Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

Ensemble Deep Learning Ultimate Tensile Strength Classification Model for Weld Seam of Asymmetric Friction Stir Welding

Friction stir welding is a material processing technique used to combine dissimilar and similar materials. Ultimate tensile strength (UTS) is one of the most common objectives of welding, especially friction stir welding (FSW). Typically, destructive testing is utilized to measure the UTS of a welded seam. Testing for the UTS of a weld seam typically involves cutting the specimen and utilizing a machine capable of testing for UTS. In this study, an ensemble deep learning model was developed to classify the UTS of the FSW weld seam. Consequently, the model could classify the quality of the weld seam in relation to its UTS using only an image of the weld seam. Five distinct convolutional neural networks (CNNs) were employed to form the heterogeneous ensemble deep learning model in the proposed model. In addition, image segmentation, image augmentation, and an efficient decision fusion approach were implemented in the proposed model. To test the model, 1664 pictures of weld seams were created and tested using the model. The weld seam UTS quality was divided into three categories: below 70% (low quality), 70–85% (moderate quality), and above 85% (high quality) of the base material. AA5083 and AA5061 were the base materials used for this study. The computational results demonstrate that the accuracy of the suggested model is 96.23%, which is 0.35% to 8.91% greater than the accuracy of the literature’s most advanced CNN model.

Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix

The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay.  The process variables considered were surfactant concentration, , activation time, ,  activation temperature, , and chitosan dosage, .  The ANFILS models showed better modeling abilities of the adsorption data on the synthesized composite adsorbent than those of ANN for reason of lower % mean absolute deviation, lower % error value, higher coefficient of determination, , amongst others and lower error functions’ values than those obtained using ANN for both responses.  When applied RSM, the hybrid of RSM–genetic algorithm (RSM–GA) and ANFILS 3–D surface pot optimization technique to determine the optimal conditions for both responses, ANFILS was adjudged the best.  The ANFILS predicted optimal conditions were = 116.00 mg/L, = 2.06 h, = 81.2oC and = 5.20 g.  Excellent agreements were achieved between the predicted responses of 99.055% removal of naphthalene and 248.6375 mg/g adsorption capacity and their corresponding experimental values of 99.020% and 248.86 mg/g with % errors of -0.0353 and 0.0894 respectively.  Hence, in this study, ANFILS has been successfully used to model and optimize the conditions for the treatment of industrial wastewater containing polycyclic aromatic compounds, especially naphthalene and is hereby recommended for such and similar studies.

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land borders to its neighbors. Small and medium-sized farmers make up more than 85% of those who produce agricultural items. Numerous scholars have studied the transportation methods used by the Greater Mekong Subregion (GMS) nations along the economic corridor, but the majority of them have concentrated on import–export operations involving sizable firms, which are not applicable to the transportation of agricultural products, particularly when attention is paid to small and medium-sized farmers. In this study, mixed-integer programming (MIP) is presented to design an agricultural product logistics network. In order to prolong the lifespan of the container used, the MIP’s primary goal is to maximize the total chain profit while maintaining the lowest container usage possible. The approach was developed to increase small and medium-sized farmers’ ability to compete. Small and medium-sized farmers bring their products to an agricultural product collecting center, also known as a container loading facility. After that, skilled logistics companies distribute the goods. In order to convey the goods to the final clients in neighboring nations, the proper locations of the containing loading centers, the correct transportation option and the borders must be decided. The issue was identified as multi-echelon location–allocation sizing (MELLS), an NP-hard problem that cannot be handled in an efficient manner. To solve a real-world problem, however, efficient techniques must be supplied. AMIS, an artificial multiple intelligence system, was created to address the suggested issue. AMIS was developed with the goal of leveraging a variety of methods for local search and development. There are several well-known heuristics techniques employed in the literature, including the genetic algorithm (GA) and the differential evolution algorithm (DE). With respect to the improved solutions obtained, the computational results show that AMIS exceeds the present heuristics, outperforming DE and GA by 9.34% and 10.95%, respectively. Additionally, the system’s farmers made a total of 15,236,832 THB in profit, with an average profit per container of 317,434 THB and an average profit per farmer of 92,344.44 THB per crop. The container loading center uses 48 containers, with a 5.33 container average per container loading center (CLC). The farmers’ annual revenues were previously less than 88,402 THB per family per year, so we can predict that the new network may increase customers’ annual income by 4.459% for each crop.