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Pol Vanwersch
Department of Mechanical Engineering
KU Leuven, Belgium
Pol Vanwersch obtained his Master’s degree in Electromechanical Engineering Technology from KU Leuven and UHasselt in 2020. He is a FWO-SB PhD fellow at the Department of Mechanical Engineering at KU Leuven, Belgium. His doctoral research focuses on laser micromachining and injection moulding of microfeatures. More specifically, he specializes in the manufacturing of thermoplastic hollow microneedles with femtosecond laser ablation and injection molding.
Femtosecond Laser Machining and Injection Molding Simulations for the Production of Thermoplastic Hollow Microneedles
This presentation discusses the fabrication of thermoplastic hollow microneedles (HMNs) using femtosecond (fs) laser machining and injection molding simulations. HMNs offer a minimally invasive alternative to traditional needles, with applications in drug delivery and diagnostics. The focus is on addressing key manufacturing challenges, including sharpness, replication fidelity, and scalability. A predictive fs laser ablation model for metals is developed and validated in three steps, accurately predicting HMN cavity shape, size, and microtextures with over 90% accuracy. Injection molding simulations are specifically tailored for high-accuracy replication of micro-features. By tuning boundary conditions and incorporating entrapped air correction, the prediction accuracy of needle height reaches up to 97%. Integrating these two simulation tools reduces HMN development time by 70% and significantly lowers equipment requirements, streamlining production and facilitating broader adoption of HMNs in biomedical applications.
Suraj Kumar
Department of Mechanical Engineering
Indian Institute of Technology Bombay, India
Suraj Kumar is a Ph.D. student in the Machine Tools Lab at the Department of Mechanical Engineering, IIT Bombay, India. His research focuses on real-time chatter detection and control in high-speed micromilling, with the goal of developing a fully automated micromilling machine capable of detecting and mitigating chatter in real time. He holds an M.S. degree in Mechanical Engineering from Ming Chi University of Technology, Taiwan, where he specialized in the numerical thermal analysis of silicone rubber induction molds, designing and optimizing cartridge heater layouts to achieve precise temperature profiles. At IIT Bombay, his work also includes the development of smart micromilling machines equipped with advanced signal processing techniques for real-time chatter control. Additionally, his research emphasizes understanding the physics of chatter frequency patterns and their correlation with machined surface topography to create a robust detection and control method.
Chatter Onset Detection using Energy and Spectral Entropy in High-Speed Micromilling
Micromilling offers intricate microfeatures on various materials with high efficiency and lower costs compared to other methods like lithography or laser. However, the lower flexural stiffness of the microendmills can lead to chatter and tool failure during machining, affecting surface quality and tool life. Chatter is related to the natural frequency of the machine tool structure, and it can be detected either using offline or online (real-time) methods. Offline chatter detection is time-consuming and needs a subject expert to perform the modal analysis and correctly draw the stability lobe diagram (SLD). This study introduces a methodology for detecting the onset of chatter in real-time via a custom feature called normalized spectral entropy, which is the ratio of spectral entropy under chatter and stable conditions. The feature sensitivity is enhanced by employing a high-pass Butterworth filter, eliminating the known frequencies, namely spindle and tooth passing frequencies. Using this approach, it has been observed that chatter occurs around when the normalized spectral entropy falls below 0.95. The chatter onset position is validated by the Fast Fourier Transform (FFT) of the accelerometer data and machined surface profile data. This proposed method of real-time chatter detection works well with different machining conditions. The validation has been done using machine surface profile data at three different rotational speeds of 20000, 40000, and 60000 rpm. This real- time chatter detection method can significantly improve micromilling by increasing precision and reducing downtime, leading to more efficient and reliable micro-manufacturing.
