Surrogate-based optimization for minimizing injection-molding cycle times
An innovative surrogate-based optimization algorithm, combining machine learning and metaheuristic optimization, aims to minimize injection-molding cycle times. It leverages smaller sampling plans and demonstrates superior accuracy and efficiency over traditional methods.
Overview
Surrogate-based optimization presents a cutting-edge approach to minimizing injection-molding cycle times by integrating machine learning with metaheuristic optimization. This method is designed to determine optimal machine-parameter values efficiently, even when constrained by upper and lower limits. With a focus on optimizing parameters based on post-consumer recycled (PCR) properties and percentages, this technique is expected to enhance manufacturing efficiency significantly.
Technical specifications
Key features:
- Combines machine learning with metaheuristic optimization for process improvement.
- Utilizes a smaller sampling plan than traditional design-of-experiments, increasing efficiency.
- Demonstrates higher accuracy and speed compared to Bayesian optimization.
- Initially validated in energy retrofitting, machining parameters, and queuing system design.
- Future validation includes iterative testing for various PCR properties and percentages.
Technology readiness level
This technology is at TRL 6, indicating that it has been demonstrated in relevant environments. The next steps involve testing across diverse PCR properties and process variables to refine the algorithm further. Collaborative efforts with THWS University underscore its potential for optimization in manufacturing processes.
About Tecnológico de Monterrey
Tecnológico de Monterrey is a leading private, multi-campus research university in Mexico, recognized for entrepreneurship and industry collaboration. Its Monterrey headquarters anchors an urban innovation district with open lab space, prototyping, and shared testing facilities that welcome corporate collaborators. An integrated health system supports clinical research and translation, while structured internships and challenge-driven partnerships connect companies with faculty and student talent year-round. Research is supported by competitive federal funding through Mexico’s national science and technology council, along with industry contracts and international sponsors. A dedicated technology transfer office manages IP, licensing, and startup formation.
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