Corner Milling Research Articles

Corner milling force prediction and improvement method of aviation thin-walled structural parts

Corner milling force prediction and improvement method of aviation thin-walled structural parts

Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method

Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to obtain sufficient test data on corner milling for most of the current pattern recognition methods, which are based on the supervised method. In this work, we propose a time-frequency intrinsic feature extraction strategy of acoustic emission signal (AEs) to construct a cutting condition-independent method for tool wear monitoring. The proposed new feature-extraction strategy is used to obtain the tool wear conditions through the intrinsic information of the time-frequency image of AEs. In addition, an unsupervised tool condition recognition framework, including the unsupervised feature selection, the clustering based on adjacent grids searching (CAGS) and the density factor based on CAGS, is proposed to determine the relationship between tool wear values and AE features. To test the effectiveness of the monitoring system, the experiment is conducted through the corner milling of a titanium alloy workpiece. Five metrics, PUR, CSM, NMI, CluCE and ClaCE, are used to evaluate the effectiveness of the recognition results. Compared with the state-of-the-art supervised methods, our method provides commensurate monitoring effectiveness but requires much fewer test data to build the model, which greatly reduces the operating cost of the TCM system.

An Investigation of Power Consumption in Milling AISI P20 Plastic Mold Steel By Finite Elements Method

Mold steels lead the production of products used in all areas of our lives. Performing the machinability tests of these materials with the finite element method (FEM) provides cost and time savings. In addition, it provides to get away from physical experiments that provide high power consumption. Because consumption must be eliminated in order to ensure a sustainable and clean production. In this study, AISI P20 mold steel was milled using the finite element method. Same directional milling and corner milling operations were performed. The effects of the cutting parameters used in the milling process on the power consumption were investigated. Four different feed rates (0.075-0.113-0.169 and 0.253 mm/tooth) four different cutting speeds (170-200-230-260 m/min), and two different cutting depths (0.075-1.5 mm) were used in the study. Power consumption values were obtained by taking the resultant values of the cutting forces (Fx, Fy and Fz) occurring during cutting. According to the results obtained, it was concluded that the power consumption increased with the increase in feed rate, cutting speed and cutting depth. The highest power consumption value was determined as 8041.91 W, and the lowest power consumption value was determined as 1748.10 W. As a result, it has been shown that the FEM and statistical evaluation yield consistent results.

Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path.

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

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.

Corner-milling process parameter optimization regarding H62 brass using Kriging model and improved particle swarm optimization algorithm

Single-pass corner milling is common in machining process. Machining quality and cost are the two most important goals for machining process. Machining quality is greatly affected by deformation and mechanical property. Cutting temperature has a very important effect on tool wear, mechanical property, deformation and machining precision. In this paper, cost per volume (CPV) and machining temperature are selected as objectives. To reduce the cost of research, AdvantEdge simulation software was used to simulating the machining process and examined for its accuracy. Comparing the result of AdvantEdge with physical experiments, it showed an average 8% error on CPV and a 6% error on temperature. Given this comparison, simulation data were used to train Kriging model as the surrogate model. Kriging model manifested high accuracy, showing mean error of 6% and 3% in terms of CPV and temperature on validation points. To solve the multi-objective problem, a K-means particle swarm optimization (PSO) was constructed, which outperformed traditional adaptive gird algor (AGA) by finding the better optimal solutions, meanwhile consuming lesser time. Optimization result showed that corner milling should have a low-level spindle speed to ensure low cost and low temperature. High feed rate, cutting depth and cutting width will result in low cost along with high temperature. Optimal parameters should be chosen from Pareto set according to different practical needs. This integrated method manifested its applicability in solving multi-objective problems.

Analiza symulacyjna kąta opasania przy frezowaniu wykończeniowym naroży wewnętrznych

The simulation of the cutting layer in the CAD system during milling internal corners of elements with complex geometry has been presented. A case of variable cutting width in the finishing process was considered and the strategy of multitransitional corner milling was analyzed.

Dokładność geometryczna naroży po obróbce frezowaniem stopów aluminium

The study comparing a geometry of the inner corners after milling operation of aluminum alloy 7075 elements was conducted. The influence of changes of technological parameters and types of treatment on the geometric accuracy of the samples was investigated. The correct process of corner milling involves the need to select the right feed speed as well as the correct engagement angle of the milling tool. Corner milling is problematic due to the increase of engagement angle in their area. This makes it much more difficult to perform stable machining and adversely affects the surface accuracy and geometry of the concave corners. The tests using various strategies and technological parameters for milling corners with variable opening angles were carried out. When analyzing the obtained results, conclusions were formulated indicating the relationship between the change of selected technological parameters of milling in the corners and the geometric accuracy of the samples tested.

