Investigation of process-structure-property relationships in polymer extrusion based additive manufacturing through in situ high speed imaging and thermal conductivity measurements

This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.

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Process-Structure-Property Relationships for 316L Stainless Steel Fabricated by Additive Manufacturing and Its Implication for Component Engineering

Journal of Thermal Spray Technology ◽

10.1007/s11666-016-0480-y ◽

2016 ◽

Vol 26 (4) ◽

pp. 610-626 ◽

Cited By ~ 22

Author(s):

Nancy Yang ◽

J. Yee ◽

B. Zheng ◽

K. Gaiser ◽

T. Reynolds ◽

...

Keyword(s):

Stainless Steel ◽

Additive Manufacturing ◽

316L Stainless Steel ◽

Structure Property ◽

Structure Property Relationships ◽

Process Structure ◽

Component Engineering

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A Collaborative Data Management System for Additive Manufacturing

Volume 1: 37th Computers and Information in Engineering Conference ◽

10.1115/detc2017-68457 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yan Lu ◽

Paul Witherell ◽

Alkan Donmez

Keyword(s):

Additive Manufacturing ◽

Data Management ◽

Management System ◽

Query Language ◽

Limiting Factors ◽

Data Management System ◽

Structure Property ◽

Structure Property Relationships ◽

Database Technology ◽

Process Structure

As additive manufacturing (AM) continues to mature as a production technology, the limiting factors that have hindered its adoption in the past still exist, for example, process repeatability and material availability issues. Overcoming many of these production hurdles requires a further understanding of geometry-process-structure-property relationships for additively manufactured parts. In smaller sample sizes, empirical approaches that seek to harness data have proven to be effective in identifying material process-structure-property relationships. This paper presents a collaborative AM data management system developed at the National Institute of Standards and Technology (NIST). This data management system is built with NoSQL (Not Only Structured Query Language) database technology and provides a Representational State Transfer (REST) interface for application integration. In addition, a web interface is provided for data curating, exploring, and downloading. An AM data schema is provided by NIST for an alpha release, as well as a set of data generated from an interlaboratory study of additively manufactured nickel alloy (IN625) parts. For data exploration, the data management system provides a mechanism for customized web graphic user interfaces configurable through a visualization ontology. As a collaboration platform, the data management system is set to evolve through sharing of both the AM schema and AM development data among the stakeholders in the AM community. As data sets continue to accumulate, it becomes possible to establish new correlations between processes, materials, and parts. The functionality of the data management system is demonstrated through the curation and querying of the curated AM datasets.

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