool Deflection Research Articles

Modelling of machined surface topography and anisotropic texture direction considering stochastic tool grinding error and wear in peripheral milling

This paper establishes a new model for predicting machined surface topography and anisotropic texture direction in peripheral milling. Unlike previous models, this model takes into account the largest number of influential factors, i.e., tool setting error (radial offset and axial tilt), static tool deflection, forced vibration, chatter vibration, and stochastic tool grinding error and wear (STGEW). STGEW, quantified by the normal distribution (μ, σ2), is incorporated into the model for the first time. The model predictions for 3D surface topography, anisotropic texture direction, 2D surface profile or roughness agree remarkably well with experimental or published results, under both stable and unstable milling conditions. It is found that static deflection slightly attenuates the effect of tool setting error on roughness. Even machining with fresh tools or tools with very small cutting length (<15 mm) and wear (VB∼5 μm), inherent STGEW creates random scratches with different geometries parallel to feed direction on the milled surface. STGEW expands the difference of roughness in axial direction. STGEW induces an additional surface texture in feed direction, which co-exist with axial texture contributed by feed marks when standard deviation σ to feed rate ft ratio is small. As σ increases, (1) feed marks are gradually obliterated by scratches, and surface texture is only oriented in feed direction; (2) the mean and variance of roughness in both directions increase, and axial roughness prevails transverse toughness at high σ. The study discovers that, STGEW, even with small σ (e.g., σ=[0.01 μm, 0.1 μm]), is an important factor affecting surface topography, anisotropic surface texture direction and roughness that cannot be ignored, especially in the case of low-feed machining, e.g., ft = 0.05 mm, which is commonly adopted in finish-milling of difficult-to-cut materials. The proposed model can be used to reliably and accurately predict surface topography, surface roughness and surface texture direction in peripheral milling operation.

Reliability sensitivity analysis of ball-end milling accuracy

In the process of milling, the setting of parameters has a significant influence on the milling characteristics. If the selection of cutting parameters is not reasonable, it will be difficult to ensure the machining accuracy of the workpiece. Considering the uncertainty of cutting parameters in the milling process, this paper proposes a reliability sensitivity analysis method for angular ball-end milling precision. First, the cutting edge contact section is determined by the discrete event simulation, based on the Z-map method. Then, the milling cutter is considered equivalent to a cantilever beam, while a flexible prediction model, including the tool deformation in the milling process, is established. In order to improve the calculation efficiency of the tool deflection error, the functional relationship between the parameters and the machining error is reconstructed, using kriging technology. Following, the kriging model is used to replace the dynamic model to analyze the reliability sensitivity of milling precision, based on the Monte Carlo method. Finally, both experimental and simulation results show that the tool deflection error model and the sensitivity calculation method, as proposed in this paper, exhibit high precision.

Tool deflection compensation by drive signal-based force reconstruction and process control

Tool deflection is a major cause for shape deviations in milling. Therefore, an approach is presented to compensate tool deflection based on the drive signals of a milling center. First, a real-time capable model is implemented to reconstruct static process forces by using the drive signals of a machine tool. To calculate the actual deflection, the forces are combined with a stiffness model of the tool. Finally, a controller is designed to minimize shape deviation of the workpiece. Based on experimental milling investigations it is shown that tool deflection can be significantly reduced.

3D modeling and experimental investigation for tool deflection in end mill cutting of AISI 1045

The present paper investigates the tool deflection in three dimensions for two different tool diameters during end milling operation and validates the experimental findings with the finite element results. Cutting forces (i.e., radial cutting force, tangential cutting force, axial cutting force) exerted by the tool on AISI 1045 steel substrate under different combinations of cutting parameters (viz. feed rate, cutting speed and depth of cut) were measured using a dynamometer. For more accurate prediction of tool deflection using finite element analysis (FEA), the tool-workpiece contact region was sliced into small segments for the equal distribution of cutting forces. In order to verify the experimentally measured inaccuracy in the cut profile using a coordinate measuring machine (CMM) under different sets of parameters, the experimental results were compared with the predicted values of FEA. From the results, it was observed that the cutting forces were comparatively larger in magnitude for the larger tool diameter. Also, the depth of cut was found to be the most influential parameter among all, whereas the effect of feed rate and cutting speed on profile error was found to be insignificant. Further, the results show good agreement between the predicted FEA simulated tool defection and experimental values.

