Tool Runout Research Articles

In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling

Micro milling aims to manufacture miniature structures with high quality and complex features, and the stochastic time-varying tool wear is a crucial factor which has great influence on machining quality and efficiency of micro milling process. To improve the precision of machining and sustainability of micro cutting tools, the in-process tool wear conditions should be identified and updated ahead of time. In this work, an improved integrated estimation method is proposed based on the long short-term memory (LSTM) network and particle filter (PF) algorithm to predict the stochastic tool wear values. The integrated PF-LSTM identification methodology is developed to predict the in-process stochastic tool wear progression on the basis of the historical measurement data. With the estimation of in-process stochastic tool wear, the cutting force model is modified, in which the influence of tool run-out and the trochoidal trajectory of cutting edge are also considered. The proposed integrated estimation method of in-process stochastic tool wear and the modified cutting force model were validated by the micro milling experiments with workpiece material Al6061. It can be seen from the comparison results that the availability and sustainability of micro cutting tool have been improved, and the prediction accuracy also could be increased by 3.4% compared with that without considering the influence of tool wear.

Influence of Tool Runout on Force Measurement During Internal Void Monitoring for Friction Stir Welding of 6061-T6 Aluminum

Abstract The goal of this research was to examine how altering the amount of friction stir tool eccentricity while controlling the amount of slant in the tool shoulder (drivers of oscillatory process forces) effects the generation of process force transients during sub-surface void interaction. The knowledge gained will help improve the accuracy of force-based void monitoring methods that have the potential to reduce the need for post-weld inspection. Process force transients during sub-surface void formation were examined for multiple tools with varying magnitudes of kinematic runout. The eccentric motion of the tool produced oscillations in the process forces at the tools rotational frequency that became distorted when features (flats) on the tool probe interacted with voided volumes, generating an amplitude in the force signals at three times the tool rotational frequency (for three-flat tools). A larger tool eccentricity generates a larger amplitude in the force signals at the tool’s rotational frequency that holds a larger potential to create a distortion during void interaction. It was determined that once void becomes large enough to produce an interaction that generates an amplitude at the third harmonic larger than 30% of the amplitude at the rotational frequency in a weld with no interaction (amplitude solely at rotational frequency), the trailing edge of the tool shoulder cannot fully consolidate the void, i.e., it will remain in the final weld. Additionally, once the void exceeds a certain size, the amplitudes of the third harmonics saturate at 70% of the amplitude at the rotational frequency during full consolidation. The interaction between the eccentric probe and sub-surface void was isolated by ensuring any geometric imperfection in the shoulder (slant) with respect to the rotational axis was removed. The results suggest that geometric imperfections (eccentricity and slant) with respect to the tool’s rotational axis must be known when developing a void monitoring method from force transients of this nature.

A Comprehensive Micro-Milling Force Model for a Low-Stiffness Machining System

Abstract Micro-milling is widely used in various crucial fields with the ability of machining micro- and meso-scaled functional structures on various materials efficiently. However, the micro-milling force model is not comprehensively developed yet when tool feature sizes continually decrease to under 200 µm in a low-stiffness system. This paper proposes an analytical force model considering the influence of tool radius, size effect, tool runout, tool deflection, and the actual trochoidal trajectories and the interaction of historical tool teeth trajectories (IHTTT). Different micro-milling status are recognized by analyzing the cutting process of different tool teeth. Conditions of single-tooth cutting status are determined by a proposed numerical algorithm, and entry angle and exit angle are analyzed under various cutting conditions for the low-stiffness system. Three micro-milling status, including single-tooth cutting status, are distinguished based on the instantaneous undeformed chip thickness resulting in three types of material removal mechanisms in predicting micro-milling force components. Discontinuous change rates of undeformed chip thickness are found in the low-stiffness micro-milling system. The proposed micro-milling force model is then verified through experiments of micro slot milling Elgiloy alloy with a 150-μm-diametrical two-teeth micro-end mill. The experimental results show a root-mean-square error (RSME) of 0.092 N in the predicted resultant force, accounting for approximately 5.12% of the measured force, by which the proposed theoretical model is verified to be of good prediction accuracy.

