The machinability and tribological characteristics of aluminum alloys with improved elevated temperature properties using rapidly solidified powder

In this investigation the tribological characteristics of rapidly solidified Al–8Fe–4Ce with improved elevated temperature properties were studied. Such characteristics were compared with cast aluminum–silicon alloy and cast zinc–aluminum alloy. These materials included Al–13Si, Zn–35Al, Zn–35Al–Si, Zn–35Al–3.75Si and Zn–35Al–5.8Si. The wear rates of all materials were tested on a crossed-cylinders wear machine against 440C stainless steel counterface lapped by random abrasion using diamond paste to the desired average surface roughness. The effects of sliding distance on both the worn volume and the coefficient of friction were examined. The aluminum–iron–cerium alloy (Al–8Fe–4Ce) showed the lowest wear rate. The experiments were then extended on this material to examine the effect of varying the applied load and sliding speed on its wear rate. It was found that increasing the applied load increased the wear rate while it was slightly sensitive to the change in sliding speed. As the wear results showed that the Al–8Fe–4Ce alloy has the lowest wear rates, its machinability during turning operation was studied. Statistically-based experimental design (response surface methodology) using central composite second-order rotatable design technique was used to improve the experimentation design without loss of accuracy of the results. The interaction of cutting parameters (cutting speed, feed rate and depth of cut) was examined and their effect on the average surface roughness was reported. It was found that employing a combination of high cutting speed and small depth of cut with small feed rate causes a significant reduction in R_a. The data were represented in three-dimensional and contour graphs for selecting the appropriate machining conditions required to achieve desired values of surface roughness.     

Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

A manufacturing system is oriented towards higher production rate, quality, and reduced cost and time to make a product. Surface roughness is an index for determining the quality of machined products and is influenced by the cutting parameters. Surface roughness prediction in machining is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of two different hybrid intelligent techniques, adaptive neuro fuzzy inference system (ANFIS) and radial basis function neural network- fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. An experimental data set is obtained with speed, feed, depth of cut and vibration as input parameters and surface roughness as output parameter. The input-output data set is used for training and validation of the proposed techniques. After validation they are forwarded for the prediction of surface roughness. Both the hybrid techniques are found to be superior over their respective individual intelligent techniques in terms of computational speed and accuracy for the prediction of surface roughness.     

Material removal in abrasive waterjet machining of metals Surface integrity and texture

An experimental study was conducted to determine the influence of material properties on the surface integrity and texture that results from abrasive waterjet (AWJ) machining of metals. A microstructure analysis, microhardness measurements, and profilometry were used in determining the depth of plastic deformation and surface texture that result from material removal. Models now available for dry abrasive erosion were adopted and found useful in understanding the influence of material properties on the hydrodynamic erosion process. It was found that the depth of subsurface plastic deformation is inversely proportional to a metals strength coefficient and extends the greatest depth near jet entry in the initial damage region (IDR). Furthermore, surface skewness in AWJ machining of metals increases with ductility and the corresponding critical strain for lip formation.     

Fiber optic surface topography measurement sensor and its design study

This paper presents some aspects of design approach, modeling, and experimental measurement results of a fiber optic-based surface topography measurement sensor that can measure surface roughness as well as the distance between the sensor tip and a surface and surface inclination angles. The working principle of the sensor is based on the detection of light intensity reflecting from the surface being measured. The sensor is very small and easy to operate. It can be attached to a coordinate measuring machine (CMM) to measure surface position coordinates, inclination angles, and surface roughness in a noncontact manner at one measurement setup. A theoretical model of intensity distribution and intensity detection has been established for the sensor. A three-factor and three-level experiment was designed to investigate the relationship between sensor performance and sensor design parameters. Two second-order regression models have been generated, which show that the central distance between the emitting and receiving fibers of a sensor has the strongest influence on the effective range of the sensor; whereas, the critical angle of a receiving fiber influences the sensitivity of the sensor most.     

Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts

Surface roughness of the workpiece is an important parameter in machining technology. Wiper inserts have emerged as a significantly class of cutting tools, which are increasingly being utilized in last years. This study considers the influence of the wiper inserts when compared with conventional inserts on the surface roughness obtained in turning. Experimental studies were carried out for the carbon steel AISI 1045 because of its great application in manufacturing industry. Surface roughness is represented by different amplitude parameters (R_a, R_zD, R_3z, R_q, R_t, R_a/R_q, R_q/R_t, R_a/R_t). With wiper inserts and high feed rate it is possible to obtain machined surfaces with R_a < 0.8 μm (micron). Consequently it is possible to get surface quality in workpiece of mechanics precision without cylindrical grinding operations.     