Investigation of the diameter error when turning thin walled workpieces

Every manufacturing process undergoes geometrical deviations from the defined geometry. In order to produce workpieces within the specified quality, a downstream quality assurance is carried out. This quality assurance is expensive and time consuming. If the geometrical error could be estimated during and after the manufacturing process this quality assurance can be reduced. The geometrical error emerges due to different causes. One of these causes is the deflection of tool and workpiece. This alters the depth of cut and therefore causes a radial error on the machined workpiece. An additional cause is the progressing tool wear during continuous machining, also influencing the depth of cut.This paper presents a method to determine the radial error after turning. The method is based on finite difference calculations, suitable for the investigation of the local error of multi-diameter workpieces. Within the developed model the deflection due to shearing and bending of the workpiece and the tool are examined. Furthermore, the tool wear is considered. Additionally, the calculation for a locally varying depth of cut is presented. The results show that by taking the tool wear into account the model error was decreased from 63.08 % to an absolute error of 5.72 %. Using the proposed model, it is possible to generate relevant data about the geometrical manufacturing error immediately after every machining process.

Analysis of cutting force and vibration at micro-milling of a hardened steel

Micro-milling is a commonly used manufacturing method; however, it is a difficult-to-design process mainly due to the size effect. The main aim of this research is the analysis of the cutting forces, the vibrations, and the dominant frequencies of the micro-milling process in a hardened steel. Full factorial machining experiments were conducted using an AlTiN coated micro end mill. The cutting forces and vibrations were analysed and discussed in detail based on material removal mechanisms, tool deflections, and dynamical behaviours. Moreover, a novel cutting force model was developed, which is adequately able to predict and optimise the cutting force.

Increasing Part Geometric Accuracy in High Speed Machining using Cascade Iterative Learning Control

High speed machining provides high productivity and low machining cycle times. Post machining, there can exist differences between desired and measured part geometry due to tool deflection induced from higher feedrates. Reducing the feedrate leads to an increase in machining time. Using predicted drive responses on a virtual CNC with an integrated surface location error model, this research is the first time Iterative Learning Control (ILC) has been applied to reduce part geometry errors from tool deflection. Validation machining trials demonstrated that the ILC scheme improved machining performance whilst maintaining machining times when compared to a baseline part program.

Real-time compensation of tool deflection using a sensor embedded boring bar with wireless signal feedback to the machine tool controller

Boring bar deflection is a generic problem when machining deep holes to high dimensional tolerances. Reaching the final dimension can be tricky and time consuming because the deflection varies with cutting parameters, insert, and material. Here we use embedded strain sensors to determine the tool deflection, and we compensate for it by adding an overlaid movement to the machine tool x-axis. The compensating value from the tool is sent via Bluetooth to an IPC that communicates with the CNC kernel via the PLC. We investigate and discuss the accuracy and dynamics of this machining system installed in our largest lathe.

A new approach for local cutting force modeling enabling the transfer between different milling conditions and tool geometries

The modeling of modern high performance machining with intermittent cut and varying effective cutting parameters requires a flexible local cutting force prediction. Due to complex tool geometries and varying cutting conditions without a rigid reference system new approaches for the local cutting force decomposition are applied, investigated and compared. The force decompositions are based on the separation of the effective cutting speed into normal and tangential components to adequately consider the locally acting mechanisms. Regression models based on the effective cutting parameters are defined to compare and validate the local force decomposition. A high feed peripheral milling experiment with specific cutting force measurement is presented to develop the regression models. An extensive cutting force database for AISI 5115 is created by tool geometry and process control variable variations. The effective cutting conditions are calculated through geometric penetration simulation. Considering the tool deflection in the simulation achieves a high regression accuracy even with low chip thicknesses. This is especially important for the cutting force prediction of finishing processes. The resulting regression cutting force models and force decompositions are rated based on the applicability to different tool geometries, like a ball end mill.

Effect of process parameters on drilling — An overview

Out of all the machining operations, most commonly used operation is drilling. Drill forces, speed, feed, and temperature play significant roles in the drilling process because they are strongly related to drilling performance such as surface roughness and tool wear. The friction between the drill bit and workpiece will result in tool wear and chipping due to excess of drilling force. Sometimes, the difficulty of chip removal causes chip clogging and drill breakage, particularly with high strength metallic materials. Therefore it is necessary to identify optimal drilling conditions after a detailed study of the literature, in order to be process efficient as well as economical. A review of the effect of control factors and the mechanism of conventional and deep hole drilling process is carried out. Tool wear, friction, Built up Edge (BUE) and tool deflection are various threats depending upon the material being machined. In case of deep hole drilling, the effectiveness depends on torsional rigidity of tool, coolant supply and proper chip disposal. Further, specific application of deep hole drilling, such as in residual stress measurement, will require rigorous experimental and FEA evaluation. Finite element analysis of the drilling process is helpful to preview the stress state in the tool and work. This will help verify the selected cutting conditions.

