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Composites design - A229

From CKN Knowledge in Practice Centre

Overview[edit | edit source]

Being relatively new materials, the manufacturing maturity of composites is not as high as it is for traditional materials such as metals or ceramics. In order to reduce the risk associated with composites manufacturing, a structured approach should be taken towards the development of best practice. Manufacturing practice is driven by the need to achieve a desired manufacturing quality. Achieving this quality is ideally driven by the relevant manufacturing science, at least where it exists. So, on one side, there exists the knowledge (science) base which defines 'why' the quality is as it is. On the other side, there is the practice of 'how' best to implement manufacturing processes to affect the quality in a given way. The interconnectivity and understanding of these two elements are important and form the basis of knowledge-in-practice thinking[1].

Current and future practice[edit | edit source]

Current typical practice in developing aerospace composite structural products is to scale up the product design in size and complexity[2]. In assessing performance, a number of increasingly larger and complex tests and analyses are undertaken. These occur first on lab-scale specimens, known as coupons, and continue through to sub-components, then components, all the way to the full structure. Each successive layer represents a more complex configuration and builds on knowledge gained at the previous scale. This is known as the Building Block approach. The approach is used to ensure requirements/specifications are met while at the same time reducing risk by demonstrating material equivalency between successive layers. That is, at each scale, the part behavior can be described or predicted from the lower scale. Simply put, the materials and structures on the aircraft behave as expected from the lab-scale tests. This can be sub-divided into chemical, physical, and mechanical perspectives: for example, respectively is the degree of cure the same, is the volume fraction the same, is the compressive strength the same. As large scale testing is particularly expensive, the idea is to minimize the number of large scale tests by increasing the number of cheaper, coupon-level tests. If material equivalency can be demonstrated, then coupon tests are invaluable in representing the response of larger-scale structures.

Although less regulated industries may not require such an approach, the Building Block approach is a very valuable framework to help guide manufacturing decisions.

Building block approach commonly applied to composites manufacturing in aerospace.
Material equivalency must be demonstrated for certification approval in the aerospace industry.

While the Building Block approach may be complex and involved, simulation is speeding up and improving the process. From a manufacturing perspective, as simulations improve, they can be used to accurately represent the physics of manufacturing processes. In doing so, more efficient tests can be designed and less testing may be needed to demonstrate material equivalency. Of course, models are only as good as their inputs. As such, it is important that rigorous development of such models are undertaken and validated against experimental data. Development of physics-based simulations for polymer composites processing is a significant area of research; the models are which are currently implemented in many advanced composite manufacturing factories today. In addition, digitalization of factories, for the purpose of gathering large datasets, is on the rise.

Many experts believe that digitalization, AI, big data, simulation, and other computing advances will revolutionize manufacturing knowledge and practice. Their prevalence will become increasingly commonplace within the field of composites engineering as the world transitions towards Industry 4.0.

Know-how vs. know why[edit | edit source]

Historically, composites manufacturing has followed a trial and error approach, wherein best practice was based on experience and expert opinion. This is representative of 'know-how' knowledge. Practitioners would know how to implement a manufacturing process or even the results of that process, but they would not necessarily understand the science behind the process or the response of the material. While experienced-based decision making is valuable, it also less efficient and presents risk, as small changes to the process may unexpectedly and significantly impact the part quality. The opposite end of the spectrum is a completely science-based approach, wherein the physics of the entire process is captured and understood. This is representative of 'know-why' knowledge.

Intuitively, it makes sense to progress towards a 'know-why' approach. However, this is not without its own inefficiencies and risk. It can be computationally expensive to run in-depth processing models and smaller companies may not have the means to do so. Moreover, companies producing low risk, non-structural components, may not need or want to understand the minutiae of each process. Additionally, many of the mechanisms in composites processing are still not well understood and require the knowledge of experienced practitioners to implement good design/manufacturing choices. Therefore, it is a combination of 'know-how' and 'know-why' that represent the best option.

It is important to understand how 'know-how' and 'know-why' knowledge implementation relate to current and future best practice. It is pertinent to capture and protect knowledge gained through experience in order to understand current practice for manufacturing decision making and risk management. Only then can advancements be made to define better future practice using a 'know-why' approach. In doing this, it is necessary that disruptions to current practice occur either by opening up, reevaluating, and changing current manufacturing specifications or by enforcing them based on the manufacturing science. This disruption defines the future best practice. The protect-advance-disrupt workflow demonstrate the transformative steps needed to bring composites manufacturing from a low modularity and low maturity technology to a high modularity and high maturity technology[1].

