Surface finish prediction models for fine turning

Surface finish data were generated for aluminium alloy 390, ductile cast iron, medium carbon leaded steel 10L45, medium carbon alloy steel 4130, and inconel 718 for a wide range of machining conditions defined by cutting speed, feed and tool nose radius. These data were used to develop surface finish prediction models, as a function of cutting speed, feed, and tool nose radius, for each individual metal. A general purpose surface finish prediction model is also proposed for ductile cast iron, medium carbon leaded steel, and alloy steel. Statistical analysis of experimental data indicated that surface finish is strongly influenced by the type of metal, speed and feed of cut, and tool nose radius. While the effects of feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved with speed for ductile cast iron, medium carbon leaded steel, medium carbon alloy steel, and aluminium alloy, but it deteriorated with speed for inconel. Apparently, speed effect on surface finish is not always positive. For all metals, the surface finish improved with the tool nose radius while it deteriorated with speed.     

An application of goal programming technique in metal cutting

This paper outlines the use of the goal programming technique in selecting levels of machining parameters in a fine turning operation on A1S1 4140 steel using cemented tungsten carbide tools. Goals that are proposed to be achieved are: (i) to finish turning the required depth in one pass, mid (ii) to finish turning within a stipulated time. Constraints used are: R.M.S. surface finish values, cutting horse power of the machine, ranges for cutting speed, feed and depth of cut. A predictive equation to predict the R.M.S. Surface roughness values from the machining variables, cutting speed, feed, depth of cut, and time of cut was used. This mathematical model was developed using stepwise regression analysis on the experimental data for 1/64 in. nose radius cemented tungsten carbide cutting tool. Experiment with the machining variables at different levels were performed to obtain the data. A statistically designed experiment called the rotatable design was used for the experimental design     

Prediction and optimisation models for turning operations

Selection of process parameters has very significant impact on product quality, production costs and production times. The quality and cost are much related to tool life, surface roughness and cutting forces which they are functions of process parameters (cutting speed, feed rate, depth of cut and tool nose radius). In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. The process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machineability models. Two data mining techniques are used; response surface methodology and neural networks. The data of 28 experiments have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the selected tool/material combination. The resulting models are utilized to formulate an optimisation model and solved to find optimal process parameters, when the objective is minimising production cost per workpiece, taking into account the related boundaries and limitation of this multi-pass turning operations. Numerical examples are given to demonstrate the suggested optimisation models.     

Optimization of Tool Geometry and Cutting Parameters for Hard Turning

In the hard-turning process, tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. So reliable models and methods are required for the prediction of the output performance of the process. In the present work, experimental investigation has been conducted to see the effect of the tool geometry (effective rake angle and nose radius) and cutting conditions (cutting speed and feed) on the surface finish during the hard turning of the bearing steel. First- and second-order mathematical models were developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using genetic algorithms. The genetic algorithm program gives minimum values of surface roughness and their respective optimal conditions.     

Effect of machining parameters on turning of VAT32® superalloy with ceramic tool

The objective of this research was to study the machining of superalloy VAT32® using alumina-based ceramic tool without cutting fluid, applying different machining parameters to evaluate the surface finish of parts, vibration and main wear of tools. For this, a turning process with a linear trajectory of 30 mm was performed, in which were collected data vibration and surface roughness of the stretch, as well as wear and damage in the tools. The turning tests were performed utilizing cutting speeds of 270, 280 and 300 m/min, a feed of 0.10, 0.18 and 0.25 m/rev and a cutting depth of 0.50 mm. With results, it was identified that the feed influenced significantly both the vibration and the system, since the cutting speed influenced only the vibration. Being that the best results happened for the smaller feed and greater cutting speed. It concludes that the machining of superalloy VAT32® with ceramic tool introduced herself promising.     

