Reference - A Framework for Formalizing Science Based Composites Manufacturing Practice

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Type	Thesis
Title	A Framework for Formalizing Science Based Composites Manufacturing Practice
Abstract	Advanced composites are materials growing in importance. In recent years, all major aerospace original equipment manufacturers (OEMs) have invested significantly in this technology, and its use in automotive, alternative energy and industrial applications is rapidly growing. Increases in product size and production scaling, given radically larger and more complex structures and the sheer volume of composites manufacturing, are leading to challenging problems concerning manufacturing risk, such as increasing development time frames and program costs. The use of manufacturing science to address these problems has always been a rational and promising strategy with most research efforts focusing on automation to improve production efficiencies, the development of multiphysics based models exercised in manufacturing simulation software, and the promise of production ‘big data’ analytics given improvements in sensor technologies and machine based learning algorithms. However, it is no longer sufficient to keep adding to this science base without explicitly addressing how manufacturing practice should be changed. In this thesis, qualitative research analysis of two industrial small and medium sized enterprises (SMEs) based in Western Canada is first performed to investigate the use of the composites manufacturing science base to manage technological and market uncertainty, and how the needs and receptor capabilities of OEMs and SMEs differ. Next, a manufacturing outcomes taxonomy explicitly linking the science–technology–practice levels of activity and a hierarchical knowledge model (Equipment–Tool–Part–Material factory ontology) that defines a common nomenclature for organizing composites manufacturing domain knowledge are introduced. A series of high-level manufacturing scenarios are presented to demonstrate this developed framework. Finally, case studies based on the thermal analysis of thick thermoset composites data sets using manufacturing simulation are presented. These case studies represent a starting point for how science based approaches can be used to directly support manufacturing decisions at all stages of the development design cycle. This work represents efforts to introduce a new translational research strategy aimed at both the composites manufacturing research community and the composites industry. Its focus is to encourage the systematic use of composites manufacturing science to transform manufacturing practice, and to support the effective management of increasing manufacturing risk.
Authors	Fabris, Janna Noemi
City	Vancouver
Department	Materials Engineering
Date	2018/10/15
University	The University of British Columbia, Vancouver
Publisher	The University of British Columbia, Vancouver
Publication	cIRcle
Websites	https://circle.ubc.ca/handle/2429/67567
DOI	10.14288/1.0372787
Owner	Janna Fabris
Language	English
Country	Canada

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.

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.

Outcomes represent the range of response/sensitivity to factory system attributes. Those that fail to satisfy manufacturing requirements are known as defects. Examples of manufacturing outcomes include process parameter outcomes, material structure outcomes, and material performance outcomes.

Thermosets are a class of polymer that undergo polymerization and crosslinking during curing with the aid of a hardening agent and heating or promoter. Initially they behave like a viscous fluid. During curing, they change from viscous fluid to rubbery gel (viscoelastic material) and finally glassy solid.

If heated after curing, initially they become soft and rubbery at high temperatures. If further heated, they do not melt but decompose (burn)

Comes in two parts: part A (resin) and B (hardener). When mixed, curing reaction starts and is not reversible.

Examples include epoxy or polyester.

The key design phases in new product development (NPD), particularly for complex engineering systems. This includes conceptual design, preliminary design (trade study), detail design, and production.