Two-dimensional packing algorithm for autoclave molding scheduling of aeronautical composite materials production

Xie, Naiming; Zheng, Shaoxiang; Wu, Qiao

doi:10.1016/j.cie.2020.106599

Cited by 12 publications

(1 citation statement)

References 35 publications

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“…However, the approach is not feasible to arrange the best location of parts because it has to compute all combinations of parts locations [9]. Another research direction is to model the parts allocation in the autoclave as the bin packing problem (BPP) where the goal is to put items in bins non-overlappingly [10]. To evaluate the effect of large molds and the various placements on heating performance, a computational fluid dynamics-based simulation model combined with response surface methodology is proposed [11].…”

Section: Related Workmentioning

confidence: 99%

Simulation and Modeling of a Data Twin Service for the Autoclave Curing Process

et al. 2022

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The production of composite material components is a high priority in the aerospace and defense industry. It is important to introduce Industry 4.0 related technologies into this field for the purpose of innovating the factory system. Among the processes of the composite materials production, autoclave curing is the key to yielding durable and sustainable structures. However, the settings of the autoclave curing process still heavily rely on the experienced operators in the current practices. In this paper, we adopt the concept of Data Twin Service to allow the interaction between human intelligence and artificial intelligence. The developed Data Twin Service for the autoclave curing process enables the operators to simulate the placement of molds and learns to predict the curing time. Our service is a GUI application which employs a human-in-the-loop design approach and helps users perceive the simulation process intuitively. We propose a two-stage machine learning model to learn the curing time according to the parts and placement. The empirical study is conducted on the one-year historical data and has proven the practical feasibility of the proposed approach. Moreover, the service is currently being used in the manufacturing site and obtains satisfactory performance.

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Section: Related Workmentioning

confidence: 99%