Automatic identification of feed marks in machined surface roughness profiles by correlating random variations

A new method is detailed to identify the positions of the ends of the tool marks in linear surface roughness profiles. No feed mark shape must be assumed and the process is entirely automated. The approach may find applications in automated quality control, surface texture classification, and modeling of metal cutting processes. Validation was done with 40 finish hard turning specimens. The method relies on the justified hypotheses that a feed mark profile is a superposition of a fixed and a random component, and that the random component has a spatial period equal to one feed mark length. A brief typology of tool mark particularities revealed by the method is presented as well as observations on the correlation of the random events within marks and between marks, both at short and at long range. Feed marks difficult to identify by visual inspection were easily identified with the method and evidence of overlapping tool marks and unstable regions was discovered. The limits of the method are also explored.     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial production.     

出刃高度差对金刚石有序排布工具的影响

采用单颗粒金刚石沿不同的切削路径(不同间距切痕或重叠切痕)与不同切削深度的组合形式进行了平面划擦广西白大理石的试验。分析了两种切削路径下出刃高度差对有序排布金刚石工具切削力Fz的影响,结果表明:在不同间距切痕的条件下,当ap1<20μm、L2<200μm时,第二道切痕切削力随着出刃高度差的增大而急剧增大,而当L2>600μm时,出刃高度差的增大却对第二道切痕切削力的影响较小,而当ap1>30μm时,可切削性能随着出刃高度差的增大而变差,但切痕距离越小越有利于切削加工,但影响较小;在重叠切痕条件下,当出刃高度差apx<30μm时,第二道切痕的切削力随着出刃高度差的增大而增大,而当出刃高度差apx>30μm时,却恰恰相反;在两种切削路径下,随着第一道切痕的切削深度ap1的增加,切痕重叠的可加工性能要更优于切痕间距。     

切削条件对氢化锂零件表面粗糙度的影响

通过车削试验研究了不同切削参数对氢化锂材料加工表面粗糙度的影响趋势及其成因。结果表明:表面粗糙度随切削速度和圆周进给量的增加呈增大趋势,随切削深度的增加呈减小趋势;进给量是影响表面粗糙度的主要因素。     

Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel

In the present research, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting forces and surface roughness in hard milling of AISI H13 steel with coated carbide tools. Based on Taguchi’s method, four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut) four-level orthogonal experiments were employed. Three cutting force components and roughness of machined surface were measured, and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces. The optimal cutting parameters for minimal cutting forces and surface roughness in the range of this experiment under these experimental conditions are searched. Two empirical models for cutting forces and surface roughness are established, and ANOVA indicates that a linear model best fits the variation of cutting forces while a quadratic model best describes the variation of surface roughness. Surface roughness under some cutting parameters is less than 0.25 μm, which shows that finish hard milling is an alternative to grinding process in die and mold industry.     

Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel

An experimental investigation was conducted to analyze the effect of cutting parameters (cutting speed, feed rate and depth of cut) and workpiece hardness on surface roughness and cutting force components. The finish hard turning of AISI 52100 steel with coated Al₂O₃ + TiC mixed ceramic cutting tools was studied. The planning of experiment were based on Taguchi’s L₂₇ orthogonal array. The response table and analysis of variance (ANOVA) have allowed to check the validity of linear regression model and to determine the significant parameters affecting the surface roughness and cutting forces. The statistical analysis reveals that the feed rate, workpiece hardness and cutting speed have significant effects in reducing the surface roughness; whereas the depth of cut, workpiece hardness and feed rate are observed to have a statistically significant impact on the cutting force components than the cutting speed. Consequently, empirical models were developed to correlate the cutting parameters and workpiece hardness with surface roughness and cutting forces. The optimum machining conditions to produce the lowest surface roughness with minimal cutting force components under these experimental conditions were searched using desirability function approach for multiple response factors optimization. Finally, confirmation experiments were performed to verify the pertinence of the developed empirical models.     

Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.     

