Cooling Curve Analysis of A356 Alloy by Conventional Casting and the Effect of Stirring

The present investigation attempted to explore the effect of stirring during solidification of Aluminum A356 alloy, mainly focusing on the change from dendrite to globular structure. For this purpose samples of A356 alloy were melted in the electrical resistance furnace and cooling curves were recorded for each level agitation. The experimental curves were numerically processed by calculating first and second derivatives. From these were determined temperatures and times of start nucleation of alpha solid and eutectic reaction.

A Study of the Convective Cooling of Large Industrial Billets

The thermodynamic heat-transfer mechanisms, which occur as a heated billet cools in an air environment, are of clear importance in determining the rate at which a heated billet cools. However, in finite element modelling simulations, the convective heat transfer term of the heat transfer mechanisms is often reduced to simplified or guessed constants, whereas thermal conductivity and radiative emissivity are entered as detailed temperature dependent functions. As such, in both natural and forced convection environments, the fundamental physical relationships for the Nusselt number, Reynolds number, Raleigh parameter, and Grashof parameter were consulted and combined to form a fundamental relationship for the natural convective heat transfer as a temperature-dependent function. This function was calculated using values for air as found in the literature. These functions were then applied within an FE framework for a simple billet cooling model, compared against FE predictions with constant convective coefficient, and further compared with experimental data for a real steel billet cooling. The modified, temperature-dependent convective transfer coefficient displayed an improved prediction of the cooling curves in the majority of experiments, although on occasion a constant value model also produced very similar predicted cooling curves. Finally, a grain growth kinetics numerical model was implemented in order to predict how different convective models influence grain size and, as such, mechanical properties. The resulting findings could offer improved cooling rate predictions for all types of FE models for metal forming and heat treatment operations.

Thermal Treatment under Vacuum for Obtaining a Quenchant from Rapeseed Oil

The aim of this study was to improve the quality of a vegetable oil, having in view its use as a quenchant for metallic parts in aircrafts. A process of pyrolysis under vacuum was applied to obtain a bio-oil with reduced viscosity and good quenching properties. Preliminarily, the rapeseed oil was fast pyrolyzed at temperature in the range of 300–375 °C and absolute pressure of 1 μbar. Some results such as viscosity and yields of bio-oil were obtained with a narrowing of the temperature range between 300–320 °C, for further processing. Quenching tests with bio-oils on stainless steel 25CD4 showed cooling curves closer to those of the standard mineral oil (Castrol IloquenchTM 1), by comparing them with unprocessed vegetable oil. The hardness of the steel after treatment rose from 29–30 HRC to 43–45 HRC, in accordance with requirements (35–45 HRC). Therefore, the conclusion is that bio-oils obtained by pyrolysis under vacuum are good quenchant proceeds from this study.

Finite Element Analysis of Temperature Distribution and Stress Behavior of Squeeze Pressure Composites

Aluminium-reinforced composites play a vital role in the engineering industry because of their better strength and stiffness. The properties are directly related to the solidification phenomenon of the cast alloy. The design engineer should understand the importance of the solidification behavior of base alloy and its reinforcement. Composites’ solidification study is rare, and the reviews are limited. The solidification process is analyzed using the finite element method (FEM), and this would fetch a lot of information about the cooling rate of the composites and also helps to reduce the time in experimentation. This paper reports and plots the cooling curves of Al/SiCp composites using simulation software. Cylindrical-shaped composites were developed using the squeeze casting method, and the experimental cooling curves were plotted using a K-type thermocouple. Composites samples were prepared at the following squeeze pressures: 0, 30, 50, 70, 100, and 130 MPa; melt and die temperature was kept constant at 800 and 400°C, respectively. The experimental and FEA cooling curves were compared, and it was agreed that the increase in the squeeze pressure increases the cooling rate of the developed composite. Furthermore, the effect of temperature distribution from the inner region of the melt and die material which causes the radial and tangential stress of components has also been examined.

Simulation of Thermal Cooling Curves in the Process to Ordering Alloys by the Monte Carlo Method

An algorithm is proposed for constructing curves of thermal cooling and ordering kinetics with a monotonic decrease in temperature for alloys to stoichiometric composition. Modeling is carried out by the Monte Carlo method in the model of a rigid crystal lattice and pair interatomic interactions. The application of the algorithm is illustrated by the example to a square lattice, taking into account interatomic interactions in the first two coordination spheres for alloys with the composition AB, A3B, and A3B5. The proposed model makes it possible to calculate individual sections of the phase diagrams to the state for binary alloys.

Intelligent LED Certification System in Mass Production

It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.