3D FEM Simulation of Milling Force in Corner Machining Process

To optimize milling force and machining accuracy quality in corner milling process, the changing law of milling force is revealed by Finite Element Method(FEM). Based on DEFORM software a serial of 3D FEM models for corner milling process are devloped. Tool curved trajectory is achieved by establishing accurate relationship of tool location with milling time. Adaptive remeshing technique and iterative algorithm are adopted to ensure convergence of FEM model. Component force characteristics are revealed by analyzing FEM simulation results. It indicates that the milling force in Y direction becomes negative comparing with forces in X and Z direction. Magnitude of forces in three directions decreases with increase of spindle speed, while it increases with increase of milling feedrate. The simulation results for cutting force are in good agreement with those obtained from experiment. The FEM simulation model is first successfully established for corner milling process in this study, and the results provide a guide for optimizing cutting parameters in cutting process.

Off-line error compensation in corner milling process

Tool deflection induced by cutting force could result in dimensional inaccuracies or profile error in corner milling process. Error compensation has been proved to be an effective method to get accuracy component in milling process. This article presents a methodology to compensate profile errors by modifying tool path. The compensation effect strongly depends on accuracy of the cutting force model used. The mathematical expression of chip thickness is proposed based on the true track of cutting edge for corner milling process, which considers the effect of tool deflection. The deflection of tool is calculated by finite element method. Then, an off-line compensation algorithm for corner profile error is developed. Following the theoretical analysis, the effect of the error compensation algorithm is verified by experimental study. The outcome provides useful comprehension about selection of process conditions for corner milling process.

Research on Cutting Force of Corner Milling of Inclined Plane

When milling corners in high speed, it will lead the mutation of cutting force that affects the processing quality and processing efficiency. In order to study the influence of milling parameters on milling force in the corner. Firstly, an orthogonal experimental of corner is designed to study the influence of various cutting parameters on cutting force. Axial cutting depth, radial depth, spindle speed and feed speed, as the major influence factors, impact on cutting force in corner milling. Then, a cutting force model of corner is established based on a method of orthogonal experiment linear regression. The significance test of regression equation and regression coefficient shows that cutting force model is accurate. The cutting force model is used to predict the cutting force, and then select the appropriate cutting parameters.

Off-line feedrate optimization with multiple constraints for corner milling of a cavity

High quality and high efficiency are very important factors in the mold industry and must be considered in machining technology development. Corners are one of the typical features in a cavity mold that can cause low accuracy, tool wear, cutter defection, and vibration. Therefore, off-line feedrate optimization in a corner-milling process has been proposed as a means for avoiding these drawbacks. The corner-milling process could be divided into five stages according to the contact conditions between the cutter and workpiece. An improved chip thickness model based on the actual trajectory of the cutter was presented to predict the movement of the cutter along a curved path. Various feedrate optimizations were presented with varying control parameters. In this paper, three feedrate optimizations were implemented using three control parameters, relative volume, milling force, and cutter deflection, based on the differential element method (DEM). A corner-milling force experiment indicated that the simulation results using a modified milling force model agreed well with actual experimental milling results. Following this, a feedrate optimization with multiple constraints was built, according to a combination of three feedrate optimizations and then consider the acceleration and deceleration limitation. The simulation by using the method in this paper demonstrated that the machining time of rough milling, semi-finish milling, and finish milling could be reduced. In addition, it was found that the resulting milling force varies within a narrow range.

Parameter Determination of Milling Process Using a Novel Teaching-Learning-Based Optimization Algorithm

Cutting parameter optimization dramatically affects the production time, cost, profit rate, and the quality of the final products, in milling operations. Aiming to select the optimum machining parameters in multitool milling operations such as corner milling, face milling, pocket milling, and slot milling, this paper presents a novel version of TLBO, TLBO with dynamic assignment learning strategy (DATLBO), in which all the learners are divided into three categories based on their results in “Learner Phase”: good learners, moderate learners, and poor ones. Good learners are self-motivated and try to learn by themselves; each moderate learner uses a probabilistic approach to select one of good learners to learn; each poor learner also uses a probabilistic approach to select several moderate learners to learn. The CEC2005 contest benchmark problems are first used to illustrate the effectiveness of the proposed algorithm. Finally, the DATLBO algorithm is applied to a multitool milling process based on maximum profit rate criterion with five practical technological constraints. The unit time, unit cost, and profit rate from the Handbook (HB), Feasible Direction (FD) method, Genetic Algorithm (GA) method, five other TLBO variants, and DATLBO are compared, illustrating that the proposed approach is more effective than HB, FD, GA, and five other TLBO variants.

