Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

Thin-wall parts are common in the aeronautical sector. However, their machining presents serious challenges such as vibrations and part deflections. To deal with these challenges, di erent approaches have been followed in recent years. This work presents the state of the art of thin-wall light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the causes of both instability and deformation through analytical models, summarizing the computational techniques used, and presenting the solutions proposed by di erent authors from an industrial point of view. Finally, some further research lines are proposed

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

Unstable Temperature Distribution in Friction Stir Welding

In the friction stir welding process, a nonuniform and high generated temperature is undesirable. Unstable temperature and distribution affect thermal and residual stresses along the welding line, thus necessitating mitigation. This paper presents a simple method to prevent significant temperature difference along the welding line and also to help nullifying some defect types associated with this welding, such as end-hole, initial unwelded line, and deformed areas. In the experimental investigation, a heat and force thermocouple and dynamometer were utilized while couple-field thermomechanical models were used to evaluate temperature and its distribution, plastic strain, and material displacement. The suggested method generated uniform temperature distributions. Measurement results are discussed, showing a good correlation with prediction

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

Modeling and optimization of cutting parameter during Wire-EDM of Inconel 718 using response surface

This paper presents a study of the relationship between wire electrical discharge machine (WEDM) parameters and surface finish. The study analyzed the relationship between cutting parameter as variable input and surface roughness as output. To optimize the surface roughness which were measured both in horizontal and vertical direction, a Box Behnken of response surface design (RSM) with three process parameter, voltage, current and feed rate is used. Analysis of variance (ANOVA) was used to identify the identified the parameters affecting the machined surface finish. With the result of P-Value and F-Value, machining current is known as the most contributing factor, followed by feed rate and machining voltage. The direction of measurement was found not to be significant. The linear prediction models were developed with average error of 3.67% and 1.74% horizontal and vertical surfaced roughness measurement direction respectively. The result of the validation also shows a good agreement with predicted values. It is beneficial as to assist machinist in improving the quality of the WEDM surface

Experimental Study on Effect of Reflector Bed Designs Heated by Direct Solar Radiation for Hot Water Storage System

The aim of this research is to investigate the performance of different reflector bed design heated by direct solar radiation for a water storage system that can be used in accordance with the climate of Malaysia country. In general, this research is related to thermal efficient water heating system, specifically to improve the water heating system that exists nowadays. The focus is to improve the thermal efficiency by adding different absorber bed designs. Based on experimental results shown the temperature of the water increases more efficient and faster by using the curve reflector bed

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