Elastic Deformation Of Workpiece Research Articles

A Voxel-Based End Milling Simulation Method to Analyze the Elastic Deformation of a Workpiece

Abstract A new method to analyze the elastic deformation of a workpiece during end milling is proposed. One of the advantages of this method is the possibility of easily combining it with a voxel-based milling simulation, which is often used to predict cutting force, and to predict machining error due to workpiece deflection. With this method, the workpiece is discretely represented by voxels connected to their neighboring voxels with beam elements. Although the finite element method (FEM) is generally used for deformation analysis, it requires substantial time to analyze the deformation. In contrast, the proposed method does not require much time for remeshing, as the workpiece shape change is represented by removing voxels, and the stiffness matrix can be easily updated from the stiffness matrix obtained before the shape change. By conducting the preliminary analysis using coarse voxels and estimating the initial value of the solution, our method also reduces the number of iterations required to determine the deformation. The proposed method was integrated into the voxel-based cutting force prediction method in order to simulate the workpiece deformation caused by the cutting force. Therefore, the cutting force and the resulting workpiece deflection are seamlessly predicted using a voxel model. The results of a verification experiment showed that the analyzed workpiece deformation was in rough agreement with the measured deformation. Our future work is to predict the machining error induced by the workpiece elastic deformation based on this method and to integrate it with our previous work on the prediction of machining error induced by elastic deformation of the tool.

Оптимизация режимов плоского шлифования деталей малой жесткости типа колец и призматических стержней

The study introduces an algorithm and method for optimization of flat grinding modes for low-rigidity workpieces such as rings and prismatic rods. The proposed solution ensures the specified quality of the machined surface at the maximum process efficiency. The required flatness tolerance of the machined surface is provided at the second stage of optimization modes by limiting the value of the maximum elastic deformation. The variable parameters are the cutting forces, magnetic field attraction of the machine table, and the bending rigidity of the workpiece. The study introduces mathematical models to determine maximum elastic deformation of workpieces when fixing and machining, outlines the conditions for ensuring the required flatness tolerance of the machined workpiece surface, taking into account the maximum elastic deformation of the low rigidity workpiece under the influence of the magnetic field of the machine tool and the radial component of the cutting force.

A calculation method for workpiece profile variation during double-sided lapping by considering workpiece elastic deformation

A calculation method for workpiece profile variation during double-sided lapping was developed in this study. In this method, shell element in FEM deformation was used to formulate elastic deformation of workpiece, and spring element was used to formulate the contact between workpiece and platens to calculate distribution of their contact pressure considering workpiece elastic deformation. First, the developed method of the contact pressure distribution was evaluated by comparing with a method using a FEM software and the existing methods, and was confirmed to be quite valuable in terms of applicable process and calculation time. Subsequently, the workpiece profile variation was calculated using the developed method for several workpiece conditions, and it was clarified that the uneven workpiece thickness can be modified by the process, but it is difficult to modify the bow shape when the workpiece is not small. Furthermore, the decrease of the load applied to the workpiece was suggested to modify the bow shape of the workpiece.

Engine-bracket drilling fixture layout optimization for minimizing the workpiece deformation