Knowledge-in-Practice. From[1]

Design for manufacturability[edit | edit source]

One of the most important aspects of engineering is managing and reducing risk and uncertainty. The link between engineering design and manufacturing engineering can present high risk if not considered early in the design process. By the end of the conceptual design phase, it is often that while little of the actual program cost has been spent, as much as 70% has been committed[3][4]. This can lead to massive cost overruns later if producibilty was not considered early on, resulting in the part not meeting specifications. Modeling and simulation tools can help reduce this risk, by performing conceptual manufacturing analyses to determine part producibility.

Program life cycle cost (LCC) commitment in complex engineering systems. Adapted from[1] [3]

Scalability[edit | edit source]

Another risk associated with composites manufacturing is scalability. While an engineer may consider the issue of size scaling - that is, the properties of the part may vary depending on the size of the structure - production scaling is less intuitive. The factory must be able to handle the throughput required for manufacturing. This involves proper implementation of factory cells and ensuring efficiency and safety (both part and human safety) are maintained between each cell. Moreover, while the analysis of size scaling is more grounded in science, testing, and process trials over time, production scaling is not validated until a part completes its entire life cycle in the factory. At this point, success or failure of the part meeting the manufacturing requirements is translated to either a profit or a loss[5]. As such, both size and production scaling should be considered together, early on.




References

  1. 1.01.11.21.3 [Ref] Fabris, Janna Noemi (2018). A Framework for Formalizing Science Based Composites Manufacturing Practice (Thesis). The University of British Columbia, Vancouver. doi:10.14288/1.0372787.CS1 maint: uses authors parameter (link)
  2. [Ref] Composite Materials Handbook 17 - Polymer Matrix Composites; Materials Usage, Design and Analysis. 3. SAE International on behalf of CMH-17, a division of Wichita State University. 2012. ISBN 978-1-68015-454-2.CS1 maint: date and year (link)
  3. 3.03.1 [Ref] Sanders, Al et al. (2011). "21st Century Manufacturing Modeling and Simulation Research and Investment Needs" (PDF). National Defense Industrial Association (NDIA) Manufacturing Division.CS1 maint: extra punctuation (link) CS1 maint: uses authors parameter (link) CS1 maint: date and year (link)
  4. [Ref] Phillips, F Y; Srivastava, R (1993). "Committed Costs vs. Uncertainty in New Product Development. Working Paper WP-1993-02-01". Cite journal requires |journal= (help)CS1 maint: uses authors parameter (link)
  5. [Ref] Ilcewicz, Larry B (1999). "Scaling crucial to integrated product development of composite aerospace structures. Part 1". 30 (3). doi:10.1016/s1359-835x(96)00116-9. Cite journal requires |journal= (help)CS1 maint: uses authors parameter (link)



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Welcome

Welcome to the CKN Knowledge in Practice Centre (KPC). The KPC is a resource for learning and applying scientific knowledge to the practice of composites manufacturing. As you navigate around the KPC, refer back to the information on this right-hand pane as a resource for understanding the intricacies of composites processing and why the KPC is laid out in the way that it is. The following video explains the KPC approach:

Understanding Composites Processing

The Knowledge in Practice Centre (KPC) is centered around a structured method of thinking about composite material manufacturing. From the top down, the heirarchy consists of:

The way that the material, shape, tooling & consumables and equipment (abbreviated as MSTE) interact with each other during a process step is critical to the outcome of the manufacturing step, and ultimately critical to the quality of the finished part. The interactions between MSTE during a process step can be numerous and complex, but the Knowledge in Practice Centre aims to make you aware of these interactions, understand how one parameter affects another, and understand how to analyze the problem using a systems based approach. Using this approach, the factory can then be developed with a complete understanding and control of all interactions.

The relationship between material, shape, tooling & consumables and equipment during a process step


Interrelationship of Function, Shape, Material & Process

Design for manufacturing is critical to ensuring the producibility of a part. Trouble arises when it is considered too late or not at all in the design process. Conversely, process design (controlling the interactions between shape, material, tooling & consumables and equipment to achieve a desired outcome) must always consider the shape and material of the part. Ashby has developed and popularized the approach linking design (function) to the choice of material and shape, which influence the process selected and vice versa, as shown below:

The relationship between function, material, shape and process


Within the Knowledge in Practice Centre the same methodology is applied but the process is more fully defined by also explicitly calling out the equipment and tooling & consumables. Note that in common usage, a process which consists of many steps can be arbitrarily defined by just one step, e.g. "spray-up". Though convenient, this can be misleading.

The relationship between function, material, shape and process consisting of Equipment and Tooling and consumables


Workflows

The KPC's Practice and Case Study volumes consist of three types of workflows:

  • Development - Analyzing the interactions between MSTE in the process steps to make decisions on processing parameters and understanding how the process steps and factory cells fit within the factory.
  • Troubleshooting - Guiding you to possible causes of processing issues affecting either cost, rate or quality and directing you to the most appropriate development workflow to improve the process
  • Optimization - An expansion on the development workflows where a larger number of options are considered to achieve the best mixture of cost, rate & quality for your application.