Abstract
The manufacturing of composite structures is a highly complex task with inevitable risks, particularly associated with aleatoric and epistemic uncertainty of both the materials and processes, as well as the need for in-situ decision-making to mitigate defects during manufacturing. In the context of aerospace composites production in particular, there is a heightened impetus to address and reduce this risk. Current qualification and substantiation frameworks within the aerospace industry define tractable methods for risk reduction. In parallel, Industry 4.0 is an emerging set of technologies and tools that can enable better decision-making towards risk reduction, supported by data-driven models. It offers new paradigms for manufacturers, by virtue of enabling in-situ decisions for optimizing the process as a dynamic system. However, the static nature of current (pre-Industry 4.0) best-practice frameworks may be viewed as at odds with this emerging novel approach. In addition, many of the predictive tools leveraged in an Industry 4.0 system are black-box in nature, which presents other concerns of tractability, interpretability and ultimately risk. This article presents a perspective on the current state-of-the-art in the aerospace composites industry focusing on risk reduction in the autoclave processing, as an example system, while reviewing current trends and needs towards a Composites 4.0 future.
Highlights
Composite materials are already commonplace in many modern industries, including aerospace, automotive, sports goods, construction and many more [1]
Carlone et al 2018 showed that dynamic predictions of the process can be made using Recurrent Neural Networks (RNNs), using the Long-Short Term Memory (LSTM) architecture [55]
With the recent emergence of AI explainability tools such as Local Interpretable Model-Agnostic Explanations (LIME)-based toolboxes, or the Shapley value-based approach used by Fiddler Labs [43], there is an opportunity for further research in this field to address the above concerns, in both the construction and evaluation of models, and associated datasets for advanced manufacturing
Summary
Composite materials are already commonplace in many modern industries, including aerospace, automotive, sports goods, construction and many more [1]. The multi-physics nature of composites manufacturing, combined with the undelineated approach of forming the shape and material properties of a part at the same time during production [6], adds complexity to the task To address these challenges and associated risks, a combination of tools and heuristic-based policies within each factory can provide a framework to better understand these systems and embed confidence in the decision-making process. Within the current best practice, the combination of qualification frameworks, along with the statistically-derived design allowable, forms the static, closed-loop approach to address and minimize risk in this complex system, by simultaneously constraining the materials, processes and other pertinent features This restriction on the design space for the material, process, equipment, tooling, consumables and other elements of the factory process, works to reduce variability and uncertainty. This is the case when the given actual system is highly complex and direct computational models (e.g. using finite element) may be highly complex, time consuming, or low in their fidelity for use in practice
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