Purpose Fixture layout design is concerned with immobilization of the workpiece (engine mount bracket) during machining such that the workpiece elastic deformation is reduced. The fixture holds the workpiece through the positioning of fixturing elements that causes the workpiece elastic deformation, in turn, leads to the form and dimensional errors and increased machining cost. The fixture layout has the major impact on the machining accuracy and is the function of the fixturing position. The position of the fixturing elements, key aspects, needed to be optimized to reduce the workpiece elastic deformation. The purpose of this study is to evaluate the optimized fixture layout for the machining of the engine mount bracket. Design Methodology Approach In this research work, using the finite element method (FEM), a model is developed in the MATLAB for the fixture-workpiece system so that the workpiece elastic deformation is determined. The artificial neural network (ANN) is used to develop an empirical model. The results of deformation obtained for different fixture layouts from FEM are used to train the ANN and finally the empirical model is developed. The model capable of predicting the deformation is embedded to the evolutionary optimization techniques, capable of finding local and global optima, to optimize the fixture layouts and to find the robust one. Findings For efficient optimization of the fixture layout parameters to obtain the least possible deformation, ant colony algorithm (ACA) and artificial bee colony algorithm (ABCA) are used and the results of deformation obtained from both the optimization techniques are compared for the best results. Research Limitations Implications A MATLAB-based FEM technique is able to provide solutions when the repeated modeling and simulations required i.e. modeling of fixture layouts (500 layouts) for every variation in the parameters requires individual modeling and simulation for the output requirement in any FEM-based software’s (ANSYS, ABACUS). This difficulty is reduced in this research. So that the MATLAB-based FEM modeling, simulation and optimization is carried out to determine the solutions for the optimized fixture layout to reach least deformation. Practical Implications Many a time the practicability of the machining/mechanical operations are difficult to perform costly and time-consuming when more number of experimentations are required. To sort out the difficulties the computer-based automated solution techniques are highly required. Such kind of research over this study is presented for the readers. Originality Value A MATLAB-based FEM modeling and simulation technique is used to obtain the fixture layout optimization. ANN-based empirical model is developed for the fixture layout deformation that creates a hypothesis for the fixture layout system. ACA and ABCA are used for optimizing the fixture layout parameters and are compared for the best algorithm suited for the fixture layout system.

Improvement of Product Quality through Optimum Fixture Design using DOE and FEM

Machining fixtures are used to improve the productivity and quality of the finished components. It is essential to produce a component in specified accuracy and quality: this can be achieved by optimizing the precise fixture design based on workpiece geometry and machining condition. In this work, optimum fixture layouts were developed for rigid body component. Design of Experiment based optimization procedure is established for identify the optimum fixture design. Finite element software ANSYS is used for predicting the workpiece elastic deformation caused by machining and clamping forces.

Fixture Design and Work Piece Deformation Optimization Using the Iterative Simplex Algorithm

Presents article is deal with optimization of the fixture for end milling process, the most important objective being the minimization of work piece deformation by changing the layout of fixture elements and the clamping forces. The main objective of this work has been the optimization of the fixtures Work piece deformation subjected to clamping forces for End milling operation. The present analysis is used in hollow rectangular isotropic material work piece for FEA analysis and its optimization A linear programing (L.P) simplex model optimization activity has been performed both on fixture-work piece systems modeled with FEM and on fixture-work piece systems modeled with 3-D solid elements. The optimization constraints is selected as W/P deformation in x,y,z direction for various clamping forces, in order to provide a new design of fixture. The MATLAB code has been developed for L.P. model optimization purpose. Present MATLAB code is validated by using available literature. This paper deals with application of the L.P. model for w/p deformation optimization for a accommodating work piece. A simplex iterative algorithm that minimizes the work piece elastic deformation for the entire clamping force is proposed. It is shown via an example of milling fixture design that this algorithm yields a design that is superior to the result obtained from either fixture layout or w/p deformation optimization alone.

PRE-MILLING FLEXIBLE FIXTURE REVIEW AND OPTIMISATION

Machining requires correct workpiece location and restriction. Fixtures secure the workpiece for reproducibility, uniformity, and accuracy. The Workpiece Fixture System (WFS) minimises workpiece elastic deformation to improve machining quality. Elastic deformation impacts workpiece shape and precision. The workpiece's elastic deformation prevented even the most exact machinery from tightening tolerances. Deformation prediction and optimisation require much study. Batch or large production WFSs involve workpiece and fixture setup for machining. Fixture layout design (FLD) involves strategically placing locators and clamps around a workpiece to optimise machining and reduce machining forces. The right FLD holds the workpiece against cutting forces, minimising elastic deformation. FLDs depend on proper locator and clamp placement. FLD optimisation in WFS is essential for high-quality, low-cost machining.

