Nonlinear Waveform Optimization for Enhanced Ink Droplet Formation in Material Jetting
Qintao Shen, Li Zhang, Renquan Ji, Viboon Saetang, Huan Qi
TL;DR
This paper introduces a new method to improve droplet formation in material jetting by optimizing the driving waveform using machine learning and optimization techniques.
Contribution
A novel CNN-PSO optimization method is proposed to suppress pressure oscillations and improve droplet formation in material jetting.
Findings
The optimized waveform reduces pressure fluctuation convergence time by approximately 32.19%.
The method significantly improves droplet formation quality and stability.
Simulation and experimental results validate the effectiveness of the proposed optimization approach.
Abstract
Material jetting, as a critical additive manufacturing technology, relies on precise control of the driving waveform to achieve high-quality droplet formation. During the droplet ejection process, pressure fluctuation at the nozzle outlet plays a significant role in droplet formation. Traditional experimental methods for optimizing the driving waveform often struggle to address the complex nonlinearities inherent in the jetting process. In this study, a numerical simulation model of the droplet ejection process is established to elucidate the influence mechanism of nozzle outlet pressure oscillations on droplet formation. A novel optimization method combining Convolutional Neural Networks (CNNs) and Particle Swarm Optimization (PSO) is proposed, targeting the suppression of residual pressure oscillations and achieving the desired pressure fluctuation. The method leverages nonlinear…
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Taxonomy
TopicsElectrohydrodynamics and Fluid Dynamics · Additive Manufacturing and 3D Printing Technologies · Fluid Dynamics and Heat Transfer