Cutter position optimization with tool runout for flank milling of non-developable ruled surfaces

Cutter position optimization with tool runout for flank milling of non-developable ruled surfaces

Milling force modeling for disc milling cutter of indexable three-sided inserts considering tool runout

Cutting force is essential for studying cutting mechanism and improving cutting process. In order to predict cutting force, the cutting force was measured by experiments; cutter runout was measured by dial indicator; the actual undeformed cutting thickness was obtained in presence of cutter runout. Then a mechanical cutting force model was established. Three kinds of cutting force coefficients, linear, exponential, and polynomial coefficients, are solved and compared. The results show that the exponential model has the highest generality and accuracy. The validation experiment illustrates deviation rates between the predicted values and measured values are mostly <30%, and the deviation rates of the middle inserts are greater than that of the left and right inserts. The mechanical model in this paper can predict the cutting force quickly, which is helpful to select the reasonable cutting parameters and lays a foundation for the research of multi tooth cutting force.

Modified iterative approach for predicting machined surface topography in ball-end milling operation

Machined surface topography prediction is an important and useful tool for optimizing cutting parameters. However, accurate prediction of machined surface topography in ball-end milling operation has been extremely challenging, due to the complexity in tool-workpiece interaction induced by the trochoidal motion of cutting edge and computing burden. In this present research, a modified iterative approach was proposed to solve the intersections between the cutting-edge sweeping surface and the discrete Z-vector model of workpiece, which were used to predict the machined surface topography in ball-end milling operation. Firstly, the accurate model of cutting-edge sweeping surface was established utilizing homogeneous coordinate transformation, in which the tool runout was considered. Secondly, the cutting-edge sweeping surface was dispersed into a series of patches in accordance with equal parameter interval, and the in-cut patch was extracted by using the minimum and maximum axial immersion angle of the cutting edge. Thirdly, the intersection between each in-cut patch and discrete Z-vector was solved using the Newton’s method, which was used to update the endpoint of the corresponding discrete Z-vector. Finally, ball-end milling experiments of AISI P20 steel were carried out to validate the proposed approach as well as investigate the effect of cutting parameters on the machined surface topography and roughness. The predicted machined surface topography and roughness were in good agreement with the measured results. Moreover, the proposed approach needs less computing time than the traditional iterative approaches at the same predicting accuracy. This research also provides guidance for optimizing cutting parameters to control surface quality in ball-end milling operation.

Milling force model prediction considering tool runout with three-teeth alternating disc cutter

Disc cutter has a large diameter and more teeth, and obvious tool runout (δi) is produced during disc milling process, so the irregular distribution of cutting force is generated due to uneven distribution of cutting output per tooth (hi*) caused by tool runout. Therefore, the tool runout (δi) must be considered when milling force model of disc milling is established. In the paper, first, milling force model of disc milling is built with a three-teeth alternating disc cutter considering the tool runout (δi). Second, tool runout (δi) is measured by the dial gauge when the revolving speed of disc cutter is 1 r/min, then cutting output per tooth considering tool runout (hi*) is obtained by the formula between cutting output of each tooth (hi) without considering tool runout (δi) and tool runout (δi). Third, the parameters of frictional angle (βn), normal shear angle (ϕn), and shear yield strength (τs) in model coefficients are calibrated by orthogonal cutting experimental. Then, the predictive model of milling force is obtained. Last, the reliability verification experiment of model is conducted. The experiment results show that the model is with high efficiency and precision and the measured force and predictive force is in good agreement in amplitude and trend, which can be used in the study of milling force of disc milling.

Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network–based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient–based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.

