The present study is devoted to extending Barlat’s famous yield criteria to tension–compression asymmetry by a novel method originally introduced by Khan, which can decouple the anisotropy and tension–compression asymmetry characteristics. First, Barlat (1987) isotropic yield criterion, which leads to a good approximation of yield loci calculated by the Taylor–Bishop–Hill crystal plasticity model, is extended to include yielding asymmetry. Furthermore, the famous Barlat (1989) anisotropic yield criterion, which can well describe the plastic behavior of face-centered cubic (FCC) metals, is extended to take the different strength effects into account. The proposed anisotropic yield criterion has a simple mathematical form and has only five parameters when used in planar stress states. Compared with existing theories, the new yield criterion has much fewer parameters, which makes it very convenient for practical applications. Furthermore, all coefficients of the criterion can be determined by explicit expressions. The effectiveness and flexibility of the new yield criterion have been verified by applying to different materials. Results show that the proposed theory can describe the plastic anisotropy and yielding asymmetry of metals well and the transformation onset of the shape memory alloy, showing excellent predictive ability and flexibility.

This study exhibits the possibility of using an artificial neural network (ANN) to model the mechanical behavior of wire arc additive manufacturing (WAAM) for Inconel 625. For this reason, tensile tests of Inconel 625 superalloy as-built (AB) samples and samples after heat treatment at 1200 °C (HT-1200) by WAAM were performed. For the HT-1200 samples, the yield stress decreased, and the elongation increased significantly due to grain refinement and the formation of annealed twins. A new hybrid model combining a swarm intelligence optimization algorithm with a back propagation neural network (BPNN) was developed to simulate the flow behavior of the superalloy. Compared with other hybrid BPNN models that have been reported, the proposed BPNN model is in better agreement with the experimental data and provides a better description of the flow stress of the Inconel 625 superalloy. The excellent predictive ability of the model may be attributed to the optimization of the weights and thresholds of the BPNN, which obtains the optimal global solution in the search space more efficiently.

Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has been shown that multiple surrogates can characterize the fitness landscape more accurately than a single surrogate. In this work, a multi-surrogate-assisted multi-tasking optimization algorithm (MSAMT) is proposed that solves high-dimensional problems by simultaneously optimizing multiple surrogates as related tasks using the generalized multi-factorial evolutionary algorithm. In the MSAMT, all exactly evaluated samples are initially grouped to form a collection of clusters. Subsequently, the search space can be divided into several areas based on the clusters, and surrogates are constructed in each region that are capable of completely describing the entire fitness landscape as a way to improve the exploration capability of the algorithm. Near the current optimal solution, a novel ensemble surrogate is adopted to achieve local search in speeding up the convergence process. In the framework of a multi-tasking optimization algorithm, several surrogates are optimized simultaneously as related tasks. As a result, several optimal solutions spread throughout disjoint regions can be found for real function evaluation. Fourteen 10- to 100-dimensional test functions and a spatial truss design problem were used to compare the proposed approach with several recently proposed SAEAs. The results show that the proposed MSAMT performs better than the comparison algorithms in most test functions and real engineering problems.

This paper experimentally investigated the fabrication and optimization of micro-scale gratings formed by nanosecond laser etching. The mechanism of nanosecond laser processing and the geometric phase analysis (GPA) are discussed, and the factors influencing the fabrication process including laser energy, laser fluence, and ablation threshold of material, are experimentally studied. In order to eliminate the dependence of the processing parameters on the samples, depositing Al film on a sample before laser processing is proposed for the fabrication of high-quality gratings. The energy of the laser pulse is optimized for clear line etching on Al film considering the distance between adjacent lines of parallel gratings. The optimal energy of the laser pulse is 9.8 μJ, and the optimum fluence is 9.5 J/mm² with the waist radius of the laser beam 25.7 μm. With the optimal parameters, experimental results indicate that the highest frequency of parallel gratings is about 30 lines/mm, with a line width of 29 μm, and the distance between two adjacent laser pulses being of 10 μm. By performing tensile tests, micro-scale gratings fabricated on specimens are experimentally verified. The verification tests prove that the proposed fabrication method for the micro-scale gratings in GPA measurements is reliable and applicable, and the micro-scale gratings can be fabricated in many areas of interest, such as the crack tip, for deformation measurements. Furthermore, the adhesion between the Al film and the tested sample is strong enough so that the pattern sticks well to the sample.

Graphene nanoplatelets (GNPs) have significant potential as reinforcement agents in metallic materials. Understanding the interfacial structure between GNPs and an aluminum matrix (AlM) is crucial for this composite (GNPs/AlMC) design and application. Studies on the effects of heat treatment on the interfacial character and its influence on mechanical properties at the atomic scale are, therefore, of high importance. This study presents molecular dynamics simulations investigating the effects of heat treatment on the interfacial structural evolution between GNPs and AlM and how such behavior tunes mechanical performance. The results show that the number of interfacial bonds increases significantly when the heat treatment temperature reaches 900 K, below which it stays at low level. Compared with the heat treatment temperature, the pressure has less effect on the interfacial bonds of GNPs/AlMCs. Moreover, defective GNPs with pores at certain heat treatment temperatures promote the formation of Al-C bonds at the interface and improve the mechanical properties of GNPs/AlMCs. These findings underscore the potential for utilizing heat treatment and defect engineering to improve interfacial bonding and achieve improved mechanical performance in GNPs/AlMCs.