Experimental study of surface roughness in slot end milling AL2014-T6

The aim of this work was to analyze the influence of cutting condition and tool geometry on surface roughness when slot end milling AL2014-T6. The parameters considered were the cutting speed, feed, depth of cut, concavity and axial relief angles of the end cutting edge of the end mill. Surface roughness models for both dry cutting and coolant conditions were built using the response surface methodology (RSM) and the experimental results. The results showed that the dry-cut roughness was reduced by applying cutting fluid. The significant factors affecting the dry-cut model were the cutting speed, feed, concavity and axial relief angles; while for the coolant model, they were the feed and concavity angle. Surface roughness generally increases with the increase of feed, concavity and axial relief angles, while concavity angle is more than 2.5°.     

Optimization of feedrate in a face milling operation using a surface roughness model

Optimization of feedrate is valuable in terms of providing high precision and efficient machining. The surface roughness is particularly sensitive to the feedrate and the runout errors of the inserts in a face-milling operation. This paper analyzes the effects of the insert runout errors and the variation of the feedrate on the surface roughness and the dimensional accuracy in a face-milling operation using a surface roughness model. The validity of the developed model was proved through cutting experiments, and the model was used to predict the machined surface roughness from the information of the insert runouts and the cutting parameters. From the estimated surface roughness value, the optimal feedrate that gave a maximum material removal rate under the given surface roughness constraint could be selected by a bisection method.     

In-process prediction of cutting depths in end milling

In geometric adaptive control systems for the end milling process, the surface error is usually predicted from the cutting force owing to the close relationship between them, and the easiness of its measurement. Knowledge of the cutting depth improves the effectiveness of this approach, since different cutting depths result in different surface errors even if the measured cutting forces are the same. This work suggests an algorithm for estimating the cutting depth based on the pattern of cutting force. The cutting force pattern, rather than its magnitude, better reflects the change of the cutting depth, because while the magnitude is influenced by several cutting parameters, the pattern is affected mainly by the cutting depth. The proposed algorithm can be applied to extensive cutting circumstances, such as presence of tool wear, change of work material hardness, etc.     

An in-process surface recognition system based on neural networks in end milling cutting operations

An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness R_a values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness R_a values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.     

高速铣削表面粗糙度的研究

通过在HSM－700型高速铣床上的正交铣削试验，联系平时实际的生产加工情况，分析高速铣削的切削加工参数对零件表面粗糙度的影响。通过分析不同铣削参数下的零件表面粗糙度和切屑变形，为高速加工切削参数的选择和表面质量的控制提供依据。     

Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness

This paper deals with a numerical and experimental study of the dynamics of flank milling operations at low cutting rates. It focuses on both properties of the cutting vibratory phenomena and their impacts on the roughness of the machined surface. The study is based on a one degree of freedom model of the mechanical machining system. The system is of the rigid cutter–flexible workpiece type. The cutting force model is based on the regenerative mechanism. The roughness of the surface machined at high speed revolutions has been studied for both forced vibrations occurring during stable cutting and self-excited vibrations occurring during unstable cutting. It is shown that forced vibrations have only a very slight impact (roughness remains quite similar to that obtained with a fully rigid mechanical system), while unstable cutting mainly impacts roughness. The stable milling zones can be shown on a roughness map. The study of the roughness shows that the boundary between stable and unstable cutting conditions, in the case of interrupted cutting, is a wide zone characterised by a doubling of the tooth passing period. In this zone, only one tooth over two is removing material due to the vibratory motion. A discussion explains the phenomenon.     

An improved cutting force and surface topography prediction model in end milling

During the milling operation, the cutting forces will induce vibration on the cutting tool, the workpiece, and the fixtures, which will affect the surface integrity of the final part and consequently the product's quality. In this paper, a generic and improved model is introduced to simultaneously predict the conventional cutting forces along with 3D surface topography during side milling operation. The model incorporates the effects of tool runout, tool deflection, system dynamics, flank face wear, and the tool tilting on the surface roughness. An improved technique to calculate the instantaneous chip thickness is also presented. The model predictions on cutting forces and surface roughness and topography agreed well with experimental results.     

