Understanding the Heat Transfer Coefficient (HTC) is essential for evaluating cooling media used in the immersion quenching of steels. This HTC characterizes the heat exchange between the immersed workpiece and the quenchant. Calculating the HTC involves solving an inverse heat transfer problem, which typically requires stochastic optimization algorithms. These algorithms use iterative processes and can be computationally demanding, often needing hundreds or thousands of iterations to find a solution. To reduce this computational burden, this paper introduces an initialization technique that employs a non-iterative approach to solve the inverse heat transfer problem. The proposed method uses an artificial neural network (ANN), specifically a multi-layer feedforward neural network trained with the backpropagation algorithm. A synthetic database with 150,000 records of heat transfer coefficients, determined as a function of temperature, is created for training the network. Unconventionally, the Fourier transform of the cooling curve is used as input for the inference system. Additionally, the performance of the neural network is compared with other conventional learning algorithms. Results show that when combined with stochastic algorithms, the ANN achieves comparable solutions in a shorter amount of time.

The automotive industry has searched for alternatives to reduce the weight of vehicles without neglecting the user’s safety by using new materials. Advanced high-strength steels of complex phases are used in structural applications requiring good performance and reducing the weight of vehicles. However, these steels have shown edge cracking, known as fissure, during processing, which has become a challenge for steelmakers and other companies that rely on them to manufacture structural components. Such defects can be associated with the interaction between the different microstructural constituents of the steel, such as various phases and precipitates generated during its processing to achieve the required mechanical properties. The present work presents the studies evaluate the effect that processing and chemical composition exerts on edge cracking in complex phase steels of grade 800 MPa produced by different steelmaking routes.

In this paper, we study the energy absorption of metamaterials composed of unit cells whose special geometry makes the cross-sectional area and the volume of the bodies generated from them constant (for the same enclosing box dimensions). After a parametric description of such special geometries, we analyzed by finite element analysis the deformation of the metamaterials we have designed during compression. We 3D printed the designed metamaterials from plastic to subject them to real compression. The results of the finite element analysis were compared with the real compaction results. Then, for each test specimen, we plotted its compaction curve. By fitting a polynomial to the compaction curves and integrating it (area under the curve), the energy absorption of the samples can be obtained. As a result of these investigations, we drew a conclusion about the relationship between energy absorption and cell number.

The knowledge of the thermal boundary conditions helps to understand the heat transfer phenomena that takes place during heat treatment processes. Heat Transfer Coefficients (HTC) describe the heat exchange between the surface of an object and the surrounding medium. The Fireworks Algorithm (FWA) method was used on near-surface temperature-time cooling curve data obtained with the so-called Tensi multithermocouple 12.5 mm diameter x 45 mm Inconel 600 probe. The fitness function to be minimized by a Fireworks Algorithm (FWA) approach is defined by the deviation of the measured and calculated cooling curves. The FWA algorithm was parallelized and implemented on a Graphics Processing Unit architecture. This paper describes the FWA methodology used to compare and differentiate the potential quenching properties of a series of vegetable oils, including cottonseed, peanut, canola, coconut, palm, sunflower, corn, and soybean oil, versus a typical accelerated petroleum oil quenchant.

In various studies, heat transfer coefficients (HTCs) have been used to characterize the relative ability of a quenching medium to harden steel. In this current work, HTCs are determined for a series of vegetable oils using a stochastic (particle swarm) optimization technique and cooling curves produced via Tensi probe measurements. The vegetable oils investigated include canola, coconut, corn, cottonseed, palm, peanut, soybean, and sunflower oil, and their quenching performance is compared with that of a typical petroleum oil quenchant.