金刚石砂轮磨削铁氧体的表面粗糙度与形貌分析

本文研究了树脂结合剂金刚石砂轮磨削铁氧体材料时,磨削深度、工件进给速度对磨削表面粗糙度和材料去除方式的影响规律,以此探索提高铁氧体磨削表面质量的有效途径。采用单因素法设计试验方案对铁氧体进行磨削,测量表面粗糙度数据并对其进行方差分析,对铁氧体磨削表面形貌进行观察。结果表明：随着磨削深度、工件进给速度的增加,表面粗糙度值升高,同时表面塑性痕迹减少,脆性断裂痕迹增加,且磨削深度对表面粗糙度的影响要比工件进给速度的更显著,因此,制定磨削工艺时,考虑到粗磨为了提高效率,降低表面损伤,优化得到磨削工艺为磨削深度5μm,工件进给速度10 m/min;精磨为了获得较低的表面粗糙度,采用磨削深度5μm、工件进给速度为5 m/min,可以提高磨削表面延展性。     

Evaluation of surface roughness by vision system

Conventional measurement of finish using stylus instruments has been in vogue for over half a century. Though these instruments have excellent capabilities and wide range, they are normally used for inspecting machined surfaces of good finish. Further the procedure is a post process approach which is not amenable for automation. In the recent past, higher levels of automation in the shop floor has focused the attention on the application of fast, reliable and cost effective procedures for evaluating surfaces of medium finished parts.This paper deals with the possibility of doing this using a vision system. It briefly reviews a few approaches and examines one — texture unit spectra — in more detail.     

Developing of an expert system for nonferrous alloy design

Expert systems have been used widely in the predictions and design of alloy systems. But the expert systems are based on the macroscopic models that have no physical meanings. Microscopic molecular dynamics is also a standard computational technique used in materials science. An approach is presented to the design system of nonferrous alloy that integrates the molecular dynamical simulation together with an expert system. The knowledge base in the expert system is able to predict nonferrous alloy properties by using machine learning technology. The architecture of the system is presented.     

Numerical modelling of shot peening process and corresponding products: Residual stress, surface roughness and cold work prediction

An investigation of the effects of controlled shot peening (CSP) process parameters on the treated material is presented. For this purpose, a three dimensional numerical model is developed, comprising the target plate and a number of shot impacts; their number is defined as the minimum required for a realistic simulation and minimum computational cost. The numerical model is verified by comparing the predicted residual stress (RS) fields to experimental. A parametric study of the shot velocity and impinging angle on the CSP products is performed for 4 shot types, i.e. S110, S230, S330 and S550. The main advantages of the present numerical model are: a) the relatively high number of shots introduced in the simulation compared to other publications that use only one shot, b) the number of shots dependency on the desired coverage, c) the thorough selection of numerical parameters, d) the high-strain rate material behaviour used for the target plate, e) the capability to calculate CSP effects on the target plate as function of coverage, f) the computed data which include RS field, surface roughness, cold work and geometrical stress concentration factor (Kt) and finally g) the computed results which are validated by experimental measurements.     

Electropolishing of 304 stainless steel: Interactive effects of glycerol content, bath temperature, and current density on surface roughness and morphology

In this study, surface roughness of 304 stainless steel (304SS) varying from ca. 7 to 45 nm can be controlled by changing electropolishing variables in glycerol-containing baths. On the basis of the adsorbate-acceptor mechanism and the model for preferential adsorption of shielding molecules, incorporating the molecular interaction concept is helpful to explain the interactive effects of glycerol content, bath temperature, and current density on the surface roughness (Ra) and morphologies of 304SS. In order to easily understand how to control the surface roughness of 304SS in the glycerol-containing electrolyte, a simplified scheme integrating the above two mechanisms was proposed. From the atomic force microscopy (AFM) analyses, a rough surface is easily obtained by electropolishing at high bath temperatures, which endows the 304SS substrate with pores.     

A novel contact/non-contact hybrid measurement system for surface topography characterization

A novel surface profile measurement instrument, developed for the characterization of engineering surfaces, is presented. The instrument is of a hybrid type and is capable of contact and non-contact measurement, making it suitable for a wider range of applications. It has an optical displacement sensor and a stylus displacement sensor. For contact measurement, the vertical measurement range and resolution of the instrument are 1 mm and 10 nm, respectively. For non-contact measurement, they are 500 μm and 3 nm, respectively. The instrument has been successfully used for several forensic applications, demonstrating its unique flexibility and high reliability as a novel surface topography instrument.     

