Composites design - A229

Composites design
Document Type	Article
Document Identifier	229

Overview

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.

Current and future practice

Current typical practice in developing aerospace composite structural products is to scale up the product design in size and complexity. 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

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.

Knowledge-in-Practice. From

Design for manufacturability

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. 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

Scalability

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. As such, both size and production scaling should be considered together, early on.

References

About	Help

With regards to manufacturing, risk is the combination of the probability and consequences of undesirable manufacturing outcomes. Manufacturing risk can lead to technical issues, program/schedule delays, and cost overruns.

Any manufacturing and/or decision making activity that occurs during any stage of the development design cycle (e.g. conceptual design to production).

In the context of Knowledge in Practice, practice refers to the systematic use of science based knowledge to reduce composites manufacturing risk, cost, and development time.

In the context of knowledge in practice, knowledge refers to the systematic use of science based knowledge in composites manufacturing practice.

There is a distinction between experience based knowledge and science based knowledge:

• Experience based knowledge (‘know-how’) is an understanding of potential outcomes and their relationships that is founded on pragmatism and experience accumulated over time in individual programs, companies and in the industry more broadly.
• Science based knowledge (‘know-why’) is an understanding of potential outcomes and their relationships, based on the important processing physics, that is mature enough to be codified using the appropriate governing laws and constitutive equations.

Engineered materials (designed to have specific properties) made from two or more constituent materials with different physical or chemical properties. The constituents remain separate and distinct on a macroscopic level within the finished structure.

The commonality of material level properties at all scales of the ‘building block’ (coupon level to production scale structures) in terms of chemical–physical–mechanical states such as degree of cure/degree of crystallization, fibre volume fraction, residual stresses, etc.

Degree of cure (DOC) is an indication of how far the chemical curing reaction (crosslinking process) has advanced in a thermoset resin.

DOC is defined with a number between 0 and 1 (or 0% and 100%) where 100% is a fully cured resin. It does not have to fully reach 100% for the resin to become solid or the part to be used. In some aerospace applications, resins are only cured to about 90%. Higher the degree of cure, higher the mechanical properties.

Volume fraction of either matrix or fibres with respect to total composite volume (matrix + fibre).

The use of multiphysics models to predict the outcome of real-world scenarios. May be analytical (closed form, "hand calculations") or computational (implementation on computers is required due to the large number of calculations involved. e.g. finite element method, finite difference method)

Most often in composite materials engineering, simulation refers to either:

• Process simulation (predicting manufacturing outcomes, or the inverse problem of predicting manufacturing parameters to achieve a desired outcome), or
• Performance simulation (predicting the stiffness/strength/suitability of a structure, or the inverse problem of the material properties and dimensional requirements to achieve a desired stiffness/strength/suitability of a structure)

(same as "Modelling")

A general term for the variability that affects the accuracy of a metric, statement or thought.

Modelling & simulation In modelling & simulation a distinction is made between uncertainty and error:

• Uncertainty
  • Aleatory uncertainty: associated with inherent variability. Process requirements, such as material and process specifications are typically used to manage aleatory uncertainty.
  • Epistemic uncertainty: unknowns due to a lack of knowledge, or imperfect knowledge. Typically managed by improving the understanding of manufacturing outcomes and their interactions (e.g. process-structure-property relationships)
• Error
  • Acknowledged errors: Explicit assumptions and/or simplifications (e.g. modelling practices)
  • Unacknowledged errors: Mistakes and/or blunders (e.g. using the wrong material model)

Innovation management As defined in terms of commercialization opportunities/risks:

• Technological uncertainty: Relates to the feasibility of an innovation idea and/or the economic viability of scaling (e.g. from primary research to production).
• Market uncertainty: Exists if the innovation idea cannot sufficiently meet market needs, where the market opportunity is not obvious/cannot be guaranteed, or where competitors are working on alternative solutions.

The capability of the manufacturing process to produce parts of acceptable quality (e.g. meet engineering, manufacturing, regulatory requirements), repeatably and robustly.