E. Jo, L. Liu, N. Garg, U. Vaidya, S. Chakraborty, F. Ju, S. Kim
Oak Ridge National Laboratory,
Keywords: additive manufacturing, polymer composites, layer time, optimization
Summary:In large-scale additive manufacturing (AM), achieving high product quality and production efficiency has highly relied on the skills and experience of machine operators. However, the lack of guidelines based on accurate data and systematic analyses has led to suboptimal performance. One crucial factor that affects product quality and production efficiency is layer deposition time (a.k.a. layer time). Determining a proper layer time requires a high-fidelity model, which is computationally expensive and not suitable for online feedback systems where fast temperature prediction is necessary. To address this challenge, we propose a fast layer time optimization framework that uses a reduced physics-based one-dimensional heat transfer model to predict the cooling behavior and layer temperature. It can predict temperature history in a few seconds. However, with assumptions of the one-dimensional reduced model, it can be applied just to the continuous wall print. So, we develop a hybrid temperature prediction model that reflects geometric effects, by adjusting the temperature from the reduced model with variances calculated based on a high-fidelity three-dimensional finite element analysis. We use the predicted temperature data in an optimization model that monitors the temperature of multiple positions and balances the relationship between the layer time and the layer temperature. To determine the optimal layer time, we develop an iteration-based solution approach by combining the layer time optimization model with the hybrid model. The approach involves iterations between the proposed layer time from the optimization model and the temperature predicted from the hybrid model until the predicted temperature converges to a target layer temperature. We apply the developed process to two cases with different printing geometries: hexagon and star shapes. The results demonstrate that our simplified and lower-cost methodology can determine an optimal layer time in the large-scale AM process. In addition, our approach provides insight into the underlying automated control of the AM process, which can be useful in improving the efficiency of the processes. Overall, our proposed framework offers a novel and practical solution for layer time optimization in large-scale AM, which can significantly reduce the dependence on human skills and experience and improve the efficiency and quality of the manufacturing process. This poster provides valuable information for researchers and practitioners in the AM industry who seek to optimize their operations using advanced optimization techniques.