The effect of tool vibrations on the flank surface created by peripheral milling

Tool vibrations have a significant influence on the surface quality with respect to surface location error and roughness. Even chatter-free milling processes can produce a high surface location error since chatter-free does not necessarily mean vibration-free. This article describes a geometric model for predicting the surface formation resulting from peripheral milling processes when tool vibrations are present. This model enables one to predict and minimize the roughness and location error of the flank surface. Comparisons between simulations and experiments show the effectiveness of this modeling approach. An important result of this research is that it has shown that milling at a stability maximum does not generally yield the best surface quality.     

Predictive modeling of surface roughness in grinding

The surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall grinding system. This paper presents the prediction of the arithmetic mean surface roughness based on a probabilistic undeformed chip thickness model. The model expresses the ground finish as a function of the wheel microstructure, the process kinematic conditions, and the material properties. The analysis includes a geometrical analysis of the grooves left on the surface by ideal conic grains. The material properties and the wheel microstructure are considered in the surface roughness prediction through the chip thickness model. A simple expression that relates the surface roughness with the chip thickness was found, which was verified using experimental data from cylindrical grinding.     

晶体材料车削表面粗糙度的实验研究

本文探讨了晶体材料的切削加工性,重点分析了切削条件对表面粗糙度的影响。     

A vision system for surface roughness characterization using the gray level co-occurrence matrix

Computer vision technology has maintained tremendous vitality in many fields. Several investigations have been performed to inspect surface roughness based on computer vision technology. This work presents a new approach for surface roughness characterization using computer vision and image processing techniques. A vision system has been introduced to capture images for surfaces to be characterized and a software has been developed to analyze the captured images based on the gray level co-occurrence matrix (GLCM).Three standard specimens and 10 machined samples with different roughness values have been characterized by the presented approach. Three-dimensional plots of the GLCMs for various captured images have been introduced, compared and discussed. In addition, some statistical parameters (maximum occurrence of the matrix, maximum occurrence position and standard deviation of the matrix) have been calculated from the GLCMs and compared with the arithmetic average roughness R_a. Furthermore, a new parameter called maximum width of the matrix is introduced to be used as an indicator for surface roughness.     

Surface roughness prediction of flow-formed AA6061 alloy by design of experiments

Design of experiments has been used to study the effects of the main flow-forming parameters such as the speed of the mandrel, the longitudinal feed, and the amount of coolant used on the surface roughness of flow-formed AA6061 tube. A mathematical prediction model of the surface roughness has been developed in terms of the above parameters. The effect of these parameters on the surface roughness has been investigated using response surface methodology (RSM). Response surface contours were constructed for determining the optimum forming conditions for a required surface roughness. The developed prediction equation shows that the longitudinal feed rate is the most important factor that influences the surface roughness. The surface roughness was found to increase with increase in the longitudinal feed and it decreased with decrease in the amount of the coolant used. The verification experiment carried out to check the validity of the developed model predicted surface roughness within 6% error.     

Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

Quantification of surface roughness of parts processed by laminated object manufacturing

Additive manufacturing (AM) technology is essentially performed using a layered manufacturing (LM) process. Because more complex 3D physical models can be efficiently fabricated without geometric limitation by the technology, a remarkable reduction in production life cycle has been achieved. However, due to the LM process, a deterioration of the surface quality of the parts processed by AM may occasionally occur, which is the primary reason that the surface problem has been a key issue in AM. In this paper, a methodology is proposed to quantify the surface roughness of the parts processed by laminated object manufacturing (LOM), which is a typical technology in AM. The surface profiles of the parts were investigated, a schematic was constructed by considering the LOM process factor geometrically, and a theoretical approach to quantify average surface roughness according to surface angle variation is presented. The expressions required for numerical computation were deduced and defined. By comparing the measured data and computed values, the proposed approach was verified. Additionally, the effects of the process variables related to surface quality were evaluated and analyzed.     