Simultaneous fixturing layout and sequence optimization based on genetic algorithm and finite element method

Dimensional and geometrical precision of industrial parts in the manufacturing process depend heavily on several items such as material, tool, machine tool and fixturing strategy. The workpiece deformation depends on several issues such as workpiece elastic deformation, geometric error of fixcles (we will call locators and clamps as “fixcles” afterwards), fixturing layout, external forces like clamping and machining forces, and variation of contact forces during machining. The optimization of locator and clamp positions, as well as their sequence, are crucial factors in minimizing the deformation error in workpiece machining. This research tries to introduce a new viewpoint toward genetic algorithm (GA) optimization method in order to introduce fixturing regions and fixturing sequence, which minimize the workpiece deformation error in 3-2-1 locating approach. A new concept, namely “Fixturing Clearance (FC)” has been proposed in this study. FC is a criteria which evaluates fixturing layout. All layouts which lead to fewer machining errors than FC have the potential to be accepted in the final fixturing regions. Although GA method originally results in an individual optimum point, the proposed strategy in this study introduces continuous fixturing regions as well as the functional fixturing sequence. An integration between GA and finite element is used to optimize the elastic deformation of the workpiece under clamping and machining loads. The effectiveness of this method has been illustrated on a 2D case study.

Prediction of Workpiece Elastic Deformation Using FEM Based Contact Analysis

The proper selection of fixtures and its locations is necessary to avoid the dimensional and form inaccuracies that are developed due to elastic deformation of the workpiece during machining. It is necessary to predict the elastic deformation with higher precision in order to eliminate the above inaccuracies during the design of fixtures. In the present work, a Finite Element Method (FEM) based 3D contact analysis has been proposed to evaluate the elastic deformation of the workpiece under the influence of fixtures and machining forces. In the proposed model, workpiece has been considered as flexible body and fixture elements (locators and clamps) as rigid bodies. A new concept of pre-stressed harmonic analysis has been introduced in order to simulate the machining forces. The contact area of the locators has been considered using contact elements and the clamping forces as point forces. The model for the analysis is developed in ANSYS environment with suitable elements. The effect of material removal has also been considered in the analysis in view of obtaining more accurate and realistic results. The deformation of the workpiece has been predicted for a particular location of clamps, locators and machining forces and presented. It is possible to optimize the location of clamps and locators under the influence of machining forces, the above analysis is suggested.

An integrated finite element method, response surface methodology, and evolutionary techniques for modeling and optimization of machining fixture layout for 3D hollow workpiece geometry

Machining fixtures play inevitable role in manufacturing to ensure the machining accuracy and workpiece quality. The layout of fixture elements, clamping forces, and machining forces significantly affect the workpiece elastic deformation during machining. The clamping and machining forces are necessary to immobilize and machine the workpiece, respectively. Finding the appropriate layout of fixture elements is the other possible way to reduce the workpiece deformation, which in turn improves the machining accuracy. The finite element method interfaced with evolutionary techniques is normally used for fixture layout optimization. In the finite element method, the workpiece is discretized into a number of small elements and fixture elements are placed only on the nodes. Hence, evolutionary techniques are capable of searching the optimal fixture layout from those discrete nodal points than from the entire area on the locating and clamping face. To overcome these limitations, in this research paper, response surface methodology is employed to establish a quadratic model between the position of fixture elements and maximum workpiece deformation. This enables the optimization techniques to search for the optimal solution in the continuous domain of the solution space. Then, the real-coded genetic algorithm based discrete optimization, continuous optimization based on binary-coded genetic algorithm and particle swarm optimization are employed to optimize the developed quadratic model and their performances are compared. The result clearly shows that the integration of finite element method, response surface methodology with particle swarm optimization is better than the integration with genetic algorithm to optimize the machining fixture layout and also reduces the computational complexity and time to a greater extent.