Generation Mechanism and Quality of Milling Surface Profile for Variable Pitch Tools Considering Runout

Abstract Surface profile is one of the foremost aspects to evaluate milling performance. Its generation mechanism is affected by a variety of factors such as tool geometry, runout values, and process parameters and thus still deserves further investigation. This paper aims to propose a unified method to study the surface generation mechanism and to predict the machining quality for variable pitch tools considering runout. First, a Floquet theory based algorithm is extended to analyze the machining stability and output the dynamic responses of the machining system. The resultant trajectories of cutting edges are obtained by kinematic synthesis of system vibrations, tool rotations, and machining feed. Next, both the surface location error (SLE) and the surface roughness are simultaneously extracted from the edge trajectories. A series of cutting tests are performed to validate the prediction results. Some new characteristics of the machined surface profile in terms of form errors and teeth marks are discovered and theoretically explained. Finally, the joint influences of tool geometry, runout values, and process parameters on the surface generation mechanism and quality are analyzed in detail with the proposed method.

A comparative analysis of characteristics of cutting forces at micro-milling of hardened steels

Micro-milling is one of the most important technologies to produce miniature components. Mainly due to the size reduction, the process has many special characteristics, such as the relatively large tool run-out, the relatively large deformation and the cutting edge radius of the micro-milling tool. The cutting force is often correlated to the phenomena mentioned above, its analysis is therefore often required. The main objective of the present paper is to analyse and compare the characteristics of cutting forces in micro-milling of different hardened steels. A systematic series of experiments were conducted on a hardened AISI H13 hot-work tool steel and a Böhler M303 martensitic corrosion resistance steel. The hardness of both materials is 50 HRC. A 500 µm diameter two fluted carbide micro end mill and a five-axis micromachining centre were applied. The effects of the cutting parameters are analysed by the analysis of variance (ANOVA). Experimental results show that the workpiece material has a significant influence on the cutting forces.

Innovative micro-tool manufacturing using ultra-short pulse laser ablation

Abstract The continuously growing demand for micro-electro-mechanical systems (MEMS) with decreasing feature sizes necessitates adjusted manufacturing solutions. Micro-milling fulfills the high requirements for precision and reliability placed on the final product. The employed tools have diameters in the range of 50–300 μm and are commonly manufactured by grinding. However, a reduction in tool diameter leads to a decrease in stiffness in the fourth power and the introduced load in grinding causes a high scrap rate. Furthermore, the flexibility in tool design is limited to the attributes of the grinding wheel. These restrictions in micro-tool manufacturing can be avoided by using ultra-short pulse (USP) laser ablation. This process allows a force-free 2-D and 3-D machining across a broad range of materials without wear formation. By using a USP laser, a wide range of customer-oriented applications in the micrometer scale can be addressed. Also, it leads to precise ablation with minimal thermal and mechanical damage. This paper provides knowledge on the manufacturing of micro-tools made of tungsten carbide with small diameters and high aspect ratio. For the desired tool geometries, necessary process parameters are evaluated and their physical limits are shown and discussed. An innovative CAM-system has been programmed to allow the manufacturing of advanced geometries using a 4-axis laser machine test bench developed by ETH Zurich. A 515 nm wavelength laser beam is capable of providing flawless tools with diameters as low as 50 μm and aspect ratios up to 6. Due to precise calibration, the tool run-out is decreased to

Energy consumption considering tool wear and optimization of cutting parameters in micro milling process

Micro milling process aims to manufacture complex micro/meso structures, and the reduction of material removal volume determines the possible decrease of the total energy use, which would put less pressure on the environment. However, the energy consumption of micro milling influenced by tool wear and tool run-out would be augmented and result in the drawback of more energy consumption. In order to reduce the energy consumption of micro milling process, a new analytical energy consumption model and the related optimization of cutting parameters are presented in this paper. Although the influence of tool wear is inevitable, it hasn't been thoroughly concerned in the existing energy consumption models. Therefore, the stochastic tool wear progression, which can be obtained from a probabilistic approach based on the online measured cutting forces, is integrated into the proposed energy model. In addition, the process nonlinearities caused by tool run-out and the trochoidal trajectories of cutting edge are also considered in the model. With the developed prediction model of energy consumption, a hybrid cuckoo search and grey wolf algorithm is used to determine the optimum cutting parameters for minimizing the total energy consumption. The micro milling experiments are performed to validate the accuracy and availability of the proposed energy consumption model and the optimization method. The improved optimization method based on the proposed energy model can reduce the energy consumption by 7.89% compared with the empirical selection.

