Modeling Of Machining Process Research Articles

Modeling of Abrasive Water Jet Machining Process

This paper presents the modeling of abrasive water jet cutting and development of analytical expressions for material removal rate during abrasive water jet machining process. Water jet machining employs the principle of increasing the pressure of water and to subsequently discharge the water through an orifice. Normally, the diameter of the orifice varies from 0.8 to 1 m. The water jet impinging on the chosen work surface generates sufficient force to cut the work material.Considering theoptimization of the process, few factors appearsto be influential.They are: velocity of the water jet, diameter of the nozzle and the average pressure of the jet.Furthermore, in the modeling, the explanation for higher width of kerf at the top than that of bottom is obtained.In this regard, the flow rate equation of water is determined. By using the expression for flow rate, the power equation for water jetis developed. With regard to material removal rate, a linear relationship with power of the water jet is evident. Additionally, the characteristics of kerf formed during cutting based on energy loss during cutting process using the relation between material removal rate and power of the water jetis assessed.

Development of energy consumption model of abrasive machining process by a combined evolutionary computing approach

Abrasive machining is employed for improving surface characteristics of components used in oil and gas applications. Optimization of power consumed in abrasive machining process is vital from environmental standpoint that requires the formulation of the generalized and an explicit mathematical model. In the present work, we propose to study the power consumption in abrasive machining process using a combined evolutionary computing approach based on Multi-Adaptive Regression Splines (MARS) and Genetic Programming (GP) techniques. Sensitivity and parametric analysis have also been conducted to capture the dynamics of process by unveiling dominant input variables and hidden non-linear relationships. It is concluded that selection of optimal machining time and abrasive is necessary for achieving better environmental performance of abrasive machining process.

Modeling of the Magnetic Abrasive Machining Process of Flat Surface Workpieces on Numerically Controlled Machine Tools

The article describes a mathematical model of the circular motion trajectory of a magnetic abrasive powder portion which participates in the magnetic abrasive machining process of flat surface workpieces. The motion trajectory of a magnetic abrasive powder portion is observed. The main formulas, assumptions and recommendations on the implementation of the mathematical model are introduced. Taking into account the feed rate of the machine table, rotational speed and the radius of the cylindrical magnetic inductor, the model allows determining an optimal amount of the magnetic abrasive powder portion which can provide the required efficiency of the finishing process. The magnetic abrasive machining process does not have any fixed standard cutting parameters, so they have to be readjusted every time. The given model can be used to predict the parameters of the finishing process of sophisticated flat surface workpieces.

Uncertainty Management of Cutting Forces Parameters and its Effects on Machining Stability

Virtual manufacturing is a field of research which numerically simulate all the manufacturing processes seen by a mechanical part during its production (for example casting, forging, machining, heat treatment,…). Its use is rising on various industries to reduce production costs and improve quality of manufactured parts. One of the most challenging component of the whole simulation chain is the simulation of machining operations due to some of its specificities (need of material law at high strain, strain rates and temperature, heterogeneities of machined material, influence of residual stresses,…).In order to circumvent these difficulties, macroscopic models of machining process have been developed in order to compute more global information (cutting forces, stability of the process, tolerance or roughness for example). For this approach, the cutting forces computation is done by using simple analytical law based on mechanistic approach. The parameters of the models have no clear physical meaning (or at least cannot be linked to intrinsic properties of the material to be machined) and are therefore considered constants for a given set of simulations.The aim of this paper is to take into account the uncertainty on the variability of the cutting force signal during machining operation used as input for mechanistic model identification. The variability of the response during a test on fixed conditions (cutting tool, machined material and cutting parameters) is taken into account to develop a model where parameters of the model can evolve during a given operation.The proposed model is then used as an input of a milling operation simulation in order to study its influence on machining stability as compared to a classical approach.

A systematic method for the automatic update and propagation of the machining process models in the process modification

Dynamically changing the machining condition and uncertain manufacturing resource availability are forcing the computer-aided process planning system to enhance the modification function of 3D process model. During the process planning, the process modification is an inevitable process activity for a new machined part. For updating the changed process models accurately and timely, this paper proposes a systematic method for the automatic update and propagation after modifying the process. Firstly, the process model is analyzed and decomposed into two parts: the process model information and the process attribute information. Secondly, based on the precedence relations of the process models in machining, the relationships among them are divided into two types: the dependency relation and the independency relation. Then, in order to ensure the validity of the changed process models, the determination method of the relationship among process models is proposed. Finally, the automatic update and propagation method of the process models is proposed by considering the relationships among them. The proposed method is verified by application of two examples.