Optimization of Fixture Layout and Artificial Neural Network (ANN) Weights of ANN-Finite Element Analysis Based Fixture Layout Model Using Genetic Algorithm

Workpiece elastic deformation in machine manufacturing may cause dimensional errors, which in turn affects the accuracy of the machined parts. Fixturing elements like locators and clamps are used to locate a workpiece with respect to the cutting tool in a given orientation such that the errors caused by workpiece elastic deformation are reduced. The optimization of locator and clamp positions is crucial in minimizing the dimensional errors in machining. In this research paper, a slot milling operation on a rectangular workpiece is considered for which the fixture layout is optimized using a hybrid system of artificial neural network (ANN) and genetic algorithm (GA). The workpiece elastic deformation for different sets of fixture layouts is calculated using finite element method (FEM) and training of ANN is done with the FEM results to develop a numerical model. To enhance the accuracy of learning in lesser time, the weights are optimized for the network using GA before the training phase. The trained ANN recognizes a pattern between the position of fixturing elements and the workpiece elastic deformation. Using the recognized pattern, GA determines the optimal position of locators and clamps to minimize the workpiece elastic deformation and thereby the dimensional errors.

A12 柔軟弾性体のエンドミル加工における工作物変形と加工誤差の計測(OS6 切削加工(3))

This research focuses on machining error due to the workpiece elastic deformation and investigates the influence on the machining error. Orthogonal cuttings by a straight edge end-mill of the elastomers are carried out. Displacements of workpiece during processing are measured by using a high speed camera. Furthermore, workpiece surface is measured by the scanning laser displacement sensor. Influence of cutting condition on the machining error and the workpiece elastic deformation is investigated by comparing the relationship between the deformation and the machining error.

Machining Fixture Layout Design for Milling Operation Using FEA, ANN and RSM

Workpiece elastic deformation caused during machining influences the dimensional and form errors. The machining fixtures are used for positioning the workpiece accurately to reduce the dimensional and form errors during machining. The position of the fixture elements is a major concern in minimizing the errors caused during machining. The optimal positioning of clamps and locators can minimize the elastic deformation of the workpiece which in turn reduces the machining errors. The main focus of this research work is to predict the fixture layout to minimize the maximum elastic deformation of the workpiece during machining. The finite element method (FEM) has been employed to determine the workpiece elastic deformation. The position of the fixturing elements is predicted using both Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). In this paper, a numerical example has been considered from the literature to compare the performance of ANN and RSM.

Optimal fixture parameters considering locator errors

A machining fixture consists of elements such as locators, clamps and supports. Fixture design aims at ensuring workpiece quality by restraining the workpiece in the desired position throughout machining thereby minimising the overall machining error. Workpiece elastic deformation and geometric error of locators are major components of the overall machining error. The effect of geometric error is considerable in certain cases and hence cannot be ignored. For a given error in locators, the geometry related machining error is manifest in the locator layout whereas the workpiece deformation depends on both the layout and the external forces such as clamping and machining forces. Layout of fixturing elements and the applied clamping forces are collectively called fixture parameters. The objective of minimising the total machining error can be achieved by optimising either one or both of these parameters. In this research work both of these parameters are simultaneously optimised using a genetic algorithm (GA). A finite element model of the workpiece fixture system is developed and analysed using commercial finite element software ANSYS®. Elastic deformation of workpiece under machining loads is obtained from the finite element model. MATLAB® based GA is interfaced with ANSYS® for the determination of total machining error and subsequent optimisation with the objective of complying with tolerance requirements on the critical machining feature. Results indicate that the error sources can contribute to the final machining error in varying degrees. The results also underscore the need to consider the entire machining path for optimisation of the fixture parameters.

Zonal Compensation for Work-Piece Elastic Deformation through Fixture Layout Optimization

Work-piece elastic deformation caused by clamping force from fixture will lead to part bending or distorting, which is critical to lower its quality performance. In this paper, a novel zonal compensation method is proposed to compensate the work-piece elastic deformation through fixture layout optimization based on High Definition Metrology (HDM). The zonal surface flatness data of work-piece in fixture is obtained with densely optical measurement firstly. Then, they are reorganized according to the accuracy requirement based on the zonal distribution. Finally, the surface flatness compensation model based on fixture layout is made. The practicality and effectiveness of this new approach was verified using the case of Aluminum alloy 6061 part milling.