Laser based measurement for the monitoring of shaft misalignment

This paper presents a method for real-time online monitoring of shaft misalignment, which is a common problem in rotating machinery, such as the drive train of wind turbines. A non-contact laser based measurement method is used to monitor positional changes of a rotating shaft in real time while in operation. The results are then used to detect the presence of shaft misalignment. An experimental test rig is designed to measure shaft misalignment and the results from the work show that the technique can be used for the monitoring of both offset and angular shaft misalignment, which will have applications in the condition monitoring and maintenance of various types of rotating machinery.     

A study on the characteristics of a wafer-polishing process according to machining conditions

The polishing process for silicon wafers plays a key role in the fabrication of semiconductors, since a globally planar, mirrorlike wafer surface is achieved in the process. The surface roughness of the wafer depends on the surface properties of the carrier head unit, together with other machining conditions, such as working speed, type of polishing pad, temperature, and down force. In this paper, the results of several experiments are used to study silicon wafer surfaces. The experiments were designed to observe the down force and temperature when a wafer carrier head unit with wafer was pressed down onto a polishing pad. A load cell was employed to detect the applied pressure against the polishing pad, and the working temperature was measured with an infrared sensor. Wafer surface roughness was investigated according to several parameters and experimental data.     

Contact and non-contact approaches in load monitoring applications using surface response to excitation method

Surface response to excitation (SuRE) method is a low-cost alternative to electromechanical impedance based structural health monitoring (SHM) technique. The SuRE method uses one piezoelectric transducer to excite the surface of a structure with a sweep sine wave. Piezoelectric sensors or scanning laser vibrometer can be used to monitor the dynamic response of structure.In this study, the performance of the SuRE method was evaluated with the conventional piezoelectric elements and scanning laser vibrometer used as contact and non-contact sensors, respectively, for monitoring the presence of loads on the surface. In order to determine the accuracy and reliability of both monitoring approaches in detecting changes in level of applied load, three different experimental setups were studied. Response of a system in the presence of a single ​load applying and multiple loads applying and its performance in detecting tightness in a nut and bolt system were investigated. The spectrum of the dynamic response is collected at the optimal operating condition. Any significant change of the spectral characteristics may indicate defects, improper loading or loose fasteners. The performance of the SuRE method using contact and non-contact sensors indicated that both variations of the method could be successfully used in load monitoring applications.     

Monitoring surface roughness of thin-walled components from steel C45 machining down and up milling

Article deals with problematic of milling thin wall components, than about study of surface roughness and analytical prediction of surface roughness Rz for variable geometrical parameters. First part is dedicated to research of realized experiments of manufacturing thin wall components, what was basis for designing of experiment. Experiment was conducted in two phases, where first was based on up milling and second on down milling for left and right side of thin wall components with thickness 10 mm. Subsequently, the surface roughness Rz was evaluated and was determinate mathematical equations for each type of milling, side and also for each depth. Final output of presented article is mathematical model of surface roughness Rz prediction for constant cutting conditions, but for variable geometrical parameters of thin wall components with thickness 10 mm.     

Cr2N/CrN multilayer coatings for wood machining tools

The present paper describes results from a recent research project aimed at forming a wear resistant coating based on chromium on tools to wood machining. Cr₂N/CrN multilayer coatings deposited on HS6-5-2 steel substrates using cathodic arc evaporation were tested. These coatings were formed from 7 bilayers being ca. 340 nm thick and equally thick Cr₂N and CrN layers. For comparison, Cr₂N and CrN monolayer coatings were also prepared. Hardness measurements, indentation and scratch tests, friction and wear were performed to characterize the mechanical properties. The wear tracks and Rockwell indentations enable to assess wear mechanisms of the coatings. The results of the Cr₂N/CrN coatings investigated show high hardness: ca. about 22 GPa and a critical force being higher than 95 N and a low wear rate.The industrial tests of planer knives with Cr₂N/CrN multilayer coatings were carried out on a down-spindle milling machine to determine the durability of tools with wear resistant coatings for woodworking. These tools show increase of “life time” two times. Another positive feature of the use of such tools is the increase of the quality of wood surface machined when compared with uncoated tools.     

图像式角位移测量的光栅偏心度监测系统

图像式角位移测量装置中,光栅的安装偏心标定结果直接影响着角位移测量的精度。为此,本文设计了一种用于调试图像式角位移测量装置光栅偏心度的系统。首先,根据图像式角位移测量机理,提出了基于线阵图像传感器的标定光栅偏心度监测原理;然后,在图像传感器上建立了偏心调试监测信号的模型,并提出存在偏心时偏心监测信号的变化机理;最后,对某型号角位移测量装置进行了实验,并给出了调试建议。实验表明,经过调节误差均方差由1017″降低到12.8″。本文设计的偏心监测系统能够实现对标定光栅的高精度安装调试,提高了图像式角位移测量装置的批量生产效率。     

Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron

This paper focused on optimizing the cutting conditions for the average surface roughness (R_a) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials. Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L₁₈ orthogonal array was used to design of experiment. Optimal cutting conditions was determined using the signal-to-noise (S/N) ratio which was calculated for R_a according to the “the-smaller-the-better” approach. The effects of the cutting parameters and tool materials on surface roughness were evaluated by the analysis of variance. The statistical analysis indicated that the parameters that have the biggest effect on R_a for Ni-Hard materials with 50 HRC and 62 HRC are the cutting speed and feed rate, respectively. Additionally, the optimum cutting conditions for the materials with 50 HRC and 62 HRC was found at different levels.     

