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      • Increasing throughput by curing parts simultaneously
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      • Practice for troubleshooting a Light RTM step
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        • Composite materials engineering webinar session 5 - Manufacturing processes - Introduction
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        • Composite materials engineering webinar session 7 - Manufacturing processes - Liquid composite moulding
        • Composite materials engineering webinar session 8 - Mechanics of composites - Part 1: Lamina level
        • Composite materials engineering webinar session 9 - Mechanics of composites - Part 2: Laminate level
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Dr. Anoush Poursartip's cdmHUB global composites expert webinar - A136

From CKN Knowledge in Practice Centre
Perspectives - A8Presentations - A135Dr. Anoush Poursartip's cdmHUB global composites expert webinar - A136
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Dr. Anoush Poursartip's cdmHUB global composites expert webinar
Perspectives article
Purdue cdmHUB-GCEWSArtwork-x6j3yXJ4ddSz.png
Document Type Article
Document Identifier 136
Themes
Tags
Video links
Dr. Anoush Poursartip's webinar recording
All Global Composites Experts Webinar Series recordings

Introduction[edit | edit source]

In the summer of 2020, the Purdue Composites Design & Manufacturing HUB (cdmHUB) organized a series of webinars presented by global composites experts. These webinars examine the history, present capabilities, and future of composites science and technology, with the goal of sharing the vast knowledge of composites that has been developed over the past 50 years. CKN Co-Director Dr. Anoush Poursartip was the inaugural speaker.

Abstract[edit | edit source]

Composite materials and structures are an excellent example of how engineering practice often outpaces scientific knowledge. Born in the analog world of the 1960s, carbon-fibre composites manufacturing and design practice is a complex and often fragile construct that is primarily driven by the need to . In the last two decades, the packaging of knowledge in the form of predictive simulations supported by characterized materials and standardized workflows has started to change this paradigm, but the best is yet to come. In this presentation, Dr. Poursartip charts the history of process simulation and in-process measurement & control to highlight how scientific knowledge is becoming good enough to disrupt current engineering practice. Dr. Poursartip posits that the specific needs of our domain can only be met with the strategic and careful merging of two previously separate digital threads, namely science-based Integrated Computational Materials Engineering (ICME) with data-based Industry 4.0. Using examples from his own 40-year career bridging academic research and industrial practice, Dr. Poursartip highlights how digital strategies will be even more important to our community as we emerge from the current economic crisis.

Video link[edit | edit source]

The recording of Dr. Poursartip's presentation can be viewed at the cdmHUB's website here[1].

Other presentations in the cdmHUB's global composites expert webinar series can be viewed found here.

Video timeline[edit | edit source]

Time
 Focus Links to related information in the Knowledge in Practice Centre
00:00
 Welcome and introduction to the cdmHUB N/A
02:16
 Introduction of Dr. Anoush Poursartip N/A
04:07
 Acknowledgments N/A
04:44
 Background on historical composites manufacturing practice Future content
06:41
 Digitalization of composites & digital manufacturing Future content
08:14
 The "digital twin"; a virtual manufactured part Future content
09:57
 A typical prepreg composites factory and the early use of a virtual twin for thermochemical cure modelling Future content
12:15
 The virtual twin for cure modelling today Future content
14:15
 The virtual twin for cure modelling today - Predicting the effect of equipment and tooling
15:48
 The virtual twin for cure modelling today - Designing the equipment, tooling and thermal cycle
17:12
 Today's virtual twin for forming processes Future content
19:17
 Today's virtual twin for autoclave processes Future content
20:07
 Merging science-based simulation and data-driven modelling - Theory guided machine learning Future content
25:27
 Science-based data analytics of autoclave production data Future content
29:47
 Uncertainty quantification in material models and simulation Future content
34:16
 Manufacturing simulation to support the disruption of current practice Future content
34:52
 Manufacturing simulation to support the disruption of current practice - The temperature history's effect on fracture properties of thermoplastically toughened epoxy Future content
40:08
 Data to support the disruption of practice Future content
41:54
 The Digital Learning Factory as a method for generating process data N/A
43:25
 Summary N/A
44:38
 Acknowledgments N/A
45:03
 Q&A N/A


Related pages

Page type Links
Introduction to Composites Articles
Foundational Knowledge Articles
Foundational Knowledge Method Documents
Foundational Knowledge Worked Examples
Systems Knowledge Articles
Systems Knowledge Method Documents
Systems Knowledge Worked Examples
Systems Catalogue Articles
Systems Catalogue Objects – Material
Systems Catalogue Objects – Shape
Systems Catalogue Objects – Tooling and consumables
Systems Catalogue Objects – Equipment
Practice Documents
Case Studies
Perspectives Articles

References

  1. ↑ 1.0 1.1 [Ref] Poursartip, Anoush (2020), Digital disruption of composites manufacturing and design, Purdue University cdmHUB (published 10 October 2020)CS1 maint: uses authors parameter (link) CS1 maint: date and year (link)



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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.

A set of steps/procedures that are intended to provide guidance in manufacturing and/or decision making activities.

There are two types of workflows:

  • Standard workflows are intended to formalize practices where the manufacturing science base exists. The focus is to provide guidance using manufacturing simulation as an enabling tool (e.g. design activities/decisions relating to thermal management).
  • Complex workflows are intended to reduce the level of effort in practices where the existing manufacturing science base is not sufficiently mature to support production scale problems. The focus is to provide guidance using simulation based thinking and/or checklists (e.g. design activities/decisions relating to porosity management).

Pre-impregnated (prepreg) material refers to fibre that is already combined with resin. It is the most common material form used in aerospace.

During prepreg production, (e.g. fibres are run through a resin bath), prepreg is heated and partially cured to B Stage (< 5 % degree of cure). Thermoset prepregs (e.g. epoxy prepreg) have to be kept in a freezer at around -20 °C. At room temperature, the epoxy starts to cure.

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, modelling refers to either:

  • Process modelling (predicting manufacturing outcomes, or the inverse problem of predicting manufacturing parameters to achieve a desired outcome), or
  • Performance modelling (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 "Simulation")

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")

Retrieved from "https://compositeskn.org/KPC/index.php?title=A136&oldid=4756"
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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.
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