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Rejecting fast narrow-band disturbances with slow sensor feedback for quality beam steering in selective laser sintering

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Abstract

A fundamental problem arises in feedback control when the system is subject to fast disturbances but can only get slowly updated sensor feedback. The problem is particularly challenging when the disturbances have frequency components near or beyond the sensor's Nyquist sampling frequency. Such difficulties occur to selective laser sintering, an additive manufacturing process that employs galvo scanners to steer high-power laser beams and relies on non-contact, slow sensing such as visual feedback to enhance the product quality. In pursuit of addressing the fundamental challenge in quality control under slow sensor feedback, this paper introduces a multi-rate control scheme to compensate beyond-Nyquist disturbances with application to selective laser sintering. This is achieved by designing a special bandpass filter with tailored frequency response beyond the slow Nyquist frequency of the sensor, along with integrating model-based predictor that reconstructs signals from limited sensor data. Verification of the algorithm is conducted by both simulation and experimentation on a galvo scanner that directs the energy beam in the additive manufacturing process.
... For example, following a repetitive trajectory, the galvo scanner used in SLS directs a high-energy laser beam onto the surface of a powder bed to form a crosssection layer of a part. The laser-material interaction here happens in a short time scale (the laser beam moves at meters per second on the powder bed), and the scanning accuracy relates directly to the part quality [21]. In practice, however, various disturbance sources exist in the optical path, including, for example, mechanical vibrations of the platform and thermal fluctuations due to smoke and circulation of the inert gas in the process chamber. ...
... loop, and Q(z), the disturbance compensating filter. With the disturbance frequency information known, one can design Q(z) with the procedure provided in [21]. Our model-based information recovery (the MR block in Fig. 9) is applied to recover a fast disturbance estimated[n] using sparsely sampled disturbance estimatesd M [n] andd N [n]. ...
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