Experimental investigation of effects of cutting parameters on surface roughness in the WEDM process

The experimental study presented in this paper aims to select the most suitable cutting and offset parameter combination for the wire electrical discharge machining process in order to get the desired surface roughness value for the machined workpieces. A series of experiments have been performed on 1040 steel material of thicknesses 30, 60 and 80 mm, and on 2379 and 2738 steel materials of thicknesses 30 and 60 mm. The test specimens have been cut by using different cutting and offset parameter combinations of the “Sodick Mark XI A500 EDW” wire electrical discharge machine in the Middle East Technical University CAD/CAM/Robotics Center. The surface roughness of the testpieces has been measured by using a surface roughness measuring device. The related tables and charts have been prepared for 1040, 2379, 2738 steel materials. The tables and charts can be practically used for WEDM parameter selection for the desired workpiece surface roughness.     

A study of back cutting surface finish from tool errors and machine tool deviations during face milling

The surface finish of mechanical components produced by face milling is given by factors such as cutting conditions, workpiece material, cutting geometry, tool errors and machine tool deviations. The contribution of the different tool teeth to imperfections in the machined surface is strongly influenced by tool errors such as radial and axial runouts. The surface profile of milled parts is not only affected by chip removal due to front cutting, but also by back cutting, which must be taken into account when predicting surface roughness. In the present work, the influence of back cutting on the surface finish obtained by face milling operations is studied. Final part surface roughness is modelled from tool runouts and height deviations that affect the surface marks provoked by back cutting. Round insert cutting tools and surface positions defined by cutter axis trajectory are considered, and milling experiments are developed for a spindle speed of 750 rpm, depth of cut of 0.5 mm and feeds from 0.4 to 1.0 mm/rev. Experimental observations are compared with the theoretical predictions provided by the surface roughness model, and good agreement is found between both results. Surface imperfections caused by front and back cutting are analysed, and discrepancies between experiments and numerical predictions are explained by undeformed chip thickness variations along the tool tooth cutting edge, the tearing of the workpiece material, and fluctuations in the feedrate and height deviation during machine tool axis displacement.     

Optimization of squeeze cast parameters of LM6 aluminium alloy for surface roughness using Taguchi method

The ability to produce near net shape components with good surface finish is made possible by means of squeeze casting, a hybrid metal forming process combining features of both casting and forging in a single operation. The primary objective of this paper is to analyze the influence of the process parameters on surface roughness in squeeze casting of LM6 aluminium alloy using Taguchi method. In Taguchi method, a three level orthogonal array has been used to determine the S/N ratio. Analysis of variance and the ‘F’-test values are used to determine the most significant process parameters affecting the surface roughness. The results indicated that the squeeze pressure and the die preheating temperature are the recognized parameters to cause appreciable improvement in the surface finish of the squeeze cast components.     

主轴转速对单点渐进成形工件表面粗糙度的影响

表面粗糙度是单点渐进成形技术的主要指标之一。在单点渐进成形机上对2A12铝合金薄板进行单点渐进成形实验,研究了主轴转速对成形工件表面质量的影响。实验表明:随着主轴转速的增大,粗糙度总体趋势是逐步减小,速度达到一定值时,粗糙度趋于平稳;转速低时,表面有类似鱼鳞纹的刀路,随着转速的变大,鱼鳞纹越来越小,直至消失,表面也越来越光亮。     

Finite element modeling of the surface roughness of 5052 Al alloy subjected to a surface severe plastic deformation process

The surface of 5052 Al alloy plates is severely plastically deformed via multiple impacts by high-velocity tungsten carbide/cobalt (WC/Co) balls in a surface nanocrystallization and hardening (SNH) process. The surface roughness of 5052 Al alloy plates as a function of the impacting ball size and processing time has been evaluated via non-contact 3D profilometry. A three-dimensional finite element (FE) model has been developed to simulate the formation of peaks and valleys during the SNH process. The peak-to-valley distance predicted from the FEM matches the maximum PV value measured experimentally quite well, indicating that surface roughening of 5052 Al alloy plates during the SNH process using WC/Co balls is mainly dictated by the indentation process of the impacting balls. The implications of this surface roughening mechanism in the final surface roughness, processing time, related microstructure change, and property alteration are discussed.