Multi-Objective Variable Neighborhood Strategy Adaptive Search for Tuning Optimal Parameters of SSM-ADC12 Aluminum Friction Stir Welding

Chainarong, Suppachai; Pitakaso, Rapeepan; Sirirak, Worapot; Srichok, Thanatkij; Khonjun, Surajet; Sethanan, Kanchana; Sangthean, Thai

doi:10.3390/jmmp5040123

Cited by 12 publications

(4 citation statements)

References 70 publications

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“…As can be seen from the solution in Table 22, the AMIS provided a higher value for ARP than the GA or DE, as suggested in Yang et al [4] and Pitakaso et al [61], because it employed a more efficient local search method. This conclusion is in line with that of [59,[70][71][72][73][74][75], which apply various local search techniques, such as the best transition methods, scaling factors and random transition methods, to solve various types of problems. The outcome demonstrates that using a more effective algorithm yields better results for the target problems.…”

Section: Amis-in Amis-at Amissupporting

confidence: 81%

“…The Pareto front's probable locations were whittled down using TOPSIS in order to select the most advantageous spot. The use of TOPSIS in conjunction with the AMIS has been recommended by Chainarong et al [74,75] as a method for choosing the most promising option. From the network design of the case study, we can conclude that the total profit of the system is 15,236,832 THB, which can be interpreted as an average profit per container of 317,434 THB.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Amis-in Amis-at Amissupporting

confidence: 81%

Section: Conclusion and Future Outlookmentioning

confidence: 99%

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

et al. 2022

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“…This study contributes significantly to the advancement of leaf abnormality detection in agriculture. Through the introduction of new datasets and an extensive evaluation of various CNN architectures [43,[59][60][61], our research expands the scope of leaf disease research, particularly for C. asiatica leaves. The proposed ensemble deep learning model, coupled with innovative image segmentation and decision fusion strategies, presents a novel and effective approach to address the complexities of leaf abnormality classification.…”

Section: Enhancing Leaf Abnormality Detection In C Asiatica: An Ensem...mentioning

confidence: 99%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

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Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.

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“…It was produced to respond the needs of the automotive industry. Especially, SSC-ADC12 aluminum alloy is used to produce engine parts, door parts, and etc., [12]. However, the study of welding of semi-solid materials is still in the great interest due to the changing shape of the microstructure from the base structure [13].…”

Section: Introductionmentioning

confidence: 99%

The Liquid Fraction of ZA27 Zinc Alloy from TLP Diffusion Bonding Affecting Mechanical Properties and Microstructural Characterizations of SSC-ADC12 Aluminum Alloy

Meengam,

Dunyakul,

Maunkhaw

2023

KEM

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This work investigated liquid fraction in ZA27 zinc alloy interlayered with SSC-ADC12 aluminum alloy workpieces for Transient Liquid Phase (TLP) diffusion bonding. The results clearly indicated that liquid fraction had a necessary influence on TLP diffusion bonding. In other words, the high liquid fraction and bonding time tends to produce excellent bond strength. The maximum bond strength at 27.21 MPa was from 100% liquid fraction and 90 min from bonding time. The hardness increased by approximately 23.36% comparing to SSC-ADC12 aluminum alloy and by 11.18% comparing to the ZA27 zinc alloy. The microstructure was homogeneous in the bond line and formed to MgZn2 and CuZn4 intermetallic compound under Scanning Electron Microscope. According to Energy Dispersive X-Ray Spectrometer analysis, Zn atoms had the ability to move about 4.381 mm from the bond line and the elements' uniform distribution.

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Multi-Objective Variable Neighborhood Strategy Adaptive Search for Tuning Optimal Parameters of SSM-ADC12 Aluminum Friction Stir Welding

Cited by 12 publications

References 70 publications

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

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

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

The Liquid Fraction of ZA27 Zinc Alloy from TLP Diffusion Bonding Affecting Mechanical Properties and Microstructural Characterizations of SSC-ADC12 Aluminum Alloy

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