Application of Big Data Analysis in Intelligent Industrial Design Using Scalable Computational Model

Smart industrial design’s incorporation of big data analytics is reshaping the manufacturing industry by boosting product innovation, optimizing design processes, and increasing overall efficiency. Massive amounts of data may be processed using scalable computing, leading to crucial insights that propel more informed, data-driven design choices. Integrating advanced analytics into current design workflows, dealing with diverse and large-volume data, and guaranteeing data quality and integrity are all obstacles to implementing extensive data analysis in industrial design. Many challenges must be solved, including keeping data secure and meeting the computational needs of real-time processing data. Intelligent industrial design benefits significantly from scalable computing’s extensive data analysis capabilities, which allow systems to analyze huge quantities of data in real time. Its dynamic resource allocation achieves efficient resource utilization and optimum performance, guaranteeing that processing power scales with the demand. This research suggests an Integrated Concentric Framework for Intelligent Industrial Design (ICF-IID) that applies big data analysis using scalable computational resources. The framework analyses big datasets from different parts of the design and production process using powerful visualization tools, machine learning algorithms, and predictive analytics. Adaptive algorithms developed for unique demands in industrial design, strong data management protocols, and a distributed computing architecture for efficient data processing are essential components. The framework is useful for predictive maintenance, product lifecycle management, and design parameter optimization in industrial design. The framework may find design defects, predict equipment failures, and suggest improvements by analyzing historical and real-time data. The efficacy and scalability of the suggested framework are assessed through simulation analysis. These results show that it can efficiently and accurately process industrial data on a wide scale. Based on the results, the framework seems useful for making decisions in complicated design contexts and providing practical insights. The proposed method increases the efficiency ratio of 9.21%. accuracy ratio of 98.32%, product innovation ratio of 97.65%, scalability ratio of 97.41%, and optimized design process ratio of 96.21% compared to existing methods.