Modelling and experimental analysis of the effects of run out, minimum chip thickness and elastic recovery on the cutting force in micro-end-milling

Prediction of cutting force has great significance for controlling the micro-end-milling processes. In this study, a mechanics model for exactly prediction cutting force is comprehensively established by considering the variety of entry and exit angles for each engaged cutting edge and an accurate instantaneous uncut chip thickness (IUCT). The determination of IUCT has considered the combination of the minimum chip thickness, tool run-out, and the material's elastic recovery, which is embedded in the cutting force model. Further, cutting force coefficients as function of uncut chip thickness have been calculated by using finite element method (FEM). To verify the reliability of the presented cutting force model, a series of experiments for cutting force are conducted and experimental results are compared to cutting force predicted. The results demonstrate that the cutting force predicted is well in agreement with that of measured. The effects of elastic recovery and tool run-out on cutting force also are investigated. Some conclusion can be drawn that elastic recovery can more obviously affect the cutting force predicted with smaller the feed per tooth, the errors of experimental and predicted is getting smaller with increasing the cutting depth, the slight change of tool run-out will lead to a great variation in cutting force.

Micro-milling force modeling with tool wear and runout effect by spatial analytic geometry

One of the major limitations of micro-milling applications in industries is its fast tool wear, which leads to low machining precision and efficiency. An accurate force model is fundamental for optimization micro-milling processes and minimize the tool wear. However, a generic model with tool runout and wear effect has not yet been established, which limits its practical application under varied working conditions. In this paper, a new idea is introduced by applying the spatial analytic geometry (SAG) method, under this framework the micro-milling force model is established based on the analysis of the geometrical relationship among the cutting edge positions, pre-processed workpiece morphology, and cutting force directions considering tool runout and wear effect. In this model, the tool runout is identified exclusively by only one parameter, namely the distance away from the center that perpendicular to the feed direction, so that it could be calibrated conveniently by calculating the ratio of resultant forces corresponding to different cutting edges. The tool wear–induced force is then modeled as increment of force coefficients to the original model. Therefore, the new force model with considering tool wear has the same form as the fresh tool. Finally, the accuracy and efficiency of the model are validated by experiments under varied working conditions.

Generic instantaneous force modeling and comprehensive real engagement identification in micro-milling

Cutting force modelling has been one of the most important tools for understanding the dynamics and for process control of micro milling. While current studies have widely investigated the uncut chip thickness and tool runout mechanics, they mostly took account a specific case and a general tool engagement determination is yet to be established. These lead to low adaptability and bring difficulties for process modeling and online control. In this study, a new and generic instantaneous force model is developed, in which the size effect is reflected in the force coefficients, and the tool runout effect is included in the instantaneous uncut chip thickness. In the model the real engagement is comprehensively identified under the tool run out effect, with the actual entry and exit angles at the critical position explicitly determined. The average uncut chip thickness (AUCT), actual cutting depth, center position and geometrical relations are analytically established. The proposed force model can be obtained conveniently and fast by only once calculation of the AUCT with inclusion of tool run out effect. With the main influencing factors considered, the force models are computational efficient, good in adaptability and could conveniently be generalized to universal model. Compared to the experimental measurements, the maximum peak errors of the forces are all less than 0.6%, which validates the efficiency and accuracy of the proposed model.

Fabrication of Micro-end Milling Cutter Based on WEDG

The effect of unbalance caused by installation error is the one of the main reasons for tool runout in micro milling processes, especially at high-speed on the spindle. As tool runout contributes significant impact to the machining accuracy in micro milling processes, an on-machine fabrication method which could avoid the existence of installation error of spindle deviation and tool tilt caused by off-line of micro-end milling cutter has been proposed. To verify the possibility of an on-machine fabrication method based on wire electro-discharge grinding, a micro-EDM machine with an ultra-high-speed air turbine spindle and a geometrically optimized tool design were developed. Ultra-fine grain carbide end mills with the target diameter equal to 50 μm were fabricated by wire electro-discharge grinding based on the on-machine fabrication method. And some micro slot milling experiments were conducted in pure copper. The experimental results indicate that the micro cutting process using the on-machine fabricated tool by wire electro-discharge grinding is possible. Moreover, this method can improve the reliability of the cutter and the processing accuracy of the parts, avoids the error caused by the second clamping of the micro-milling cutter and improves the overall quality of micro-milling.