Analysis of the Workpiece Elastic Deformation Holding in Fixture with High Definition Metrology

The work-piece holding in the fixture will have elastic deformation under the effect of clamping force and the positioning of locators which will lead to poor quality. In this paper, after comparing the advantage of High Definition Metrology (HDM) with the traditional contact measurement metrology, the surface flatness measurement experiment scheme was designed based on HDM. Then, it was optimized with Design of Experiment (DOE). Finally, the work-piece elastic deformation caused by clamping force, its location and support location were analyzed using the case of Aluminum alloy 6061 part.

Machining fixture layout design using ant colony algorithm based continuous optimization method

In any machining fixture, the workpiece elastic deformation caused during machining influences the dimensional and form errors of the workpiece. Placing each locator and clamp in an optimal place can minimize the elastic deformation of the workpiece, which in turn minimizes the dimensional and form errors of the workpiece. Design of fixture configuration (layout) is a procedure to establish the workpiece–fixture contact through optimal positioning of clamping and locating elements. In this paper, an ant colony algorithm (ACA) based discrete and continuous optimization methods are applied for optimizing the machining fixture layout so that the workpiece elastic deformation is minimized. The finite element method (FEM) is used for determining the dynamic response of the workpiece caused due to machining and clamping forces. The dynamic response of the workpiece is simulated for all ACA runs. This paper proves that the ACA-based continuous fixture layout optimization method exhibits the better results than that of ACA-based discrete fixture layout optimization method.

Dynamic analysis on optimal placement of fixturing elements using evolutionary techniques

In the design of a machining fixture configuration (layout), the stability of the fixture, which influences the dimensional and form accuracy of the workpiece, is an important factor. The optimal placement of the fixturing elements minimizes the dynamic response of the workpiece (elastic deformation of the workpiece under dynamic conditions), which in turn improves the stability of the fixture. In this paper, an Ant Colony Algorithm (ACA) and Genetic Algorithm (GA) are employed to optimize the fixture layout with an objective of minimizing the dynamic response of the workpiece. The different node systems are used on the same workpiece geometry, and the fixture layout is optimized so that the elastic deformation of the workpiece satisfies the accuracy needed on the workpiece. Two different examples are used to compare the performance of ACA- and GA-based fixture-layout optimization methods.

Improvement of Machining Accuracy in Micro Cylindrical Traverse Grinding

The elastic deformation of workpiece acted by grinding force is so large as to make the low machining efficiency and accuracy in the cylindrical grinding of micro parts of which the stiffness is extremely small because of their small diameter. In this study, the perpendicular axis type cylindrical traverse grinding is proposed for the manufacture of micro parts, and the undeformed chip shape in the grinding process is investigated comparing with that in the parallel axis type traverse grinding using generally for manufacture of over small-sized parts. The surface finish and the form accuracy of workpiece by the perpendicular axis type grinding are finer than those by the parallel axis type grinding with the developed micro cylindrical grinding machine.

Machining fixture layout optimization using FEM and evolutionary techniques

Design of fixture configuration (layout) is a procedure to establish the workpiece fixture contact through positioning of clamps and locating elements such that the workpiece elastic deformation is minimized. This work is done based on the assumption that the workpiece is an elastic body. Since the fixture layout influences the dimensional and form accuracy of the workpiece during the machining process, the fixture layout has to be optimized to minimize the workpiece deformation. In this paper, the workpiece deformation is modeled using finite element method (FEM), for the problem of fixture layout optimization problems with the objective of minimizing the dimensional and form errors. This paper presents a fixture layout optimization method that uses genetic algorithm (GA) and ant colony algorithm (ACA) separately. In this paper, three different number of node systems are defined on the same workpiece geometry to find the consistency in the performance of GA and ACA. The optimal solution, i.e., the most minimum value among the entire possible layout is determined separately for all three different number of node systems. The solution obtained for each node system using GA and ACA is compared with their respective optimal solution separately and ACA reports faster and accurate solutions.