Precise prediction of forces in milling circular corners

Pocket corner is the most typical characters of aerospace structure components. To achieve high-quality product and stable machining operation, manufacturer constantly seek to control the cutting forces in pocket corner milling process. This paper presents the cutting force in corner milling considering the precision instantaneous achievements of tool engagement angle and undeformed chip thickness. To achieve the actual milling tool engagement angle in corner milling process, the details of tool–corner engagement relationship are analyzed considering the elements of tool trajectory, tool radius, and corner radius. The actual undeformed chip thicknesses in up and down milling operations are approached on account of the trochoid paths of adjacent teeth by a presented iteration algorithm. Error analysis shows that the presented models of tool engagement angle and undeformed chip thickness have higher precision comparing with the traditional models. Combined with the cutting force coefficients fitted by a series of slot milling tests, the predicted cutting force in milling titanium pocket with different corner structure and milling parameters are achieved, and the prediction accuracy of the model was validated experimentally and the obtained predict and the experiment results were found in good agreement.

Research on Error Compensation of Corner Milling Process Based on Milling Force Prediction

In cavity die corner-machining, tool flexible deformation caused by the milling force resulting in the surface error, a method of off-line error compensation is put forward. Instantaneous chip thickness model and the corner milling force model is established based on differential and the characteristics of the corner. Combining the theory of cantilever beam and the finite element analysis, cutting tool elastic deformation model is established.

Time-domain simulation and experimental verification of dynamic cutting forces and chatter stability for circular corner milling

Based on the modeling of circular corner milling process, the implicit three-step four-order ADAMS numerical analysis method is presented in this article to solve the differential equation of milling dynamics, which considers non-uniform variation of the radial depth of cut and nonlinear feature of tool–workpiece system. The dynamic cutting forces are predicted in time domain and experimentally verified. On this basis, with comprehensive application of time-domain stability criteria for predicted data, the chatter stability of the milling process is simulated. The predicted stability lobe diagrams are compared against experimental results to confirm the validity of the simulation algorithm, which provides an effective way to optimize cutting parameters for circular corner milling.

Modeling, Simulation and Optimization of Corner Milling

Considering the wide application of corner milling in the numerical control machining, it has great significance to predict the force variation and optimize the cutting parameters before the actual milling. This paper establishes the general algorithm to predict the variation of cutting width, with which the force of the cutting process can be acquired. Then the results of the prediction are validated through five different experiments with different parameters like feed rate and cutting depth etc. Especially, a way to optimize parameters of the corner milling process is proposed and verified by general prediction algorithm.

Tool Path Optimization Based on the Constrain of Radial Cutting Depth in High Speed Milling

In order to decrease the radial amout of cutting depth in high speed milling,optimized arc transition has been proposed in corner milling and the circular trochoid model has been used to remove the residual. Mathematical relationship based on radial depth of cutting constrain among tool radius, trochoid radius and tool step length in circular trochoid model have been analyzed .Based on the goal to limiting the radial cutting depth, appropriate parameters have been computed for higher machining quality. To verify the effectiveness of the proposed method, a compared test has been conducted. Experiment shows that optimized tool paths cause a slight increase in machining time but obtain weaker processing vibration and superior machining precision.

Intelligent Process Planning System for End-milling Process (2nd Report)

This paper deals with a decision method of cutting conditions and tool life estimation method for end-milling processes of plain carbon steel. Basic cutting conditions for side cutting of a step, one of the most basic milling processes, are chosen from the database given by a cutting tool manufacturer. These data are transformed for individual milling processes, e.g. trochoidal grooving, spiral hole enlarging, and corner milling, such that constant cutting force is kept by using feedforward control. Cutting tests were conducted for the workpiece composed of various cutting features, and it was clarified that adequate cutting conditions and adequate tool life are simply decided using by this method.

Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm

This paper outlines the development of an optimization strategy to determine the optimum cutting parameters for multi-tool milling operations like face milling, corner milling, pocket milling and slot milling. The developed strategy based on the maximum profit rate criterion and incorporates five technological constraints. In this paper, optimization procedures based on the genetic algorithm, hill climbing algorithm and memetic algorithm were demonstrated for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been developed. Results obtained are used in NC machine. An example has been presented at the end of the paper to give clear picture from the application of the system and its efficiency. The results are compared and analyzed with method of feasible directions and handbook recommendations.