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Your search keyword '"Mao, Huachao"' showing total 45 results
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45 results on '"Mao, Huachao"'

4. 3D Printing of Optical Lenses Assisted by Precision Spin Coating.

Author

Shan, Yujie, Hua, Junyu, and Mao, Huachao

Subjects
COATING processes, SPIN coating, THREE-dimensional printing, SURFACE roughness, SURFACE defects
Abstract

Though 3D printing shows potential in fabricating complex optical components rapidly, its poor surface quality and dimensional accuracy render it unqualified for industrial optics applications. The layer steps in the building direction and the pixelated steps on each layer's contour result in inevitable microscale defects on the 3D‐printed surface, far away from the nanoscale roughness required for optics. This paper reports a customized vat photopolymerization‐based lens printing process, integrating unfocused image projection and precision spin coating to solve lateral and vertical stair‐stepping defects. A precision aspherical lens with less than 1 nm surface roughness and 1 µm profile accuracy is demonstrated. The 3D‐printed convex lens achieves a maximum MTF resolution of 347.7 lp mm−1. A mathematical model is established to predict and control the spin coating process on 3D‐printed surfaces precisely. Leveraging this low‐cost yet highly robust and repeatable 3D printing process, the precision fabrication of multi‐scale spherical, aspherical, and axicon lenses are showcased with sizes ranging from 3 to 70 mm using high clear photocuring resins. Additionally, molds are also printed to form multi‐scale PDMS‐based lenses. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Balancing and sequencing of stochastic mixed-model assembly U-lines to minimise the expectation of work overload time.

Author

Dong, Jietao, Zhang, Linxuan, Xiao, Tianyuan, and Mao, Huachao

Subjects
SIMULATED annealing, FLEXIBLE manufacturing systems, ASSEMBLY line methods, STOCHASTIC programming, LINEAR programming, STOCHASTIC processes
Abstract

A mixed-model assembly U-line is a flexible production system capable of manufacturing a variety of similar models, and it has become popular as an important component of the just-in-time production system. However, it poses new challenges for the optimal design of assembly lines because both the task assignment and the production sequence affect the workload variance among workstations. As a consequence, this paper addresses the line balancing problem and the model sequencing problem jointly and proposes a 0–1 stochastic programming model. In this model, task times are assumed to be stochastic variables independently distributed with normal distributions and the objective is to minimise the expectation of work overload time for a given combination of cycle time and number of workstations. To solve the problem, a simulated annealing-based algorithm is developed, which can also be used to minimise the absolute deviation of workloads in a deterministic environment. The experimental results for a set of benchmark problems show that the proposed algorithm outperforms the existing algorithms in terms of solution quality and running time. [ABSTRACT FROM PUBLISHER]

Published
2014
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