Multi-scale Reliability-Based Design Optimisation Framework for Fibre-Reinforced Composite Laminates

Acknowledgements This work was supported by the University of Aberdeen Elphinstone scholarship scheme.Peer reviewedPostprin

Enhancing cutting tool sustainability based on remaining useful life prediction

As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk.©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization