Optimizing Engineering Processes with Big Data and Machine Learning in Smart Engineering

Abstract

The integration of Big Data and Machine Learning (ML) in modern engineering presents transformative opportunities for enhancing operational efficiency, optimizing performance, and ensuring sustainability across various industries. As industries evolve towards more complex systems and higher demands, traditional engineering processes are often hindered by inefficiencies and high operational costs. This paper explores the intersection of Big Data and ML, highlighting their role in revolutionizing engineering practices through data-driven insights and intelligent decision-making. Big Data, characterized by its vast volume, variety, velocity, and veracity, is harnessed to optimize manufacturing, predictive maintenance, quality control, and real-time decision-making. ML, a subset of artificial intelligence, enables systems to learn from data, improving predictive capabilities and uncovering hidden patterns in large datasets. The convergence of these technologies gives rise to "Smart Engineering," offering significant potential for increased automation, enhanced decision-making, and improved sustainability across sectors such as manufacturing, energy, automotive, and civil engineering. However, challenges remain in terms of data management, infrastructure requirements, and workforce readiness, particularly for smaller organizations with limited resources. This systematic review synthesizes existing literature to examine the applications, integration, challenges, and future opportunities of Big Data and ML in engineering, providing insights that can guide industry stakeholders in navigating the complexities of implementing these technologies for optimized performance and sustainable growth.