The heat treatment process is important to conduct on A356 Aluminum Alloy that broadly used in an automotive application. Analysis of the effect grain refinement, eutectic silicon modification of T6 solutionizing heat treatment parameter on strength development of A356 Aluminum Alloy were performed to become an important prospect of controlling the behaviour of materials as well as duration and temperature. In this study, there are three parameters condition were conducted which is duration Time (Hour) and Temperature (°C) which is 465°C in 8 hours, 500°C in 7 hours and 535°C in 6 hours, respectively. The study generated three responses that are tensile, hardness test and microstructure analysis of the different temperature and duration time, respectively following by water-quenching. The result showed that the modification of duration time and high temperature of solutionizing heat treatment have superior mechanical properties while the microstructure analysis showed the clear formation of refinement α-Al dendrite and eutectic silicon particle.

Thixoforming is a promising metal forming process to produce near net-shape components with high casting quality. Thixoforming of metallic alloy utilizes the thixotropic behaviour of the material with near globular or globular microstructure in the semi-solid condition. The solid content is between 50% to 70% before forming. In this paper, the effect of plunger speed and a solid fraction on an alternator housing was investigated by advance casting simulation software. The 3D CAD model of the alternator housing was created using SolidWorks software and AnyCasting software is utilized for the simulation of the thixoforming parameter and magnesium alloy (AZ91D) is the material used. The simulation had been done by varying the plunger speed, temperature (solid fraction) of the material, and both Power Law and Bingham Viscosity model are used in the simulation to identify the defect prediction at the end. The simulation result shows that laminar filling in semi-solid slurry able to achieved by controlling the plunger speed and temperature. Slower speed and lower melt temperatures are preferable in thixoforming. Therefore, a solid fraction of the material, plunger speed, and solidification rate do influence the filling behaviour of the casting of semi-solid metal.

Magnesium alloy usage in manufacturing engineering components resulting in weight reduction and as a consequence, reduction in fuel and energy consumption. Magnesium has a relatively low density and roughly 30% lighter than aluminum. However, magnesium is considered to be difficult to deform because of the HCP structure. In this present work, the effect of semi-solid forming temperature and heat treatment on mechanical properties of Mg-Al-Zn were investigated. Mg-Al-Zn ingot was machined into a billet and formed with three different temperatures and underwent T4 heat-treatment process. To determine the mechanical properties and microstructure of the magnesium alloy, tensile and hardness test were performed and the result indicates that the highest average maximum tensile stress was achieved at 209 MPa at 530ºC after forming with T4 heat treatment and highest hardness value was at  21.44 HRB at 560ºC. On the other hand, effect of the forming temperature gives impact to the evolution of the microstructure from large grain size (as-cast) to the smaller grains size (0.00797mm2) forming at 560°C. This relate to the extensive dynamic recrystallization (DRX) occurs during forming and Mg-Al-Zn was sensitive with heat either direct or indirect heating method.

This paper presents an automation process is a need in the agricultural industry specifically chili crops, that implemented image processing techniques and classification of chili crops usually based on their color, shape, and texture. The goal of this study was to develop a portable sorting machine that will be able to segregate chili based on their color by using Artificial Neural Network (ANN) and to analyze the performance by using the Plot Confusion method. A sample of ten green chili images and ten red chili images was trained by using Learning Algorithm in MATLAB program that included a feature extraction process and tested by comparing the performance with a larger dataset, which are 40 samples of chili images. The trained network from 20 samples produced an overall accuracy of 80 percent and above, while the trained network from 40 samples produced an overall accuracy of 85 percent. These results indicate the importance of further study as the design of the smart sorting machine was general enough to be used in the agricultural industry that requires a high volume of chili crops and with other differentiating features to be processed at the same time. Improvements can be made to the sorting system but will come at a higher price.

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