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Browsing Staff publications (MMD) by Publisher "Springer"

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    Cyclic thermal treatment parameters of bagasse particle reinforced epoxy bio-composites for sustainable applications
    (Springer, 2025-03-13) Oladele, Isiaka Oluwole; Falana, Samuel Olumide; Ilesanmi; Akinbamiyorin, Michael; Onuh, Linus Nnabuike; Taiwo, Anuoluwapo Samuel; Adelani, Samson Oluwagbenga; Olajesu, Olanrewaju Favor
    The demand for sustainable, high-performance materials has led to increased interest in bio-based composites. However, optimizing the mechanical properties of such materials for engineering applications remains a challenge. This study addresses this gap by developing and characterizing an epoxy-based biocomposite reinforced with sugarcane bagasse particles, focusing on the influence of cyclic thermal treatment on its properties. The bagasse particles were chemically treated with 1 M NaOH to remove impurities, improve interfacial bonding with the epoxy matrix, and enhance the overall composite performance. The treated particles j were pulverized to 470 µm and incorporated into the epoxy matrix (0–20 wt%) using the hand layup method. The composites were divided into untreated and thermally treated groups, with the latter subjected to cyclic thermal treatment (100 °C for 3 h over 7 days). Mechanical, wear, and water absorption properties were evaluated, while fractured surface morphologies were analyzed using SEM. Results revealed that cyclic thermal treatment significantly enhanced the composites’ performance, with the 15 wt% heat-treated composite showing optimal properties: density of 1.102 g/cm3, flexural strength of 29.13 MPa, ultimate tensile strength of 103.50 MPa, impact strength of 3.49 kJ/m2, hardness of 64.70 HS, and wear indices of 0.034 mg. These findings demonstrate that alkali treatment and cyclic thermal treatment synergistically enhance the performance of bio-composites, making them suitable for diverse applications, including automotive, aerospace, and other engineering fields.
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    Impact of cold-wire gas metal arc welding (CW-GMAW) parameters on microstructure and microhardness characteristics in repairing S275JR structural steel
    (Springer, 2025-03-23) Musa, Zahraddeen; Ganguly, Supriyo; Suder, Wojciech; Igwemezie, Victor; Rajamudili, Kuladeep
    This study investigates the influence of adding a cold wire during gas metal arc welding (CW-GMAW) for repair of S275JR structural steel. The research is aimed at improving repair productivity through increased deposition rates with enhanced performance. During weld repair, multiple passes induce large number of thermal cycles and a huge thermal gradient on the material which has an adverse effect on the material’s properties. This is largely due to the microstructural changes that occur during the process. In this work, a systematic approach has been adopted to explore the effects of varying gas metal arc welding (GMAW) parameters, including wire feed rate, welding current, voltage, travel speed, and specifically cold-wire feed speed on the heat affected zone (HAZ) microstructure and hardness. Macrostructural examination highlights significant alterations in the heat affected zone (HAZ) region, with marked microhardness changes in both WM and HAZ. Cold-wire addition led to a reduction in the HAZ area, depth of weld metal penetration, and significantly reduced the impact of imposing thermal cycles on the HAZ of the welded samples. Additionally, microstructural analysis was conducted using a standard optical microscope to correlate the observed hardness variations with microstructural transformations in the weld metal and heat affected zone (HAZ). The findings reveal that specific combinations of CW-GMAW parameters can significantly influence the microstructure and thereby hardness, suggesting that with careful control of these parameters, it would be possible to do faster repair with minimal loss of integrity for critical structural steels.
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    Mapping research frontiers in gender and sustainability in agricultural development: a bibliometric review
    (Springer, 2025-01-01) Kumari, Anshu; Tiwari, Manish; Mor, Rahul; Jagtap, Sandeep
    Gender and sustainability are crucial in agriculture, which remains a significant source of global employment. However, urbanization, industrialization, and technological advancements have reshaped the sector, impacting labor dynamics and gender roles. Traditional agricultural labor faces challenges due to low wages, physically demanding tasks, and unfavorable working conditions. Addressing gender disparities and promoting inclusive work environments is essential for achieving sustainability. According to the ILO (International Labour Office) decent work encompasses productivity and equal employment opportunities for both genders. This study aims to review the literature on gender, sustainability and agricultural development using a bibliometric analysis of Scopus-indexed articles. The findings identify five main research domains: gender dynamics and roles, agriculture and climate change, sustainability and development, human and labor dynamics, and environmental and technological aspects. Additionally, four key scientific communities led the research: Gender studies, agricultural economics, environmental management, and rural sociology. Emerging research trends focus on gender roles in sustainable farming, environmental innovation, and labor governance in agriculture. Spain, the United Kingdom, United States, and Canada lead in knowledge production, contributing significantly to these research domains. This review highlights the importance of interdisciplinary approaches to address the complex issues of gender and sustainability in agriculture. It also specifies a target for expectations research, highlighting that the ILO’s definition of appropriate employment can guide efforts to improve gender equity and labor conditions, ultimately supporting sustainable development in the agricultural sector.
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    Optimizing industrial etching processes for PCB manufacturing: real-time temperature control using VGG-based transfer learning
    (Springer, 2025-04-01) Luo, Yang; Jagtap, Sandeep; Trollman, Hana; Garcia-Garcia, Guillermo; Liu, Xiaoyan; Abdul Majeed, Anwar P. P.
    Accurate temperature control in Printed Circuit Board (PCB) manufacturing is essential for maintaining high-quality etching results. Automated monitoring using machine vision and deep learning offers an effective approach for this task. This study investigated a feature-based transfer learning technique for classifying temperature readiness in infrared images of the etching process. The captured dataset containing 470 ‘Production-Ready’ and 480 ‘Not-Ready’ infrared images of the etchant tank was utilized. Pre-trained Visual Geometry Group (VGG) Convolutional Neural Network (CNN) models, specifically VGG16 and VGG19, were employed to extract discriminative features from these images. Logistic Regression (LR) classifiers were then trained on these features to classify the infrared images. The performance of the VGG16-LR and VGG19-LR pipelines was evaluated on training, validation, and test sets using a 60:20:20 split. While both pipelines achieved 100% accuracy on the training sets, the VGG19 pipeline showed exceptional performance, achieving a validation accuracy of 95%, and a test accuracy of 99%. The VGG16 pipeline also demonstrated robust performance, achieving 96% accuracy on both the validation and test sets. Considering the dimensions and the overall efficiency of the pipeline, it was determined that the VGG19-LR model was appropriate for the captured dataset. The high accuracy indicates that transfer learning is suitable for categorizing temperature fluctuation in infrared thermography, as opposed to training a deep neural network from scratch. Computer vision and deep learning provide automated and precise temperature management during the etching process, leading to enhanced efficiency in PCB manufacturing.