Nanostructuring Ultra-thin Co Films to Active Catalyst Particles for Vertically Aligned Single-Walled CNT Growth

In the field of material synthesis using chemically-derived technique, nanostructuring metal catalyst particles towards high quality production of carbon nanotubes (CNTs) has been very attractive. In this work, cobalt (Co) which used as catalyst for vertical growth of CNTs were studied by means of transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). Aluminum (Al) films (20 nm) were thermally-oxidized to form aluminum oxide (Al-O) as to support 0.5 nm Co catalyst during CNT growth. In growing CNTs by using chemical vapor deposition (CVD) technique, the role and characters of all involving materials are crucial to the growth result. From the Co/Al-O substrate and at 650 oC of CVD temperature, 33-m thick of vertically aligned single-walled CNT (VA-SWCNT) forest was grown. TEM particle analysis revealed that the Co particles have an average of 3.50 nm which experimentally and in principle favored the growth of highly demanded VA-SWCNTs. The as-prepared Co particles are suggested chemically active for the CNT growth. In addition, XPS analysis confirmed the surface chemical state of Co particles prior to the VA-SWCNT growth using ethanol based CVD system

Effects of End Mill Helix Angle on Accuracy for Machining Thin-Rib Aerospace Component

Accuracy of machined component is one of the challenging tasks for manufacturer. In the aerospace industry, machining process is widely used for fabrication of unitized-monolithic component that contains a thin-walled structure. During machining, the cutting forces cause deflection to the thin-wall section, leading to dimensional form errors that cause the finished part to be out of specification or failure. Most of the existing research for machining thin-wall component only concentrated on the process planning and the effects of cutter geometric feature is often neglected. Tool geometric feature has a direct influence on the cutting performance and should not be neglected in the machining consideration. This paper reports on the effect of helix angle on the magnitude of wall deflection. The established effects will be used for the development of high performance cutting tool for specifically machining thin-wall component

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process

Finite Element Model of Machining with High Pressure Coolant for Ti-6Al-4V alloy

This study present a series of finite element models for high-pressure jet-assisted machining of Ti-6Al-4V alloy. The application of Fluid-Structure Interaction (FSI) together with the Johnson-Cook plasticity model, Cockcroft-Latham chip separation criteria and EOS polynomial were implemented to study the effect of coolant pressure on chip formation, cutting force and cutting temperature. The resulting motion of fluid at the tool-chip interface, chip breakage, cutting force as well as temperature generation at the tool-chip interface is then interpreted, analyzed and compared to their real experimental results. The models simulate interactions between the fluid and solid structure, where continuous chip formation was observed when simulation in conventional coolant supply while chip breakage was clearly evident as high-pressure coolant was introduced. Increasing coolant pressure significantly reduce the friction at the tool-chip interface, which significantly reduced the cutting force and cutting temperature

Transforming Linear Laser Cutting machine Into Laser Lathing – An Empirical Investigation & Evaluation of Roundness Quality

Lasers are widely used in industries as cutting tools due to ultra-flexibility cutting capabilities in obtaining high quality end product while posing advantage of quick set up, non-mechanical contact mechanics, and small region of heat affected zone. This paper presents the laser lathing performance of 2D CO2 flatbed laser cutting machine by investigating the roundness quality. A specially designed mechanism was developed to clamp and spin a steel rod of 10mm diameter cylindrical workpiece on X to Y sacrificial table. Three significant cutting parameters were controlled in this experiment namely; cutting speed, spinning speed and depth of cut. The experiments were carried out based on DOE full factorial matrix design. The results were realistic, promising and efficient as compared to manual lathes within the same range of workpiece properties and dimension

Finite Element Model of Machining with High Pressure Coolant for Ti- 6Al-4V alloy

This study present a series of finite element models for high-pressure jet-assisted machining of Ti-6Al-4V alloy. The application of Fluid- Structure Interaction (FSI) together with the Johnson-Cook plasticity model, Cockcroft-Latham chip separation criteria and EOS polynomial were implemented to study the effect of coolant pressure on chip formation, cutting force and cutting temperature. The resulting motion of fluid at the tool-chip interface, chip breakage, cutting force as well as temperature generation at the tool-chip interfaceis then interpreted, analyzed and compared to their real experimental results. The models simulate interactions between the fluid and solid structure, where continuous chip formation was observed when simulation in conventional coolant supply while chip breakage was clearly evident as high-pressure coolant was introduced. Increasing coolant pressure significantly reduce the friction at the tool-chip interface, which significantly reduced the cutting force and cutting temperature

Experimental Analysis On Parameters Affecting The Material Removal Rate In Wire Electrical Discharge Turning Using The Taguchi Method

Wire electrical discharge turning (WEDT) allows success in the machining of advanced materials, particularly for the cylindrical shape components used in the metrology, medical and aerospace field. Using WEDT technology, the micro dimension of geometrical features can be machined without constraints on difficult-to-machine electrically conductive materials. The high precision with large aspect ratio machining capability make WEDT valuable. The correct machining parameter selection is the most important aspect to take into consideration when machining by WEDT. Therefore, the purpose of this study was to rank, determine and optimise the machining parameters that affect the material removal rate (MRR) in WEDT process. As WEDT has as much as eleven control parameters in this study, representing a large number of machining, the Taguchi L-12 orthogonal array was used to obtain the required data with a minimum number of experiments. By using similar experimental data, the regression equation was developed to represent as fitness function for optimisation by genetic algorithm (GA). It was found that by selecting the fast feed of workpiece to machining zone, the faster material was removed and moreover, it dominated other machining parameters. The optimised parameters yield 1.398 mm³/min as the maximum value predicted for MRR recommended by GA