A first step toward intelligent forming of fabric composites : adapting basic industry 4.0 concepts
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Kazemi Nojadeh, Sorayya
issued: 2020-06-01T17:07:48Z
issued: 2020
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Industry 4.0 is becoming a next-generation R&D area in the advanced composites manufacturing sector, to fully automate fabrication processes and increase the production quality and agility. Forming woven fabric reinforced composites on to complex 3D shapes is particularly of interest under this R&D trend, as these composites have become materials of choice in high-performance aerospace, energy, and automotive structures. Defects during the forming of long-fiber fabrics can affect the mechanical properties of the product. A vision under Industry 4.0 would be to develop AI-based tools that can predict, detect, and mitigate such defect during manufacturing. This thesis presents a prototype example of intelligent fabric composite manufacturing by conducting a series of hemisphere draping trials on a thermoplastic fiberglass/polypropylene weave, followed by the digitization of quality check. An image acquisition system was designed and integrated into a robotic arm to facilitate data collection. The quality function for each product was defined based on the height and area of formed wrinkles, as well as the sphericity and roundness of the formed hemisphere. By gathering data from around 70 samples and over 1200 images from different forming set-ups, machine learning algorithms were trained and consequently, the quality class of each product was predicted. Furthermore, to optimize the product quality, machine learning tools recommend the desired input variable of forming. Overall, a higher number of clamps and a higher number of reinforcement layers, with a higher and uniformly distributed torque magnitudes across clamps yielded the least visible wrinkles on the product. As another imaginary scenario in a smart factory, an on-line quality check tool was developed and tested. Namely, when the forming process is finished, a smart image acquisition system scans the part and checks the quality of different formed areas of the part and informs the AI tool of the ‘wrinkle’ or ‘…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/74627
note: A FIRST STEP TOWARD INTELLIGENT FORMING OF FABRIC COMPOSITES: ADAPTING BASIC INDUSTRY 4.0 CONCEPTS by Sorayya Kazemi Nojadeh A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2020 © Sorayya Kazemi Nojadeh, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: A FIRST STEP TOWARD INTELLIGENT FORMING OF FABRIC COMPOSITES: ADAPTING BASIC INDUSTRY4.0 CONCEPTS submitted by Sorayya Kazemi Nojadeh in partial fulfillment of the requirements for the degree of Master of Applied Science In Mechanical Engineering Examining Committee: Dr. Abbas S Milani, School of Engineering, UBC Okanagan Supervisor Dr. Homayoun Najjaran, School of Engineering, UBC Okanagan Supervisory Committee Member Dr. Zheng Liu, School of Engineering, UBC Okanagan Supervisory Committee Member Dr. Warren Hare, IKBarber School of Arts and Science, UBC Okanagan University Examiner iii Abstract Industry 4.0 is becoming a next-generation R&D area in the advanced composites manufacturing sector, to fully automate fabrication processes and increase the production quality and agility. Forming woven fabric reinforced composites on to complex 3D shapes is particularly of interest under this R&D trend, as these composites have become materials of choice in high-performance aerospace, energy, and automotive structures. Defects during the forming of long-fiber fabrics can affect the mechanical properties of the product. A vision under Industry 4.0 would be to develop AI-based tools that can predict, detect, and mitigate such defect during manufacturing. This thesis presents a prototype example of intelligent fabric composite manufacturing by conducting a series of hemisphere draping trials on a thermoplastic fiberglass/polypropylene weave, …
has type: Thesis/Dissertation
date issued: 2020-09
is shown at: 10.14288/1.0391085
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/74627