Formulation of Surface Roughness Models for Machining Nickel Super Alloy with Different Tools

This paper presents a study of the development of surface roughness models for turning supermet 718 nickel super alloy (300 BHN), using different tool materials namely; CBN (SANDVIK CB50), Carbide (SANDVIK HIP k10), and ceramic (SANDVIK CC680) under dry cutting conditions and a constant nose radius. The models are developed in terms of cutting speed, feed rate, and depth of cut. These variables were investigated using design of experiments and utilization of the response surface methodology (RSM). A separate surface roughness model corresponding to each tool material is established, tested and reported.     

Mathematical models to predict surface finish in fine turning of steel. Part II

This paper outlines further experimental development of mathematical models for predicting RMS surface finish in fine turning operation using TiC coated and cemented tungsten carbide throwaway cutting tools. The five independent variables included are: cutting speed, feed, depth of cut, time of cut of tool, nose radius. Using these five variables at different levels an experimental approach, predictive models for tungsten carbide and titanium coated tungsten carbide tools were developed. A sixth variable, 'the type of cutting tool,' was used to develop a single model for both the TiC coated and cemented carbide cutting tools. AIS1 4140 steel was used as workpiece specimen in the experimental work. Stepwise regression analysis was used in developing the models.     

Investigation of the influential parameters of machining of AISI 304 stainless steel

Austenitic stainless steels are hard materials to machine, due to their high strength, high ductility and low thermal conductivity. The last characteristic results in heat concentration at the tool cutting edge. This paper aims to optimize turning parameters of AISI 304 stainless steel. Turning tests have been performed in three different feed rates (0.2, 0.3, 0.4 mm/rev) at the cutting speeds of 100, 125, 150, 175 and 200 m/min with and without cutting fluid. A design of experiments (DOE) and an analysis of variance (ANOVA) have been made to determine the effects of each parameter on the tool wear and the surface roughness. It is being inferred that cutting speed has the main influence on the flank wear and as it increases to 175 m/min, the flank wear decreases. The feed rate has the most important influence on the surface roughness and as it decreases, the surface roughness also decreases. Also, the application of cutting fluid results in longer tool life and better surface finish.     

Comparison of tool life and surface characteristics of uncoated,coated carbide and ceramic tools during machining of SiC reinforced ZA43 alloy MMC

Abstract

In the present investigation, machinability issues of zinc–aluminium (ZA43) alloy reinforced with silicon carbide particles (SiC) were evaluated. The fabrication of composite was done through liquid metallurgy technique. Metal matrix composite (MMC) was subjected to turning using conventional lathe with three grades of cutting tools, namely, uncoated carbide tool, coated carbide tool and ceramic tool. Surface roughness and tool wear were measured during the machining process. Results reveal that roughness increases with increase in the reinforcement concentration and particle size. Feed has direct influence on roughness, i.e. surface deteriorates with higher feeds. Depth of cut has very minimum effect on the surface roughness, while inverse effect of cutting speed on the roughness was observed (i.e. increase in the cutting speed leads to better finish on the specimen). Tool wear was studied during the investigation, and it was noticed that MMC with higher reinforcement concentration and particle size cause severe wear on the flank of the cutting tool. Increase in the cutting speed, feed and depth of cut also increases the flank wear on the tool. Out of all the three grades of tools, coated carbide tool outperformed uncoated carbide and ceramic tools.     

The Impact of Tool Material and Cutting Parameters on Surface Roughness of a Nickel-Base Superalloy

The objective of the present work, is to assess the effect of tool material and cutting parameters on surface roughness of the supermet 718 Nickel-base superalloy, under dry cutting conditions and a constant nose radius (0.5 mm). The parameters investigated are cutting speed, feed rate, depth of cut and tool material. The tool materials used were the ceramic (Sandvik CC 680) and the CBN (Sandvik CB 50) inserts. These variables were investigated using a 2^k factorial design.

The present work demonstrates a favorable effect for ceramic inserts on surface roughness, when compared with CBN inserts. The work also, showed that the feed rate has the dominant effect among the parameters studied on the surface roughness, irrespective of the tool material used.     