Surface roughness analysis,modelling and prediction in selective laser melting

Selective Laser Melting (SLM) is an increasingly employed additive manufacturing process for the production of medical, aerospace, and automotive parts. Despite progresses in material flexibility and mechanical performances, relatively poor surface finish still presents a major limitation in the SLM process.In this study an investigation of surface roughness and morphology is presented for Steel 316L alloy parts made by SLM. In order to characterise the actual surfaces at different sloping angles, truncheon samples have been produced and an analysis has been conducted at different scales, by surface profilometer and scanning electron microscope (SEM). The surface analysis has showed an increasing density of spare particles positioned along the step edges, as the surface sloping angle increases. When layer thickness is comparable to particle diameter, the particles stuck along step edges can fill the gaps between consecutive layers, thus affecting the actual surface roughness.Classic models for roughness prediction, based on purely geometrical consideration of the stair step profile, fail to describe the observed trend of the experimental data. A new mathematical model is developed to include the presence of particles on top surfaces, in addition to the stair step effect, for the accurate prediction of surface roughness. Results show that surface roughness predicted by this model has a good agreement with the experimentally observed roughness. The paper investigates the key contributing factors influencing surface morphology, and a theoretical model for roughness prediction that provides valuable information to improve the surface quality of SLM parts, thus minimising the need of surface finishing.     

Autofix—an expert CAD system for jigs and fixtures

This paper describes AutoFix, a fully automated CAD package for configuring complex fixtures from a database of molecular elements. The program is also capable of designing special elements if standard ones do not exist in the database. A feature of AutoFix is the use of Finite Element Analysis to compute the deflection of the fixtured workpiece and determine the optimum position of supports. Examples of fixtures designed by AutoFix are presented.     

Development of an engineering－practical expert system for arc welding and its shell

Ｄｅｖｅｌｏｐｍｅｎｔｏｆａｎｅｎｇｉｎｅｅｒｉｎｇ－ｐｒａｃｔｉｃａｌｅｘｐｅｒｔｓｙｓｔｅｍｆｏｒａｒｃｗｅｌｄｉｎｇａｎｄｉｔｓｓｈｅｌｌＰｅｎｇＪｉｎｎｉｎｇ；ＣｈｅｎＢｉｎｇｓｅｎａｎｄＬｉｕＱｉａｎｇ（ＱｉｎｇｈｕａＵｎｉｖｅｒｓｉｔｙ，...     

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.     

Numerical modelling of ground surface topography: effect of traverse and helical superabrasive grinding with touch dressing

Grinding processes are often used for final finishing of components because of their ability to satisfy stringent requirements of surface roughness and dimensional tolerance. Surface topography generated during grinding depends upon many parameters like wheel parameters, wheel velocity, downfeed, grit density etc. and it also depends upon the type of grinding procedures (viz. plunge grinding, traverse grinding, helical grinding, touch dressing etc.) employed. Therefore, a correct examination of the parameters and type of process employed to carry out grinding are necessary. This paper is an attempt to develop the relation between the different grinding parameters and the grinding procedures like plunge, traverse and helical superabrasive grinding with touch dressing and the average surface roughness. For this purpose, a numerical simulation technique has been implemented to generate the grinding wheel topography. The ground workpiece surface has also been generated by simulating removal of work material depending upon the trajectory of the abrasive grits on the grinding wheel without taking rubbing and ploughing into consideration.     

Application of neural networks to an expert system for cold forging

The technique of neural networks is applied to an expert system for cold forging in order to increase the consultation speed and to provide more reliable results. A three-layer neural network is used and the back-propagation algorithm is employed to train the network.

By utilizing the ability of pattern recognition of neural networks, a system is constructed to relate the shapes of rotationally symmetric products to their forming methods. The cross-sectional shapes of the products which can be formed by one blow are transformed into 16 × 16 black and white points and are given to the input layer. After learning about 23 products, the system is able to determine the forming methods for the products which are exactly the same or slightly different from the products used in the network training. To exploit the self-learning ability, the neural networks are applied to the prediction of the most probable number of forming steps, from information about the complexity of the product shape and the materials of the die and billet, and also to the generation of rules from the knowledge acquired from an FEM simulation. It is found that the prediction of the most probable number of forming steps can be made successfully and that the FEM results are represented better by the neural networks than by the statistical methods.     

