Computational Engineering and Information Systems for Advanced Design and Manufacturing

Modern design and manufacturing face unprecedented challenges in managing complex product requirements, integrating diverse simulation tools, and handling massive datasets while maintaining competitive time-to-market pressures. This research addresses the critical need for comprehensive computational engineering systems that seamlessly integrate computer-aided design (CAD), simulation, optimization, and data analytics to enhance product development efficiency. The core technical innovation lies in developing an integrated design environment that combines parametric modeling with computer-aided engineering (CAE) for multi-physics simulation, computer-aided manufacturing (CAM) for production planning, and product lifecycle management (PLM) for data coordination. The methodology employs advanced optimization techniques including topology optimization for structural design, shape optimization with morphing techniques, and multi-objective optimization with Pareto analysis, while integrating machine learning applications for predictive modeling, neural networks for complex relationship modeling, and deep learning for advanced pattern recognition. This comprehensive framework enables automated feature recognition, process selection optimization, digital factory modeling, and real-time monitoring systems that bridge the gap between design creativity and manufacturing reality.

The system delivers transformative benefits across aerospace and automotive industries, where complex product development requires sophisticated integration of design, analysis, and manufacturing processes. Aerospace manufacturers implementing this technology experience enhanced aircraft component design and optimization, improved structural analysis with weight optimization, and streamlined manufacturing process planning that significantly reduces development cycles and improves product performance. Automotive companies benefit from advanced vehicle component optimization, crashworthiness analysis capabilities, and integrated supply chain coordination that dramatically improves quality while reducing costs and time-to-market. The technology enables comprehensive enterprise integration connecting ERP, CRM, and quality management systems while providing robust data analytics, workflow automation, and knowledge management capabilities that enhance innovation and competitive advantage. Carnegie Mellon’s research team seeks partnerships with engineering companies and technology providers interested in implementing next-generation computational engineering systems that combine advanced simulation, optimization, and data analytics to revolutionize product development and manufacturing excellence.

For complete technical details and experimental results, please refer to the original publication: 20-asme-jcise-yamakawa.pdf