Daily Archives: May 7, 2026

Authors: Mahantesh M. Ganganallimath, Dr. K. Vizayakumar, Dr. Umesh M. Bhushi

Abstract: By providing cast components to the automobile, aerospace, railroad, construction, defence, and heavy engineering industries, the Indian foundry sector is essential to the manufacturing sector. Casting flaws, process unpredictability, material waste, high rejection rates, energy inefficiency, and growing international competitiveness are some of the industry's major obstacles. In this regard, sustainable industrial growth now depends on quality assurance and quality-centric methods. The necessity of methodical quality assurance procedures, process control systems, and continuous improvement techniques in Indian foundries is examined in this study. The study highlights that quality-driven systems enhance customer satisfaction and product dependability while simultaneously lowering costs and promoting long-term competitiveness and environmental sustainability. The combination of Industry 4.0, automation, and statistical quality tools for stable growth is further supported by recent research on KPI-driven foundry quality systems and sustainable control models. An important part of the manufacturing sector, the Indian foundry industry greatly boosts employment and economic growth. This study looks into how top management influences quality management procedures in Indian foundries of different sizes. The study examines implementation difficulties, strategic quality efforts, and leadership commitment at various operational scales. The results show that whereas major foundries use organized quality management systems, small foundries encounter obstacles because of limited resources, ignorance, and opposition to change. The report suggests a framework to improve quality performance in the Indian foundry industry and emphasizes the necessity of a leadership-driven quality culture.

DOI: https://doi.org/10.5281/zenodo.20060718

Published by: vikaspatanker

Authors: Piyush Dewangan, Shivam Vishwakarma, Nikhil Yadav, Prahlad Yadav, Himanshu Mokashe, Deepak Sahu

Abstract: Global climate change poses severe threats to agricultural and forested ecosystems that underpin terrestrial carbon balance, biodiversity, and food security. This paper presents a comprehensive investigation into how Artificial Intelligence (AI)—encompassing machine learning, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and generative adversarial networks (GANs)—is transforming climate change responses across agriculture and forestry. Drawing on peer-reviewed literature and documented case studies, we examine AI applications including precision irrigation, crop disease detection, yield forecasting, satellite-based deforestation monitoring, wildfire risk prediction, acoustic biodiversity surveillance, and hydrological flood modeling. A three-tiered analytical framework maps causal pathways from technological deployment to environmental, economic, and social outcomes, while critically addressing structural barriers including data scarcity, algorithmic bias, computational inequity, and governance deficits. Principal findings confirm that AI delivers measurable gains in climate mitigation and adaptation efficiency; however, transformative societal potential remains contingent on equitable data access, open-source computational infrastructure, and coherent multilateral policy frameworks.

DOI: https://doi.org/10.5281/zenodo.20060622

Published by: vikaspatanker