Surface textural analysis using acousto optic emission- and vision-based 3D surface topography��a base for online tool condition monitoring in face turning

In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. Thus, by monitoring the machined surface topography of the workpiece and extracting the relevant information the cutting process and tool wear state should be able to be monitored and quantified. But the effects of vibrations have been paid less attention. The work in the present paper is divided into two parts. First part consists of a data acquisition and signal processing using acousto optic emission sensor (i.e., laser Doppler vibrometer) for online tool condition monitoring and the second part of the work presents the surface topography analysis of machined surfaces during the progression of the tool wear. Most of the work presented is also a study where surface metrology is being used to measure all aspects of the machining in combination with an online metrology tool. The encouraging results of the work pave the way for the development of a real-time, low cost, and reliable tool?Ccondition?Cmonitoring system. A high degree of correlation is established between the results of the acousto optic emission signal- and vision-based surface textural analysis in identification of tool wear state.     

<Emphasis Type="Bold">Prediction of Surface Topomorphy and Roughness in Ball-End Milling</Emphasis>

Milling is today the most effective, productive and flexible-manufacturing method for machining complicated or sculptured surfaces. Ball-end tools are used for machining 3D freeform surfaces for dies, moulds, and various parts, such as aerospace components, etc. Milling data, such as surface topomorphy, surface roughness, non-deformed chip dimensions, cutting force components and dynamic cutting behaviour, are very helpful, especially if they can be accurately produced by means of a simulation program. This paper presents a novel simulation model, the so-called MSN-Milling Software Needle program, which is able to determine the surface produced and the resulting surface roughness, for ball-end milling. The model simulates precisely the tool kinematics and considers the effect of the cutting geometry on the resulting roughness. The accuracy of the simulation model has been thoroughly verified, with the aid of a wide variety of cutting experiments. Many roughness measurements were carried out on workpieces, which were cut using a 5-axis machining centre. The calculated roughness levels were found to be in agreement with the experimental ones. The proposed model has proved to be suitable for determining optimal cutting conditions, when finishing complex surfaces. The software can be easily integrated into various CAD-CAM systems.     

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.     

Surface roughness modeling and optimization of tungsten–copper alloys in micro-milling processes

Nowadays, the micrometric and nanometric dimensional precision of industrial components is a common feature of micro-milling manufacturing processes. Hence, great importance is given to such aspects as online metrology and real-time monitoring systems for accurate control of surface roughness and dimensional quality. A real-time monitoring system is proposed here to predict surface roughness with an estimation error of 9.5%, by using the vibration signal that is emitted during the milling process. In the experimental setup, the z-axis component vibration is measured using two different diameters under several cutting conditions. Then, an adaptive neuro-fuzzy inference system model is implemented for modeling surface roughness, yielding a high goodness of fit indices and a good generalization capability. Finally, the optimization process is carried out by considering two contradictory objectives: unit machining time and surface roughness. A multi-objective genetic algorithm is used to solve the optimization problem, obtaining a set of non-dominated solutions. Pareto front representation is a useful decision-making tool for operators and technicians in the micro-milling process. An example of the Pareto front utility-based approach that selects two points close to both extreme ends of the frontier is described in the paper. In the first case (point 1), machine time is of greater importance, and in the second case (point 2), importance is attached to surface roughness. In general terms, users can select different combinations, at all times moving along the Pareto front.     

A MACHINING PERFORMANCE STUDY IN DRY CONTOUR TURNING OF ALUMINUM ALLOYS WITH FLAT-FACED AND GROOVED DIAMOND TOOLS

This paper presents the results from an experimental study of dry contour turning operations on aluminum alloys (6061B and 2011-T3) using PCD flat-faced and diamond coated grooved tools. The machining performance is assessed on the basis of cutting forces, chip flow, chip-form and surface roughness observed during contour turning operations. The constantly varying cutting conditions (especially effective depth of cut due to varying geometry of the contour surface) and effective tool geometry cause a wide fluctuation in cutting forces and the ensuing chip flow. The chip flow angle is measured along the contour geometry using high-speed filming techniques and these results are compared with predicted chip flow values from the measured experimental cutting forces (which are measured along the entire contour geometry). The resultant surface roughness at different locations along the contour profile is measured and correlated with the chip flow and chip-form variations. Machining performance issues specifically relevant to dry contour turning of aluminum (such as problems due to poor chip flow and the resultant poor surface roughness) are studied and the effectiveness of selective work-tool (both tool material and tool geometry) pairs is illustrated.     

Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V

The present study focuses on the development of predictive models of average surface roughness, chip-tool interface temperature, chip reduction coefficient, and average tool flank wear in turning of Ti-6Al-4V alloy. The cutting speed, feed rate, cutting conditions (dry and high-pressure coolant), and turning forces (cutting force and feed force) were the input variables in modeling the first three quality parameters, while in modeling tool wear, the machining time was the only variable. Notably, the machining environment influences the machining performance; yet, very few models exist wherein this variable was considered as input. Herein, soft computing-based modeling techniques such as artificial neural network (ANN) and support vector machines (SVM) were explored for roughness, temperature, and chip coefficient. The prediction capability of the formulated models was compared based on the lowest mean absolute percentage error. For surface roughness and cutting temperature, the ANN and, for chip reduction coefficient, the SVM revealed the lowest error, hence recommended. In addition, empirical models were constructed by using the experimental data of tool wear. The adequacy and good fit of tool wear models were justified by a coefficient of determination value greater than 0.99.     

PREDICTIVE MODELING OF MACHINING PERFORMANCE IN TURNING OPERATIONS

Recent definitions of machining performance have been based on technological machining performance measures such as cutting forces, tool-life/tool-wear, chip-form/chip breakability, surface roughness, etc. However, modeling work on these performance measures has so far been characterized by isolated treatment of each of these measures. The modeling approach followed by the machining research group at the University of Kentucky aims for an integrated predictive modeling methodology for the major technological machining performance measures. Extensive use of analytical, experimental, numerical, and Al-based approaches is made in the development of these predictive models. This paper presents the outline of this modeling effort and reports the progress made to date in implementing it.     

切削参数对不锈钢加工表面粗糙度的影响

通过切削试验研究了进给量、切削速度、刀具圆弧半径以及切屑形态等四个因素对不锈钢加工表面粗糙度的影响规律,确定了降低表面粗糙度的切削参数优化组合。     

Experimental study of the effect of tool orientation in five-axis micro-milling of brass using ball-end mills

The effect of tool orientation on the final surface geometry and quality in five-axis micro-milling of brass using ball-end mills is investigated. Straight grooves with a semicircular cross section are cut with different tool inclination and tilt angles, and the resulting surfaces are characterized using an optical profilometer and microscope. Micro-milling cutting forces are recorded synchronously with spindle electric current and cutting motions in order to investigate the correlation between the tool orientation and the achieved surface quality. Results of various cutting experiments and analysis of the final surface geometry show that varying the tool orientation reduces rubbing of the material at the bottom of the grooves, which often occurs in ball-end milling of brass, and improves the final surface quality. The experimental analysis for surface roughness shows that applying a tool inclination angle of 15° can considerably improve the surface roughness at the bottom of the grooves. Analysis of static and averaged peak-to-valley (P-to-V) values of the cutting forces show that the static cutting force values are reduced by half when the tool inclination was increased from 0 to 15°. P-to-V cutting force values in along-the-feed direction were also decreased in the inclined machining.     

Prediction of cutting forces and machining error in ball end milling of curved surfaces -I theoretical analysis

This paper presents a theoretical model by which cutting forces and machining error in ball end milling of curved surfaces can be predicted. The actual trochoidal paths of the cutting edges are considered in the evaluation of the chip geometry. The cutting forces are evaluated based on the theory of oblique cutting. The machining errors resulting from force induced tool deflections are calculated at various parts of the machined surface. The influences of various cutting conditions, cutting styles and cutting modes on cutting forces and machining error are investigated. The results of this study show that in contouring, the cutting force component which influences the machining error decreases with increase in milling position angle; while in ramping, the two force components which influence machining error are hardly affected by the milling position angle. It is further seen that in contouring, down cross-feed yields higher accuracy than up cross-feed, while in ramping, right cross-feed yields higher accuracy than left cross-feed. The machining error generally decreases with increase in milling position angle.     

CORRELATION-BASED ESTIMATION OF CUTTING FORCE COEFFICIENTS FOR BALL-END MILLING

This article presents a methodology to estimate cutting force coefficients based on the least squares approximation using correlation factor between the estimated and measured cutting forces in order to determine the corresponding tool angular position. This method can be applied on measured cutting force data over any small interval of time that need not contain information of the time instant when the cutting tool enters the workpiece, which has been the main requirement in the conventional method. This allows a quick estimation of the cutting force coefficients regardless of the chosen cutting conditions and tool-workpiece material, which is often the case in industrial machining processes. This proposed method has been validated by comparison of cutting force coefficients obtained using conventional estimation technique for a slot ball-end milling test. Besides being useful for predictive evaluation of forces, such estimation of cutting force coefficients of the cutting force model can be useful for understanding variations in cutting process over the tool life and can assist in online monitoring and process optimization.     