Experimental investigation and numerical analysis for machinability of alumina ceramic by laser-assisted grinding

Alumina (Al₂O₃) ceramic has been widely used in various fields, but it has certain difficulties in machining as a hard and brittle material. While laser-assisted grinding (LAG), an alternative and novel method for fabrication of alumina ceramic, can utilize laser beam to locally heat the workpiece before the ceramic is removed, thereby reducing fracture toughness and keeping the surface integrity. In this paper, a thermal model is established to study and understand the processing mechanism of the LAG process. Meanwhile, an orthogonal experiment is designed and implemented to optimize the grinding process. Then, by analyzing the surface topography, the advantages of LAG are strongly proved. It is found that the temperature modelling results matches experimental results well. The processing parameter that has greatest impact on surface roughness is laser power, followed by grinding depth and wheel speed, and feeding speed at last. The optimal surface roughness value can be obtained by certain processing parameters. Also, compared to conventional grinding (CG), the removal method of alumina ceramics alters from brittle fracture to plastic fracture. Overall, this study clearly elucidates that LAG of alumina ceramic is a very promising machining method, and can be potentially utilized for various industrial, aerospace and automobile applications.     

三维表面粗糙度测量方法综述

表面粗糙度测量是评估零件表面特性的重要手段。经过二十多年的发展,三维表面粗糙度逐渐成为反映工件表面特性的重要指标。本文总结并比较了多种三维表面粗糙度的测量方法及特点,根据测量过程是否接触被测表面,将三维表面粗糙度测量方法分为接触式测量法与非接触式测量法。接触式测量法主要包括触针扫描法;非接触式测量法主要包括光学法(相移干涉显微法、相干扫描干涉法、数字全息法、共聚焦显微法、共聚焦色差显微法、点自动对焦法、变焦法、激光三角法和光切法)和电镜法(扫描电子显微法、扫描隧道显微法、扫描近场光学显微法和原子力显微法)。针对每种测量方法的发展现状,本文讨论了其适用范围及局限性,并指出未来的发展方向。     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

Surface characterisation by parameter-based technique, slicing method and PSD analysis

An effort has been made to compare the capability and results of three different surface roughness characterisation methods (parameter-based technique, slicing method and power spectral density analysis). Although all of these surface characterisation techniques are well-known such a comparison has not been made yet. It was found that 3D surface roughness parameters widely used in practice do not show any correlation with the surface finishing technique applied because of the inhomogenity of investigated surfaces. Contrary to this, based on parameters provided by the slicing method, such as width and orientation of asperities, ratio of major and minor axis of asperities, the different surface finishing techniques can be distinguished clearly. Additionally, it was found that the different machining technologies cannot be properly separated, in every case, by the fractal dimension. In other words, fractal dimensions may be very similar even if they belong to two different surface finishing techniques.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

Abstract

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

ISSUES IN MODELING MACHINED SURFACE TEXTURES

Abstract

This paper discusses the issues involved in modeling surface textures generated during machining and presents the potential of scale-sensitive fractal analysis in addressing these issues. Modeling surface texture creation supports the design machining processes for producing product surface textures with desired properties. The current understanding of how surface textures behave is largely incomplete. In order to understand how surface textures influence surface behavior, it is necessary to a) develop surface texture characterization methods with enough sophistication to capture the essence of the information inherent in the surface texture, and b) convey it so that the discovery of functionality, or behavior, of the texture is enabled. For a model of texture formation to be useful, the parameters that it calculates must be the same as the parameters that explain the texture behavior.     

ISSUES IN MODELING MACHINED SURFACE TEXTURES

This paper discusses the issues involved in modeling surface textures generated during machining and presents the potential of scale-sensitive fractal analysis in addressing these issues. Modeling surface texture creation supports the design machining processes for producing product surface textures with desired properties. The current understanding of how surface textures behave is largely incomplete. In order to understand how surface textures influence surface behavior, it is necessary to a) develop surface texture characterization methods with enough sophistication to capture the essence of the information inherent in the surface texture, and b) convey it so that the discovery of functionality, or behavior, of the texture is enabled. For a model of texture formation to be useful, the parameters that it calculates must be the same as the parameters that explain the texture behavior.     

直齿锥齿轮刨齿加工在线检测与机床调整

文章介绍了对齿厚、齿高、齿深等参数进行预测并在线测量,及时调整机床参数,纠正偏差,从而提高直齿锥齿轮刨齿加工精度的方法和具体步骤.