基于正交试验法的W-Fe-Ni合金车削表面粗糙度研究

运用正交试验法对W-Fe-Ni合金半球试件车削加工表面粗糙度进行了试验,并采用极差分析法分析了试验结果。结果表明,球形零件的加工表面粗糙度随进给量、切削深度的增大而增大,随切削速度的增大而减小,其中进给量是最主要的影响因素,其次为切削深度,切削速度的影响很小。     

EFFECT OF PROCESS PARAMETERS AND TOOL SHAPE ON THE MACHINABILITY OF A PARTICULATE FILLED-POLYMER COMPOSITE MATERIAL FOR RAPID TOOLING

This paper presents the results of an experimental study on the effects of machining parameters (cutting speed, feed, depth of cut) and tool shape on chip formation, surface topography, resultant cutting force and surface roughness produced in flat and ball end milling of the Ren Shape-Express 2000™ aluminum particulate filled-polymer composite material. This material is shown to exhibit a brittle-to-ductile transition in chip formation with decreasing cutting speed. The transition is explained by the strain-rate sensitivity of the polymer matrix and is found to correlate well with a corresponding change in the surface roughness. The absence of clear feed marks on the milled surface explains why molds made from the composite material require less hand polishing than machined metal molds. The influence of cutting conditions and tool shape (flat end vs. ball-nose) on the cutting force, surface roughness, and workpiece breakout are discussed and relevant comparisons with conventional metal and polymer machining are made.     

An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design

Many previous researches on high-speed machining have been conducted to pursue high machining efficiency and accuracy. In the present study, the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra high-speed face milling of AISI H13 steel (46–47 HRC) are experimentally investigated. It is found that the ultra high cutting speed of 1,400?m/min can be considered as a critical value, at which relatively low mechanical load, good surface finish, and high machining efficiency are expected to arise at the same time. When the cutting speed adopted is below 1,400?m/min, the contribution order of the cutting parameters for surface roughness Ra is axial depth of cut, cutting speed, and feed rate. As the cutting speed surpasses 1,400?m/min, the order is cutting speed, feed rate, and axial depth of cut. The developing trend of the surface roughness obtained at different cutting speeds can be estimated by means of observing the variation of the chip shape and chip color. It is concluded that when low feed rate, low axial depth of cut, and cutting speed below 1,400?m/min are adopted, surface roughness Ra of the whole machined surface remains below 0.3?μm, while cutting speed above 1,400?m/min should be avoided even if the feed rate and axial depth of cut are low.     

基于粗糙度形成机理的曲面高速铣精加工参数选用

为在曲面精加工中获得理想的表面粗糙度,通过分析表面粗糙度的形成机理,建立了粗糙度与走刀行距、进给率关系的数学模型;通过实验,建立了高速曲面铣削时粗糙度与加工倾角、主运动线速度关系的图谱,实现了在生产过程中按照加工目标的表面粗糙度确定相应的走刀行距、进给率、加工倾角、主运动线速度等加工参数.研究结果表明,该研究对提高加工...     

Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis

This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.     

Multi-response optimization of machining parameters in hot turning using grey analysis

This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process.     

Surface topography and roughness of high-speed milled AlMn1Cu

The aluminum alloy AlMn1Cu has been broadly applied for functional parts production because of its good properties. But few researches about the machining mechanism and the surface roughness were reported. The high-speed milling experiments are carried out in order to improve the machining quality and reveal the machining mechanism. The typical topography features of machined surface are observed by scan electron microscope(SEM). The results show that the milled surface topography is mainly characterized by the plastic shearing deformation surface and material piling zone. The material flows plastically along the end cutting edge of the flat-end milling tool and meanwhile is extruded by the end cutting edge, resulting in that materials partly adhere to the machined surface and form the material piling zone. As the depth of cut and the feed per tooth increase, the plastic flow of materials is strengthened and the machined surface becomes rougher. However, as the cutting speed increases, the plastic flow of materials is weakened and the milled surface becomes smoother. The cutting parameters (e.g. cutting speed, feed per tooth and depth of cut) influencing the surface roughness are analyzed. It can be concluded that the roughness of the machined surface formed by the end cutting edge is less than that by the cylindrical cutting edge when a cylindrical flat-end mill tool is used for milling. The proposed research provides the typical topography features of machined surface of the anti-rust aluminum alloy AlMn1Cu in high speed milling.     

Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach

In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughness R _a, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.     