The importance of surface roughness for implant incorporation

318 threaded implants with a diameter of 3.7 mm, length 6 to 7 mm and a pitch-height of 0.6 mm were inserted in 56 New Zealand white rabbits. Implants of varying surface roughness were produced by a blasting procedure using 25m 75m and 250 μm sized particles. As- machined, (i.e turned) implants served as controls. The implant surface roughness was measured with the confocal laser scanner (TopScan 3D), and appropriate software was used for visual and numerical characterization. After implantation time of 4 weeks, 12 weeks or 1 year, the animals were euthanized. The implants were evaluated with respect to the peak removal torque, the percentage of bone-to-implant contact.The results from the animal experiments demonstrated, generally, higher removal torques and higher percentages of bone-to-implant contact for implants blasted with 25 and 75 μm sized blasting particles than with as-turned or 250 μm blasted implants. The corresponding average height deviation for the 4 surfaces was 1 μm 1.5 μm 0.7 μm and 2.0 μm When comparing the 25 and the 75 μm blasted implants it was found that 75 μm blasted screws showed the strongest bone fixation.     

Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system

Modeling and prediction of surface roughness of a workpiece by computer vision in turning operations play an important role in the manufacturing industry. This paper proposes a method using an adaptive neuro-fuzzy inference system (ANFIS) to accurately establish the relationship between the features of surface image and the actual surface roughness, and consequently can effectively predict surface roughness using cutting parameters (cutting speed, feed rate, and depth of cut) and gray level of the surface image. Experimental results show that the proposed ANFIS-based method outperforms the existing polynomial network-based method in terms of modeling and prediction accuracy.     

Electrical discharge machining using simple and powder-mixed dielectric: The effect of the electrode area in the surface roughness and topography

Electrical discharge machining (EDM) is one of the most widely disseminated manufacturing technologies, in particular as regards the generation of accurate and complex geometrical shapes on hard metallic components. Nevertheless current EDM technologies have major limitations when dealing with fine surface finish over large process area. Indeed this is one reason that explains the need of final manual polishing of mould cavities performed by EDM. Recently EDM with powder-mixed dielectric (PMD-EDM) has been a focus of an intense research work in order to overcome these technological performance barriers. This paper presents a research work within the objective to acquire deep knowledge on EDM technology with powder mixed dielectric and to compare its performance to the conventional EDM when dealing with the generation of high-quality surfaces. In particular the analysis of the effect of the electrode area in the surface quality measured by the surface roughness and craters morphology was carried out for both technologies. The results achieved evidenced a linear relationship between the electrode area and the surface quality measures as well as a significant performance improvement when the powder mixed dielectric is used.     

Modeling and predicting of surface roughness for generating grinding gear

Gear surface has a significant effect on the load-carrying capacity and plays a crucial role for wear, friction and lubrication properties of the contact. However, most mathematical model of a generating gear grinding process trends to focus on grinding forces and temperature distribution in gear tooth. A new method for predicting gear surface roughness with the generating grinding process is developed in this paper. An algorithm for geometrical analysis of the grooves on the gear surface left by idea conic grains is given. To determine the final gear surface roughness produced by thousands of grinding wheel grains with randomly distributed protrusion heights, a search technique is proposed to systematically solve the gear surface roughness, which starts from the addendum circle along the involutes direction and ends at the dedendum circle. The numerical calculated results show that the surface of gear root part is smoother than that of gear top part.     

A study of surface topography, friction and lubricants in metalforming

In this paper are presented the results of investigations concerning the relation between friction behaviour and surface topography using various lubricants and initial workpiece surface conditions in ring upsetting and rod extrusion processes. The tests were carried out using either a liquid lubricant or under clean dry conditions. Two types of workpiece surfaces, random and directional, were prepared by either shotblasting, or EDM or turning to different levels of surface finish. Not only has the friction effect of lubricant and initial surface been studied, but also the surface topography of the finished products has been examined in detail, to obtain a better understanding of the mechanism of lubrication and surface interaction. It was found that, for random surfaces, smoother ones could retain more lubricant and decrease friction resistance. The experimentation also demonstrated that turned surfaces were effective in reducing friction, but the final surface finish of the workpiece was not as good as that from random surfaces.     

Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning

Surface roughness is one of the important factors in all areas of tribology and in evaluating the quality of a machining operation. In order to achieve computer controlled machining in a flexible manufacturing system, a technique is needed for the on-line, real-time monitoring of each machining process parameter that affects surface roughness. As the first step in developing such an algorithm, research on the correlation between surface roughness and cutting vibration is reported in this paper. The algorithm utilizes the relative cutting vibrations between tool and workpiece, which are measured through an inductance pickup and filtered by an analog bandpass filter for a specific vibrational component. The cutting vibration signals of a specific :frequency are superimposed onto the kinematic roughness, which is calculated by the tool edge radius and feed rate. Experimental results show good correlation between the simulated roughness obtained using the proposed algorithm and the roughness actually measured with a surface profilometer