CUTTING FLUID LUBRICITY AND SURFACE ROUGHNESS IN TURNING

This paper describes research performed to determine the effect of cutting fluid on first cut surface finish in single point turning. A measure of cutting fluid lubricity was used as an independent variable as well as cutting speed, tool feed, depth of cut and replications. The dependent variable was surface roughness. Though cutting medium was not a significant main factor, it was highly significant in several interactions with the other variables.     

Investigating the Tool Life, Cutting Force Components, and Surface Roughness of AISI 302 Stainless Steel Material Under Oblique Machining

The aim of this work is to investigate the machinability of austenitic AISI 302 stainless steel under oblique cutting. This can be achieved by studying the cutting forces, analysis of tool life, and investigation of the surface roughness at different cutting conditions and nose radius. A factorial experiment and analysis of variance technique are used in which several factors are evaluated for their effects on each level. The machinability experiments are based on design of experiments to obtain empirical equations for machinability values for machining conditions such as speed, feed, depth of cut, and nose radius. The parameters considered in the experiments were optimized to attain maximum tool life using a response graph and a response table. Based on the response models, dual response contours (tool life and surface roughness as a response and metal removal rate) have been plotted in cutting speed-feed planes. Evaluating the effect of the predominant variables influencing the value of tool life is very important for improving the machined product quality.     

Effect of machining parameters on surface roughness and material removal rate in finish turning of ±30° glass fibre reinforced polymer pipes

This paper studies the effect of varying machining parameters in turning on surface roughness and material removal rate (m.r.r.) for ±30° filament wound glass fibre reinforced polymers (GFRP) in turning operations using coated tungsten carbide inserts under dry cutting conditions. The paper describes the development of an empirical model for turning GFRP utilising factorial experiments. Second order predictive model covering speed, feed, depth of cut and tool nose radius has been developed at 95% confidence interval for surface roughness and material removal rate. Contour plots of the surface roughness and m.r.r. for different machining conditions have been generated from the empirical equations. Overlaid contour graph help in obtaining iso-value of roughness for different values of m.r.r.     

Nose radius and cutting speed effects during milling of AISI 304 material

Not only milling parameters, but also cutting tool properties affect the machining performance. Therefore, in the current work, the effect of nose radius and cutting speed on the wear, force, surface roughness and chip morphology in down and up milling of AISI 304 stainless steel was investigated. Machining experiments were conducted with cutting tools with radii of 0.4, 0.8 and 1.2?mm at various cutting speeds in both down and up milling. Experimental results showed that the main tool failure mechanisms and modes were adhesion, abrasion, chipping and fracture during milling with various nose radii. Cutting forces dropped with the increment in nose radius regardless of the cutting speed and milling direction, except for up milling at 100?m/min. From the experimental results, it was found that roughness diminished with increase in both nose radius and speed. Surface roughness and the resultant forces during up milling were found to be lower than that during down milling. It was observed that the increment in nose radius increased the edge serration in chip morphology.     

Optimization for Turning of Al-6061-SiC-Gr Hybrid Nanocomposites Using Response Surface Methodologies

Metal matrix composites have cemented their applicability in industrial sector by virtue of their excellent mechanical properties. However, work has largely been done on the studies related to macro/microsize particles. This work has been aimed to evaluate the influence of input parameters in turning of Al-6061-SiC-Gr hybrid nanocomposites. This article evaluates the effect of process parameters on the cutting force and average roughness of the machined surface in turning of Al-6061-SiC-Gr nanocomposites. The experiments were designed using CCD, and cutting force and roughness were evaluated using response surface methodology. Statistical models were generated. The results of the study indicated that feed rate and depth of cut are the major influencing factors in descending order for the cutting force. The analysis of surface roughness revealed that both these factors are having identical effect. The cutting speed had little effect on cutting force and an improvement is seen in surface finish. The experiments also revealed that tool wear is negligible for nanocomposites. The software-predicted values and the experimentally obtained values of the responses were acceptably close to each other with an error percentage of less than 5%. Using response surface optimization, optimal combinations of machining parameters are also obtained.     