Milling parameter and tool wear dependent surface quality in micro-milling of brass

Short life-time and high tool costs still remain major constraints for the micro-milling process. Understanding the wear mechanisms and their effects on the workpiece quality is essential for efficient tool usage. Usually, wear increases the cutting forces and reduces the emerging surface quality during the micro-milling process. Due to high tool costs, cutting parameters are usually chosen for optimal tool lifetime and/or process time rather than optimal surface quality.The scope of this paper is to investigate the correlation of the process parameters, strategy and wear status of the tool on the resulting surface topography. To reach this goal, micro-milling experiments were conducted, in which several grooves were milled using two end milling tools, new and worn, with a diameter of 1.5 mm and four cutting edges. The cutting speed and feed were varied, as well as the cutting direction. Brass was chosen as workpiece material to ensure a constant wear state of the tools during the experiments. During the cutting process the process forces were recorded and examined for their magnitude and frequency response. Furthermore, the grooves were analyzed optically for their surface roughness.The roughness shows in most cases slightly higher values for the specimen manufactured with the worn tool than the ones done with the new tool. The biggest influence on the surface roughness results from the feed rate, while cutting speed and milling strategy have a smaller influence. The measured cutting forces show similar tendencies, than the resulting surface roughness. The results show also a significant influence of tool wear on the vibration behavior during the process, while the influence of feed rate is mostly negligible. This results partly from the greater tool runout and bigger deviation of the cutting edges.

Improved dynamic cutting force modelling in micro milling of metal matrix composites part II: Experimental validation and prediction

The effects of cutting dynamics and the particles' size and density cannot be ignored in micro milling of metal matrix composites. This article presents the improved dynamic cutting force modelling for micro milling of metal matrix composites based on the previous analytical model. This comprehensive improved cutting force model, taking the influence of the tool run-out, actual chip thickness and resultant tool tip trajectory into account, is evaluated and validated through well-designed machining trials. A series of side milling experiments using straight flutes polycrystalline diamond end mills are carried out on the metal matrix composite workpiece under various cutting conditions. Subsequently, the measured cutting forces are compensated by a Kalman filter to achieve the accurate cutting forces. These are further compared with the predicted cutting forces to validate the proposed dynamic cutting force model. The experimental results indicate that the predicted and measured cutting forces in micro milling of metal matrix composites are in good agreement.

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.

Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

This research work deals with the numerical modeling and analysis of macro- to microscaled milling of a strain rate-sensitive alloy AA2024T351. The milling computations of a semicircular slot are performed in Abaqus/Explicit by employing the Johnson–Cook thermo-elasto viscoplastic material damage model. The Coulomb friction model is applied at the contact interfaces of work piece and cutting tool. Due to the simultaneous effect of cutting feed and angular speed (ωr), the geometry of uncut chip is modeled with variable section thickness to adapt the trochoidal path. The configuration of the uncut chip undertakes the macro- to micromilling effect. This research effort uniquely explains a comprehensive modeling and milling computations at macro- to microscales to identify the imperative milling characteristics. The presented formulation in form of the modified Johnson–Cook constitutive equation explains the size effect during micromilling. A parametric sensitivity analysis is performed with four different cutting speeds (500, 600, 700, and 800 m/min) and two contact friction coefficients (0.2 and 0.3) while keeping the feed rate constant (0.2 mm/teeth). It was observed that cutting speed and contract friction coefficient play a pivotal role in cutting reaction force and chip–tool interface temperature. A temperature in the range of 141–167 °C is predicted with different cutting speeds. The percentage deviations of the simulated cutting forces from the published experimental results are found promising. The estimated deviation may be because of compromising some imperative factors in numerical model, such as cutter vibrations, tool wear effect, tool run-out, and limitations, to develop an exact trochoidal trajectory.