Experimental Study and Finite Element Analysis of Temperature Reduction and Distribution During Machining of Al-Si-Mg Composite Using Deform 3D

Composite materials are promising materials in the manufacturing industry due to the quality of their materials. However, in transforming these materials, the machining process experiences a high-heat generation rate, which has led to the study of temperature distribution, and reduction analysis at the cutting region. High-temperature generation during machining operation leads to thermal deformation on the developed component or parts, affecting the operation life span of the component. Thus, this study investigated the effect of mineral oil-based-Multi-walled carbon nanofluid (MWCNTs) compared to pure mineral oil in the turning of aluminum-silicon magnesium metal composite (AlSiMg) on temperature reduction and distribution. The nanofluid was prepared with 0.4g of MWCNT to 1 liter of mineral oil. The study employed the energy dispersive spectrometer to obtain the chemical composition of the developed nanofluid. Furthermore, Finite element software DEFORM 3D v11.0 uses a lagrangian incremental approach to simulate chip formation and temperature distribution on the workpiece. Also, to study the effects of the machining parameters on the temperature distribution. The experiment results showed a significant reduction of 11.9% in temperature when machining with nanofluid compared to pure mineral oil. The simulation results showed that the temperature increases as the cutting speed and feed rate increase. The minimum temperature via the DEFORM 3D Finite Element Model simulation was achieved at spindle speed 870 rpm, feed rate 2 mm/rev, and depth-of-cut 1 mm. In conclusion, the study recommends that the manufacturing industry employ the optimized machining parameters during the turning of AlSiMg metal matrix composite for a sustainable machining process

Optimization of Processing Data Time for Stephens Bread Industries Owerri, Imo State, Nigeria

Abstract-This research work is aimed at modeling production processes in bread industries using linear programming model as optimization model in a given bread industry (Stephens Bread) for effective and efficient production. The L.P model was deduced to be the best optimization model to be used in any bread industry since the model analytically gives a simultaneous result of profit maximization and cost minimization. Specifically, a designed simulator in a MATLAB and Graphical Users Integrated Development Environment (GUIDE) windows (BREADPROD) that are capable of detecting the exact quantity of bread to be produced and the optimization level to maintain in any bread industry was developed. The simulator was subjected to various initial input conditions in bread production processes which include mixing, matching, moulding and baking processes with three (3) different sizes of bread loaves: the giant, the long and small loaves with reference to non-basic variables -x 1 , x 2 , x 3 respectively. BREADPROD, the designed simulator, equally shows how realistic or unrealistic bread production could be. The application of L.P model which agreed with the designed BREADPROD simulator gave different profits for the two bread industries in this study while the optimum recommended production gave a higher profit of over one hundred percent when compared with that of Stephens Bread industries. The result of the proposed production mix used gave a production mix of 202 giant loaves, 102 long loaves and 92 small loaves representing 51%, 26% and 23% of the total production respectively as against the production mix for Stephens Bread, which gave 115 giant loaves, 40 long loaves and 110 small loaves. The giant loaf, long loaf and small loaf are 43%, 42% and 15% of the total production respectively