Machining Process & Information Modeling Based on MBD Procedure Machining Cell

To describe the machining process and information of mechanical parts in petroleum engineering, the paper defines MBD machining process model from the perspective of process parts; analyzes evolutionary law of geometric features information in the course of part machining process and creates the concept of procedure machining cell; makes clear description about machining process based on procedure machining cell sequence to achieve the expression of machining process information; analyzes geometric features of procedure machining cell, proposes extended AAG based on AAG by combing with examples and links with attribute information table to complete the modeling of geometric and non-geometric features information about procedure machining cell. The research made in this paper provides a basic framework for integration of process information in 3D CAPP system.

Modeling and multi-objective optimization of powder mixed electric discharge machining process of aluminum/alumina metal matrix composite

Low material removal rate (MRR) and high surface roughness values hinder large-scale application of electro discharge machining (EDM) in the fields like automobile, aerospace and medical industry. In recent years, however, EDM has gained more significance in these industries as the usage of difficult-to-machine materials including metal matrix composites (MMCs) increased. In the present work, an attempt has been made to fabricate and machine aluminum/alumina MMC using EDM by adding aluminum powder in kerosene dielectric. Results showed an increase in MRR and decrease in surface roughness (Ra) compared to those for conventional EDM. Semi empirical models for MRR and Ra based on machining parameters and important thermo physical properties were established using a hybrid approach of dimensional and regression analysis. A multi response optimization was also performed using principal component analysis-based grey technique (Grey-PCA) to determine optimum settings of process parameters for maximum MRR and minimum Ra within the experimental range. The recommended setting of process parameters for the proposed process has been found to be powder concentration (Cp) = 4 g/l, peak current (Ip) = 3 A, pulse on time (Ton) = 150 μs and duty cycle (Tau) = 85%.

Optimal spindle speed determination for vibration reduction during ball-end milling of flexible details

In the paper a method of optimal spindle speed determination for vibration reduction during ball-end milling of flexible details is proposed. In order to reduce vibration level, an original procedure of the spindle speed optimisation, based on the Liao–Young criterion [1], is suggested. As the result, an optimal, constant spindle speed value is determined. For this purpose, non-stationary computational model of machining process is defined. As a result of modelling, a hybrid system is described. This model consists of following subsystems, i.e. stationary model of one-side-supported flexible workpiece (modal subsystem), non-stationary discrete model of ball-end mill (structural subsystem) and conventional contact point between tool and workpiece (connective subsystem). The method requires identification of some natural frequencies of stationary modal subsystem. To determine them, appropriate modal experiments have to be performed on the machine tool, just before machining. Examples of vibration surveillance during cutting process on two high speed milling machines Mikron VCP 600 and Alcera Gambin 120CR are illustrated.

An effective numerical control machining process reuse approach by merging feature similarity assessment and data mining for computer-aided manufacturing models

To effectively reuse existing numerical control machining process of similar feature, a novel numerical control machining process reuse approach by merging feature similarity assessment and data mining for computer-aided manufacturing models is proposed. First, a feature-based numerical control machining process model is proposed to formalize the links between machining feature and its associated machining operations. Based on feature-based numerical control machining process model, the machining know-how database is automatically generated through data mining for existing computer-aided manufacturing models. Then, an effective numerical control machining process reuse–enabled feature similarity assessment approach is presented to locate the similar features. Moreover, two numerical control machining process reuse modes are explored to reuse the associated machining operations of the selected similar feature. Finally, a prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

A point-based variation propagation model for multi-pass machining process

In a machining process, the workpiece is usually machined by several passes to achieve the desired dimension and form. Workpiece deviation generated in the previous pass will affect the next pass. A new point-based variation propagation model for a single machining process with multi-passes was proposed in this article to investigate the relationships between different passes. The effects of locating errors, geometric errors of machine tool, and tool deflection on workpiece deviation are analyzed. Based on these analyses, a point-based variation propagation model for a machining process with multi-passes was derived. By including the influence of the tool deflection, a linkage between workpiece deviation and the cutting parameters is established. Since different passes are distinguished by their cutting parameters, the relationships between different passes are built up via this model. At the end of this article, case studies were carried out to validate the model and its application in cutting parameters’ optimization.

Modeling of Cutting Force Distribution on Tool Edge in Turning Process

Machining process models can be used to predict cutting force/power and further optimize the process parameters. In this work, a mechanistic cutting force model for turning processes is developed. In order to accurately predict cutting forces, the cutting tool edge is discretized and an approach for calculating the chip load corresponding to each discretized edge segment is developed. Approach for calculating effective tool angles is also developed considering effects of the tool corner radius and the change of feed direction. By using the yield shear stress of the work material, the friction angle between work and tool material, the chip load and the effective tool angles for each tool edge segment, the distributions of the turning forces and force intensity along the tool edge can be predicted. Turning cases of straight turning, contour turning, taper turning and facing are tested and the model outputs include the distributions of the instantaneous effective tool angles, chip load, cutting force coefficients and force intensity. Test results for straight turning and contour turning operations are analyzed to demonstrate the model capability. The distributions of force and force intensity on tool edge provide useful information for prediction of turning forces/power and potential further prediction of tool wear/life.

Experimental identification of tool holder acceleration in the process of longitudinal turning

Planning of the modern manufacturing systems that would be suitable for the most demanding cutting processes requires analysis of all technical and technological process parameters by applying scientific methods, modeling and optimal conditions of machining process and systems defining. In order to secure the planned functioning of machine tools they need to be sufficiently resistant and stable at all influences which are exposed in the cutting process. Because high number of influence factors to the cutting process, it is very difficulty to determine most influence parameters of the cutting process by analytical model. For this reason it is necessary to apply the experimental measurements and analysis of the results which will be used for creating fudantion for cutting force identification and which is indispensable in the development of new machine tools and improving existing machining systems. Objective of this paper is to find mathematical algorithm of tool holder acceleration aNA = f(σz, v, δ) in function of machined material strength, cutting velocity and depth of cut. Experimental investigation of tool holder acceleration during turning process has done for three different types of steel: 9SMn28, S355JOG3 and C45E.

Interdisciplinary modelling of the electrochemical machining process for engine blades

In this paper an interdisciplinary simulation model of the electrochemical machining (ECM) process for aero engine blades is set up. Simulation results of a compressor vane are presented and compared to optical measurements of actual manufactured workpieces. The simulation model is based on conservation equations for the electric field, fluid flow and heat transfer in combination with analytical functions for the influence of temperature and gas evolution on the specific electrical conductivity. Validation is done by optical in situ measurements. Finally construction rules for an inverse modelling approach are examined and simulation results of the calculated cathode geometry are presented.

Numerical modelling and simulation of orthogonal machining process using FE-code

A numerical model is developed using general purpose finite element code to simulate the orthogonal machining process. Also, computational procedure is proposed for modelling and simulation of metal cutting process. The model is applied to calculate induced residual stresses over surface and sub-surface layers of work-piece. Dynamic explicit time integration technique with Arbitrary Lagrangian Eulerian (ALE) boundary condition is used to simulate metal removal process. Material models have been created using Johnson-Cook flow stress and damage laws. Novelty of current work is that the real time metal cutting behaviour with smooth and continuous chip formation is modelled by modifying material flow stress and damage parameters. Also, it is stated that, accuracy of such simulations is greatly influenced by material models and mesh definition. Findings of this paper provide useful insight about understanding and improving of orthogonal metal cutting process. Model predictions are compared with experimental data of residual stresses under various cutting conditions for validation. The results of simulation are consistent with experimental observations.

An optimised back propagation neural network approach and simulated annealing algorithm towards optimisation of EDM process parameters

The present research addresses the multi-criteria modelling and optimisation of electrical discharge machining (EDM) process, via optimised back propagation neural networks (OBPNN) and simulated annealing (SA) algorithm. The process response characteristics considered are material removal rate, surface roughness, and tool wear rate. The process input parameters include voltage, peak current, pulse off time, and pulse on time and duty factor. The three performance characteristics are combined into a single objective using weighted normalised grades (WNG) obtained from experimental study based on Taguchi method, to develop the artificial neural network (ANN) model. In order to enhance the prediction capability of the proposed model, its architecture is tuned by SA algorithm. Next, the developed model is embedded into SA algorithm to determine the best set of process parameters values for an optimal set of outputs. Experimental results indicate that the proposed optimisation procedure is quite efficient in modelling and optimisation of EDM process parameters. [Received 25 January 2015; Revised 12 April 2015; Accepted 3 May 2015]

Sustainable machining of high temperature Nickel alloy – Inconel 718: part 2 – chip breakability and optimization

Abstract Global competition, stricter environmental legislation and demands for fulfilling sustainability initiatives, are increasing the pressure on the manufacturing industry, to develop and implement in their production alternative sustainable machining processes. This work focuses on sustainability evaluation and comparison in machining of high-temperature Nickel-based alloy (Inconel 718). Four sustainable machining alternatives (dry, near-dry (MQL), cryogenic and cryo-lubrication (cryogenic + near-dry)) to conventional machining, are evaluated based on the performance models from the Part 1 of the paper and used in the optimization procedure for determining the overall optimum machining conditions. As chip breakability is important machining characteristic, performance-based models of sustainable machining processes, developed in Part 1, are upgraded in this Part 2 of the paper with an additional model for chip breakability. Updated models are used as inputs to the optimization procedure developed, based on the combined overall desirability function. Genetic algorithms (GA) are used to evaluate and optimize the machining process for each cooling/lubrication (C/L) application. Results of the work show that machining performance optimization, when including beside machining parameters also the sustainable alternative cooling/lubrication conditions, can provide further improvement of the processes through tool-life, machined surface quality, chip breakability, productivity and nevertheless power consumption.