END MILLING SYSTEM COMPLIANCE AND MACHINING ERROR

Tool deflection resulting from cutting forces places a constraint on the achievable precision and productivity in machining. This paper presents an analytical model of machining error, in terms of part form deviation in end milling due to the elastic compliance of cutting tool. Based on the relationship of local cutting forces and chip thickness, the shear loading and bending moment on the tool cross section are presented in terms of cutter angular position. The tool deflection resulting from the bending moment is then established from the principle of virtual work. The resulting deflection of workpiece and machine tool structure is also considered through shear loading analysis. The expression for machining error is derived as a closed-form function of the machining parameters, cutting configuration, material characteristics, and machine receptance. End milling experiments were conducted to verify the analytical model under various cutting conditions. Error maps are presented to illustrate the effects of process conditions on the achievable part accuracy.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

PCD刀具几何参数对铝合金加工表面粗糙度的影响

文中研究了PCD刀具在不同的刀具几何参数下车削铝合金的加工表面粗糙度.分别改变刀具的前角、后角和刀尖圆弧半径3个几何参数做单因素切削试验,试验后利用表面轮廓仪测量工件的表面粗糙度,最后分析刀具几何参数对加工表面粗糙度的影响.     

Formation of iron aluminide coatings on plain carbon steel by TIG process

Micro end-milling is widely used in many industries to produce micro products with complex 3D shapes. The accurate modeling and prediction of surface roughness are important for evaluating the productivity of the machine tools and the surface quality of the machined parts. This paper presents an accurate surface roughness model based on the kinematics of cutting process and tool geometry by considering the effects of tool run-out and minimum chip thickness. The proposed surface roughness model is validated by micro end-milling experiments with the miniaturized machine tool. The results show that the proposed surface roughness model can accurately predict both the trends and magnitude of the surface roughness in micro end-milling.     

Prediction of surface roughness in slotting of CFRP

Nowadays, more attention is drawn to the industrial products made of composite materials. Their anisotropy and inhomogeneity cause a difficulty in predicting their behavior during machining. With the purpose of understanding and zooming the contact area tool/workpiece, this paper presents a study that evaluate the transverse and longitudinal roughness measurements for knurled tool in slotting of multidirectional carbon fiber-reinforced plastic (CFRP) laminate. By transverse (respectively longitudinal) roughness we mean roughness measured perpendicular to the advance direction (respectively in the advance direction). A theoretical model of transverse roughness is given: it highlights its dependance of only tool geometry. Experiments were carried out to validate the model, to study how longitudinal roughness measurements depends on cutting conditions (cutting speed and feed per tooth) and to predict the surface topography.     

END MILLING SYSTEM COMPLIANCE AND MACHINING ERROR

Abstract

Tool deflection resulting from cutting forces places a constraint on the achievable precision and productivity in machining. This paper presents an analytical model of machining error, in terms of part form deviation in end milling due to the elastic compliance of cutting tool. Based on the relationship of local cutting forces and chip thickness, the shear loading and bending moment on the tool cross section are presented in terms of cutter angular position. The tool deflection resulting from the bending moment is then established from the principle of virtual work. The resulting deflection of workpiece and machine tool structure is also considered through shear loading analysis. The expression for machining error is derived as a closed-form function of the machining parameters, cutting configuration, material characteristics, and machine receptance. End milling experiments were conducted to verify the analytical model under various cutting conditions. Error maps are presented to illustrate the effects of process conditions on the achievable part accuracy.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

基于声表面波原理的切削力测量智能刀具研究

切削力的测量对于监测加工过程以及获得高精度的零部件具有重要作用,为实现自适应加工提供切削状态参数。研究了一种基于声表面波原理的切削力测量智能刀具。能在切削加工中实现主切削力的实时测量,并具有无线、无源的测量优势,能够适应复杂的加工环境。建立了切削力与声表面波谐振器石英基片应变的关系模型,分析了声表面波谐振器谐振频率,得到了切削力与声表面波谐振频率偏移量的关系。实验结果表明,基于声表面波原理的切削力测量智能刀具能够实现切削力的实时测量。