Stability of micro-milling thin-walled part process

Micro-scale thin-walled parts have the characteristics of small size and low rigidity, so chatter is very easy to occur in high speed micro-milling, which influences machining precision and surface quality of the parts. To solve this problem, the authors first establish micro-milling force models in micro-milling thin-walled parts that consider the elastic deflection of both thin-walled part and micro-milling tool. According to the Lagrange’s equation, the dynamic characteristics of thin-walled part that vary with the cutting position of the tool are obtained combined with the Rayleigh-Ritz method. The relative transfer function between the micro-milling tool and the thin-walled part is achieved through the relationship between cutting force and vibration vector and then the dynamic characteristics of the system are obtained. Finally, the stability lobe diagram is drawn through time-domain simulation and verified by micro-milling experiments. The comparison results show that the prediction results of the stability lobe diagram are consistent with the experimental results. The research is a meaningful exploration of the mechanism of micro-milling thin-walled parts and provides a basis for the selection of cutting parameters for stable cutting.

Analysis of vibration assisted dry end milling using 3D FE simulation – An investigational approach

Abstract A three-dimensional finite element simulation model is developed to analyze the vibration-assisted dry end milling of Ti-6Al-4V alloy specimens. Results of finite element simulated forces in cutting and deflections of the cutter are compared experimented results. Cutting forces in the experiment are recorded using a Kistler 9272 force dynamometer and vibration signal data is acquired using a triaxialKistler (Model_8793) accelerometer. Two milling cutters, both uncoated and coated milling cutters are used for the experimental investigation. In the end, the impact of spindle rotational speed and feed rate on forces in cutting and subsequent tool deflections were also premeditated using the FE simulations. The investigation shows that there is a rise in forces of cutting as the feed is increasing and deflection also increases as the surge in feed rate and rotational speed. The experimental investigation will give the insights to understand the mechanics of the milling operation and thereby this data is cast-off to treasure the optimal machining conditions.

An investigation into the challenges of the point grinding machining process

Point grinding is an abrasive machining process that utilises miniature single layer superabrasive tools to remove material. The use of such small diameter tools offers advantages in the manufacturing of small or difficult to access complex 3D geometries, however, in their current state, these tools suffer from several critical challenges preventing their successful implementation. An investigation into the use of a typical commercially available point grinding tool for machining of hardened steel components has been carried out, with the aim of identifying the critical process challenges. The requirement for high rotational speeds, high tool deflection, variation in grit protrusion heights and bond layer thickness, accelerated tool wear, increased sensitivity to runout, zero cutting speed at tooltip and high tool loading have been identified as the main issues affecting the point grinding process. It is crucial that these challenges are correctly understood to facilitate future tool development.

Experimental and numerical optimization of process parameters for thin wall machining of bearing housings

Bearing Housing is the critical part in Air end assembly of Screw compressor, because it determines the Discharge end clearance (DEC) which in term determine the volumetric and adiabatic efficiency. For setting a DEC in air end assembly, it is important to maintain the shoulder thickness of bearing housing within close tolerance of 10 µm. If not maintained so, metallic shim to be added in assembly process to maintain the clearance. Metallic shim selection for each assembly involves iteration method which is time consuming process. However due to machining constraint the current tolerance which achieved is 100 µm but the requirement is 10microns. If the improvement is done to achieve the required tolerance, DEC setting time can be reduced by eliminating iteration shim setting process. The aim of this project is to achieve the linear & Geometric dimensions within deserved critical tolerance in milling thin wall areas of housings in CNC milling machine by optimizing the process parameters using Design of experiments (DOE) and Finite element analysis (FEA). DOE with cutting parameters using Response surface modeling (RSM) with face centered design is used to obtain the significance level of Cutting speed, feed and Depth of cut. Optimize the tool cutting parameters to minimize the tool and part deflection during machining. FEA is used to reach the optimum clamping area and force to minimize the part deflection & deformation.

Study of vertical milling machine layout in terms of rigidity

The paper explores the signs of static elastic deformation when operating milling machines. The research is concerned with the issue of simulation of a machine-device-tool-workpiece system for preliminary evaluation of elastic deformation on treatment in order to eliminate rejects. Linear and angular deformations were defined. Total rigidity of a milling machine depends not only on its inherent stiffness and contact rigidity of its components, but also on geometrical characteristics of manufacturing system layout. These characteristics could be evaluated through axes of rigidity. Errors in surface machining depend on both tool deflection and workpiece distortion. That is why it is necessary to analyze workpiece and tool load-deformation curve separately. Especially that their rigidity is determined by different layouts of bearing system. Main deformation values are defined and vectors of axes of rigidity are created. Using the results of the analysis it is possible to select a cutting pattern or change manufacturing system layout so that the resultant of cutting forces comes closer to highest rigidity axis, which reduces elastic deformations and improves working accuracy.