Surface roughness predictive modeling: neural networks versus regression

Surface roughness plays an important role in product quality and manufacturing process planning. This research focuses on developing an empirical model for surface roughness prediction in finish turning. The model considers the following working parameters: work-piece hardness (material), feed, cutter nose radius, spindle speed and depth of cut. Two competing data mining techniques, nonlinear regression analysis and computational neural networks, are applied in developing the empirical models. The values of surface roughness predicted by these models are then compared with those from some of the representative models in the literature. Metal cutting experiments and tests of hypothesis demonstrate that the models developed in this research have a satisfactory goodness of fit. It has also presented a rigorous procedure for model validation and model comparison. In addition, some future research directions are outlined.     

High-feed turning of AISI D2 tool steel using multi-radii tool inserts: Tool life,material removed,and workpiece surface integrity evaluation

Multi-radii tool inserts offer novel configuration that comprises of multiple radii at tool nose. A review of the available literature indicates that there exists a need for experimental investigation on certain key machining characteristics of such tools. This paper reports on tool wear/life, material removed, and workpiece surface roughness when multi-radii mixed alumina TiN coated tool inserts are employed for turning D2 steel. Inserts of three different nose radii (0.40, 0.80, 1.20?mm) at six levels of feed rates (ranging from 0.157 to 0.562?mm/rev) are used. Results show that flank wear is the dominant wear mode with catastrophic tool failure occurring at highest nose radius (1.20?mm) and feed rate (0.562?mm/rev) combination. Also, there is ～59% reduction in tool life accompanied by ～62% increase in quantity of material removed as the feed rate increases from 0.157 to 0.562?mm/rev at maximum nose radius (1.20?mm). Feed rate is found to be statistically significant factor for all three responses considered herein at 95% confidence level. Surface integrity assessment at maximum feed rate reveals presence of a strain hardened layer extending to the depth of 150?µm below the machined surface without any observance of white layer for all the tool conditions and nose radius.     

Anisotropy of surface roughness in diamond turning of brittle single crystals

This paper deals with an investigation of the effect of crystallographic orientation and process parameters on the surface roughness of brittle silicon single crystals in ultraprecision diamond turning. The process parameters involve the depth of cut, feed rate, and spindle speed. Experimental results indicate that anisotropy in surface finish occurs when the cutting direction relative to the crystal orientation varies. There exists a periodic variation of surface roughness per workpiece revolution, which is closely related to the crystallographic orientation of the crystals being cut. Such an anisotropy of surface roughness can be minimized with an appropriate selection of the feed rate, spindle speed, and depth of cut. The findings provide a means for the optimization of the surface quality in diamond turning of brittle silicon single crystals.     

A surface roughness model for a turning operation

A mathematical model for the surface roughness in a turning operation was developed in terms of the cutting speed, feed and depth of cut. Utilizing PL1 language and an IBM 360/50 computer, the model was used to generate contours of surface roughness in planes containing the cutting speed and feed at different levels of depth of cut. The surface roughness contours were used to select the machining conditions at which an increase in the rate of metal removal was achieved without sacrifice in surface finish.     

Experimental investigation of cutting parameters influence on surface roughness and cutting forces in hard turning of X38CrMoV5-1 with CBN tool

This experimental investigation was conducted to determine the effects of cutting conditions on surface roughness and cutting forces in hard turning of X38CrMoV5-1. This steel was hardened at 50 HRC and machined with CBN tool. This is employed for the manufacture of helicopter rotor blades and forging dies. Combined effects of three cutting parameters, namely cutting speed, feed rate and depth of cut, on the six performance outputs-surface roughness parameters and cutting force components, are explored by analysis of variance (ANOVA). Optimal cutting conditions for each performance level are established. The relationship between the variables and the technological parameters is determined through the response surface methodology (RSM), using a quadratic regression model. Results show how much surface roughness is mainly influenced by feed rate and cutting speed. The depth of cut exhibits maximum influence on cutting force components as compared to the feed rate and cutting speed.