Experimental investigation on surface finish during intermittent turning of UNS M11917 magnesium alloy under dry and near dry machining conditions

In the last decades, light materials, such as magnesium, are increasing their use in a wide range of industrial applications. The growing use of magnesium encourages the study of its use under different production processes. In this sense, the present study shows an experimental investigation in intermittent turning of UNS M11917 magnesium alloy, analysing different machining conditions: cutting speed, depth of cut and feed rate, along with the use of dry machining and minimum quantity lubrication (MQL) system. To evaluate intermittent turning process, continuous bars and slotted bars were used. The process is evaluated taking as response variable the surface roughness. Full factorial experimental designs are used and their results are analysed using the Analysis of Variance (ANOVA). Main results of the statistical analysis include the identification of feed rate as the main significant factor for all the tests, explaining the most part of the variability analysed. By contrast, cutting speed and type of interruption were not found to be significant sources of variability when analysed in isolation. Moreover, more dispersion of surface roughness values, in terms of Ra, was identified when machining at low feed rates which can difficult its prediction. In addition, when analysing depth of cut, this factor and its interaction with feed rate are also found to be significant sources of variability for Ra. Finally, the influence of the environment used in the machining process is also studied, finding that the use of the MQL system can lead to slightly worse surface roughness (Ra) results at the higher feed rates tested.     

Elliptical vibration cutting of hardened die steel with coated carbide tools

Elliptical vibration cutting of hardened die steel with coated carbide tools is examined in this research in order to achieve low-cost high-precision machining. Diamond coated tools are applied because of superior hardness of their polycrystalline diamond coating and its low manufacturing cost. TiN coated tools are also tested, since they are widely used for conventional machining of steels. Machinability of hardened die steel by the elliptical vibration cutting with coated carbide tools is discussed in three aspects in this study, i.e. transferability of cutting edge profile to cut surface, cutting force, and tool life. The transferability is evaluated quantitatively by calculating correlation coefficients of measured roughness profiles. It is clarified that the diamond coated tools have high transferability which leads to diffraction of light on the surface machined at micro-scale pick feed. Total cutting forces including ploughing components are measured at various feed rates, and then shearing components and ploughing components are separated utilizing linear regression. The measured results indicate, for example, that the all forces become considerably smaller only when elliptical vibration is applied to the TiN coated tool without cutting fluid. It is also found that this considerable reduction of forces interestingly corresponds to higher friction coefficient, which is identified from the ploughing components. Tool life tests are carried out by various machining methods, i.e. elliptical vibration/ordinary wet/dry cutting with diamond/TiN coated tools. The result shows, for example, that the flank wear is smallest in the wet elliptical vibration cutting with the diamond coated tool.     

PERFORMANCE PREDICTION MODELS FOR TURNING WITH ROUNDED CORNER PLANE FACED LATHE TOOLS. I. THEORETICAL DEVELOPMENT

In this paper the need for reliable quantitative machining performance information for efficient and effective use of machining operations is discussed, as are the recent developments of predictive models for forces and power in practical machining operations based on the 'unified mechanics of cutting approach'. This investigation is aimed at extending this mechanics of cutting approach to turning with rounded corner plane faced lathe tools. Three predictive models for the forces, power and chip flow angle based on the 'unified mechanics of cutting approach1 have been developed while the surface roughness models have been based on the feed marks generated on the machined surface allowing for the precise tool corner profile. The first force model is based on the modified mechanics of cutting analyses for single edge tools while the two alternative models are based on the generalised mechanics of cutting analyses for single edge and multi-edge form tools for the turning cut as a whole. The predictive force models incorporate the effects of the major tool geometrical variables including the corner radius, the cutting conditions as well as the effect of TiN coating. This first paper will outline the development of the models while the proposed models will be numerically tested and experimentally verified qualitatively and quantitatively in the subsequent parts of this investigation.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach

In present work performance of coated carbide tool was investigated considering the effect of work material hardness and cutting parameters during turning of hardened AISI 4340 steel at different levels of hardness. The correlations between the cutting parameters and performance measures like cutting forces, surface roughness and tool life, were established by multiple linear regression models. The correlation coefficients found close to 0.9, showed that the developed models are reliable and could be used effectively for predicting the responses within the domain of the cutting parameters. Highly significant parameters were determined by performing an Analysis of Variance (ANOVA). Experimental observations show that higher cutting forces are required for machining harder work material. These cutting forces get affected mostly by depth of cut followed by feed. Cutting speed, feed and depth of cut having an interaction effect on surface roughness. Cutting speed followed by depth of cut become the most influencing factors on tool life; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower feed value, lower depth of cut and by limiting the cutting speed to 235 and 144 m/min; while turning 35 and 45 HRC work material, respectively, ensures minimum cutting forces, surface roughness and better tool life.