Modeling of micro-grinding forces considering dressing parameters and tool deflection

The prediction of cutting forces is critical for the control and optimization of machining processes. This paper is concerned with developing prediction model for cutting forces in micro-grinding. The approach is based on the probabilistic distribution of undeformed chip thickness. This distribution is a function of the process kinematics, properties of the workpiece, and micro-topography of the grinding tool. A Rayleigh probability density function is used to determine the distribution of the maximum chip thickness as an independent parameter. The prediction model further includes the effect of dressing parameters. The integration of the dressing model enables the prediction of static grain density of the grinding tool at various radial dressing depths. The tool deflection is also considered in order to account for the actual depth of cut in the modeling process. The dynamic cutting-edge density as a function of the static grain density, the local tool deflection, elastic deformation, and process kinematics can hence be calculated. Once the chip thickness is calculated, the single-grain forces for individual abrasive grains are predicted and the specific tangential and normal grinding forces simulated. The simulation results are experimentally validated via cutting-force measurements in micro-grinding of Ti6Al4V. The results show that the model can predict the tangential and normal grinding forces with a mean accuracy of 10% and 30%, respectively. The observed cutting forces further imply that the flow stress of the material did not change with changing the cutting speed and the cutting strain rate. Moreover, it was observed that the depth of cut and grinding feed rate had the same neutral effect on the resultant grinding forces.

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively.

Minimizing Flute Engagement to Adjust Tool Orientation for Reducing Surface Errors in Five-Axis Ball End Milling Operations

AbstractSurface errors due to force-induced tool and workpiece deflections are one of the major errors in multi-axis machining of parts especially with thin-walled structures. Dominant approaches to reduce these surface errors are re-machining the part, feed scheduling, and tool path modification. These methods are time consuming and computationally costly, and they rely on experimental data which is used in cutting force and deflection predictions. The present paper introduces a pure geometrical approach to reduce surface errors drastically by minimizing the engagement lengths of flutes’ cutting edges when a point on the flute’s cutting edge is in contact with the design surface. The total engagement length of the flutes’ cutting edges when one of them generates a contact point on the workpiece surface is formulated and considered as the minimization objective function of an optimization problem. Tilt and lead angles, which define the tool orientation, are the design variables of the optimization problem subjected to constraints based on the geometrical requirements of the ball end milling process. The optimization problem uses the nominal tool path to generate an optimal tool path with adjusted tool orientations. The presented method is computationally inexpensive and does not need any experimentally calibrated coefficients to predict cutting forces because of the pure geometrical nature of the approach. The method is experimentally validated through five-axis ball end milling experiments in which more than 90% surface error reduction is achieved.

Enhancement of accuracy in double sided incremental forming by compensating tool path for machine tool errors

Incremental Sheet Forming (ISF) is a dieless forming process and has capability to form complex 3D geometries. Double Sided Incremental Forming (DSIF) is the most flexible variant of ISF as it uses two tools, one on either side of the sheet (one tool forms the sheet and other acts as support). However, forming the components with acceptable accuracy is one of the challenges as it depends on many factors such as sheet and tool(s) deflections as well as machine tool errors. If these deflections/deviations are not compensated in tool path, accuracy of the component gets affected and support tool may lose contact or squeeze the sheet while forming components. In the present work, grid-based method is used to measure the machine tool error at discrete points of the work envelope. To enhance the accuracy and support tool contact condition, machine tool errors are compensated in tool path along with tool and sheet deflection compensations developed earlier. To demonstrate the effectiveness of developed methodology on the accuracy, components having different geometric characteristics as well as opening sizes are formed with and without compensating for machine tool errors in tool path. Results indicate that deviation in symmetry within the same contour reduced from 850 μm to less than 40 μm. In addition, support tool has maintained contact throughout support surface with maximum force of 50 N when machine tool errors are compensated.

Online Chatter Detection for Milling Operations Using LSTM Neural Networks Assisted by Motor Current Signals of Ball Screw Drives

AbstractFor certain combinations of cutter spinning speeds and cutting depths in milling operations, self-excited vibrations or chatter of the milling tool are generated. The chatter deteriorates the surface finish of the workpiece and reduces the useful working life of the tool. In the past, extensive work has been reported on chatter detections based on the tool deflection and sound generated during the milling process, which is costly due to the additional sensor and circuitry required. On the other hand, the manual intervention is necessary to interpret the result. In the present research, online chatter detection based on the current signal applied to the ball screw drive (of the CNC machine) has been proposed and evaluated. There is no additional sensor required. Dynamic equations of the process are improved to simulate vibration behaviors of the milling tool during chatter conditions. The sequence of applied control signals for a particular feed rate is decided based on known physical and control parameters of the ball screw drive. The sequence of the applied control signal to the ball screw drive for a particular feed rate can be easily calculated. Hence, costly experimental data are eliminated. Long short-term memory neural networks are trained to detect the chatter based on the simulated sequence of control currents. The trained networks are then used to detect chatter, which shows 98% of accuracy in experiments.