Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al606

Optimization of technological parameters when polishing sic materials by magnetic compound fluid with the straight electromagnetic yoke

Crystallized silicon carbide (SiC) wafers are widely used in the field of integrated circuits as well as essential in the epitaxial growth of graphene and are one of the promising materials for applications in electronics at future high capacity. The surface quality of the required ultra-fine crystalline silicon wafer is the most essential factor in achieving graphene's desired electronic properties. Aiming to produce superfine surface quality SiC wafers, in this study, a new algorithm is developed to solve optimization problems with many nonlinear factors in ultra-precision machining by magnetic liquid mixture. The presented algorithm is a collective global search inspired by artificial intelligence based on the coordination of nonlinear systems occurring in machining processes. A new algorithm based on the optimization collaborative of multiple nonlinear systems (OCMNO) with the same flexibility and high convergence was established in optimizing surface quality when polishing the SiC wafers. To show the effectiveness of the proposed OCMNO algorithm, the benchmark functions were analyzed together with the established SiC wafers polishing optimization process. To give the best-machined surface quality, polishing experiments were set to find the optimal technological parameters based on a new algorithm and straight electromagnetic yoke polishing method. From the analysis and experimental results when polishing SiC wafers in an electromagnetic yoke field when using a magnetic compound fluid (MCF) with technological parameters according to the OCMNO algorithm for ultra-smooth surface quality with Ra=2.306 nm. The study aims to provide an excellent reference value in optimizing surface polishing SiC wafers, semiconductor materials, and optical device

How heterogeneous are the determinants of total factor productivity in manufacturing sectors? Panel-data evidence from vietnam

One of the remaining challenges in explaining differences in total factor productivity is heterogeneity between sectors and within a specific sector in terms of labor and capital. This paper employs the generalized method of moments (GMM) to identify factors that affect total factor productivity across 21 manufacturing sectors and to clarify the heterogeneous determinants of total factor productivity within manufacturing sectors for the period 2010–2015. Our estimations show that large firms have significantly greater total factor productivity levels than small firms in some fragmentations of firms in terms of both labor and total capital and in some manufacturing sectors. It is suggested that firm characteristics should be considered by the government in establishing relevant policies for enhancing firm productivity

Dynamic response analysis of truss bridges under the effect of moving vehicles

With the characteristics of heavy and concentrated loads, the influence of moving loads on the dynamic response of the bridges is significant. Therefore, in this paper, the dynamic response of a large-scale truss bridge is studied to consider the effect of the various parameters of moving loads. The considered main parameters consist of moving mass, moving velocity, and type of moving loads. The nonlinear dynamics of the bridge based on time history analysis are obtained using the Wilson-  method. four time history – based dynamic analysis method including modal superposition in frequency domain, modal superposition in time domain; direct time integration, and direct solution in the frequency domain are employed to analysis the obtained results. To compare the effectiveness of the aforementioned method. A large-scale railway truss bridge is employed for dynamic response analysis. The obtained results give more insight into the nature of the problem and help to determine the significant parameters of moving load affecting the bridge response

A RESEARCH ON MULTI-OBJECTIVE OPTIMIZATION OF THE GRINDING PROCESS USING SEGMENTED GRINDING WHEEL BY TAGUCHI-DEAR METHOD

In this study, the mutil-objective optimization was applied for the surface grinding process of SAE420 steel. The aluminum oxide grinding wheels that were grooved by 15 grooves, 18 grooves, and 20 grooves were used in the experimental process. The Taguchi method was applied to design the experimental matrix. Four input parameters that were chosen for each experiment were the number of grooves in cylinder surface of grinding wheel, workpiece velocity, feed rate, and cutting depth. Four output parameters that were measured for each experimental were the machining surface roughness, the system vibrations in the three directions (X, Y, Z). The DEAR technique was applied to determine the values of the input parameters to obtaine the minimum values of machining surface roughness and vibrations in three directions. By using this technique, the optimum values of grinding wheel groove number, workpiece velocity, feed-rate, cutting depth were 18 grooves, 15 m/min, 2 mm/stroke, and 0.005 mm, respectively. The verified experimental was performed by using the optimum values of input parameters. The validation results of surface roughness and vibrations in X, Y, Z directions were 0.826 (µm), 0.531 (µm), 0.549 (µm), and 0. 646 (µm), respectively. These results were great improved in comparing to the normal experimental results. Taguchi method and DEAR technique can be applied to improve the quality of grinding surface and reduce the vibrations of the technology system to restrain the increasing of the cutting forces in the grinding process. Finally, the research direction was also proposed in this stud

Approximation solution for steel concrete beam accounting high-order shear deformation using trigonometric-series

Steel concrete beams have a reasonable structure in terms of using material and high load carrying capacity. This paper deals with an approximate solution based on a trigonometric series for the static of steel concrete beams. The displacement field is based on the higher-order theory using Reddy’s hypothesis. The governing equations are derived from variation principles. An approximate solution based on the representation of displacement fields by trigonometric series is developed to solve the static problem of steel concrete beams. In order to verify the accuracy of the present approximate solution, numerical results are compared with those of exact solutions using classical beam theory. The displacements and nominal stress distributions in the depth direction are obtained with various high of beams. The present approximate approach can accurately predict the displacements and stresses of steel concrete beams

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy