The optimal design of products is crucial in the manufacturing industry due to its significant impact on economic performance, resource efficiency, and environmental sustainability. This study addresses a critical challenge in tire manufacturing: determining optimal extrusion initializations to improve production efficiency and product quality. To tackle this problem, the research explores the use of Digital Twins, a technology that facilitates the simulation of various scenarios without working directly with the physical system. Specifically, a data-driven Digital Twin approach is employed, which has shown promising computational efficiency in similar contexts. Despite its potential, several challenges associated with Digital Twins have been identified in the literature, including issues related to human–machine interaction, model adaptability to dynamic changes, data quality, and overall precision. This study advances Digital Twin technology by developing a framework that enables real-time identification and control of crucial physical parameters. The proposed approach emphasizes real-time computation, seamless process integration, adaptability to production demands, and user-friendly interaction for operators. This approach, according to our results, could avoid the 99% failed extrusions and could decrease 82% the time required to stabilize product quality. These outcomes demonstrate the practicality and effectiveness of the Digital Twin in improving manufacturing operations. The study also underscores the importance of incorporating human-centric design principles, ensuring that the technology not only enhances operational efficiency but also aligns with the needs of the operators. Overall, the research highlights the significant advancements and practical benefits of applying Digital Twin technology in tire manufacturing.

This paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is.

Climate change, resource depletion, and worldwide urbanization feed the demand for more energy and resource-efficient buildings. Increasingly, architectural designers and consultants analyze building designs with easy-to-use simulation tools. To identify design alternatives with good performance, designers often turn to optimization methods. Randomized, metaheuristic methods such as genetic algorithms are popular in the architectural design field. However, are metaheuristics the best approach for architectural design problems that often are complex and ill defined? Metaheuristics may find solutions for well-defined problems, but they do not contribute to a better understanding of a complex design problem. This paper proposes surrogate-based optimization as a method that promotes understanding of the design problem. The surrogate method interpolates a mathematical model from data that relate design parameters to performance criteria. Designers can interact with this model to explore the approximate impact of changing design variables. We apply the radial basis function method, a specific type of surrogate model, to two architectural daylight optimization problems. These case studies, along with results from computational experiments, serve to discuss several advantages of surrogate models. First, surrogate models not only propose good solutions but also allow designers to address issues outside of the formulation of the optimization problem. Instead of accepting a solution presented by the optimization process, designers can improve their understanding of the design problem by interacting with the model. Second, a related advantage is that designers can quickly construct surrogate models from existing simulation results and other knowledge they might possess about the design problem. Designers can thus explore the impact of different evaluation criteria by constructing several models from the same set of data. They also can create models from approximate data and later refine them with more precise simulations. Third, surrogate-based methods typically find global optima orders of magnitude faster than genetic algorithms, especially when the evaluation of design variants requires time-intensive simulations.