Sustainable machining of high temperature Nickel alloy – Inconel 718: part 1 – predictive performance models

This two-part paper presents the results from modeling and optimization of alternative sustainable machining processes of Inconel 718. Nickel and Titanium, as representatives of high temperature alloys, with their specific thermo-mechanical properties, pose significant difficulties in machining. The high temperature and the consequent work hardening of these materials during the machining processes adversely affect cutting forces, tool-wear, surface integrity and chip breakability. This first part (Part 1) of the paper presents the experimental study of sustainable high performance machining of Inconel 718 with the development of performance-based predictive models for dry, near-dry (MQL), cryogenic and cryo-lubrication (cryogenic + near-dry) machining processes using response surface methodology (RSM). The robustness of these models was verified using the ANOVA. Cutting forces, surface roughness and tool-wear were measured, analyzed and modeled. The results show that the cooling/lubrication condition has an influence on machining performance and that optimum machining conditions have to be determined, at which an improved overall machining performance can be achieved, while sustainability directions are followed in terms of reducing cutting forces/power consumption, the prolonging of tool-life, and increasing productivity. The models developed in the first part of the paper are used for process evaluation and optimization in the second part, to determine optimum machining conditions for an overall process performance improvement.

Finite element modeling and analysis of powder mixed electric discharge machining process for temperature distribution and volume removal considering multiple craters

Powder mixed electric discharge machining (PMEDM) is one of the modern developments in electric discharge machining (EDM) process. In the present work, finite element modeling has been carried out considering randomly oriented multiple sparks during PMEDM. Transient thermal analysis is done to obtain temperature distribution, volume removal, and proportion of volume removed by melting and evaporation at different current, pulse on time and fraction of heat that enters to work piece. Gradually growing spark behavior and Gaussian distribution of heat source is used to simulate multiple craters. Temperature distribution along radial direction shows peak temperature at center of spark and thereafter a gradual decrease with increase in radial distance. Along depth direction temperature sharply decreases that forms wider craters with shallow depth in PMEDM. Peak temperature and volume removal increases with current more rapidly. Volume removal by melting is much higher than evaporation at lower current settings and with higher current almost equal amount of material is removed by melting and evaporation thus reducing the re-solidification of melted material. Current plays a significant role behind the contribution of material removal by evaporation followed by fraction of heat. Increase in pulse on duration increases the total volume of material removal however does not significantly increase the proportion of volume removal by vaporization.

Modeling and Simulation of Cylindrical Electrochemical Magnetic Abrasive Machining Process

Cylindrical Electrochemical Magnetic Abrasive Machining (C-EMAM) is an advanced abrasion-based hybrid machining process that constitutes magnetic abrasive machining and electrochemical dissolution. During the C-EMAM process, a large amount of material is removed from the peaks of the surface irregularities under the simultaneous effect of electrochemical dissolution, abrasion and abrasion-passivation synergism. This article presents the mathematical modeling for material removal and surface roughness during the C-EMAM process. Magnetic potential distribution between the two magnetic poles in which a cylindrical workpiece was placed was calculated using the finite element method. It was further used to find the forces acting on the ferromagnetic particles at contact surfaces. An empirical relation has been also developed considering the effect of electrochemical dissolution and abrasion-passivation synergism based on experiments conducted on a self-developed C-EMAM setup. Finally, a surface roughness model was developed by considering the total volume of material removed with the assumption of a triangular surface profile. The simulated results for material removal and surface roughness were validated using self-conducted experimental results. The computed results were found to be in good agreement with experimental observations.

Machining process modeling by means of FEM and the use of metamodels with fuzzy logic and linear regression

In this work it was implemented a numerical model that simulates the process of orthogonal machining of metallic materials by means of the Finite Elements Method (FEM). For the development of the model, the program Ansys Flotran was used. The material was simulated as a high viscosity fluid crashing against a solid with the cutting edge geometry. From the numerical model it was made a factorial fractionated design of experiments using Minitab software, where the selected input variables were: cutting speed, depth of cut, rake angle, density and viscosity of the fluid. The considered output variables were the velocity of chip and the position of the stagnation point. Two functional metamodels of the FEM model were made, the first one by determining the empirical equations by means of a linear regression. The second was made by establishing functions based on fuzzy logic. The experiments with both metamodels showed that the depth of cut and the rake angle have the greater influence in the chip speed and in the position of the stagnation point. The metamodel based on fuzzy logic presented a better representation between the input and output variables.