Machine Learning Guided Development of Tailored Polyolefin Molecular Structures for Lightweighting Applications

机器学习引导开发适合轻量化应用的定制聚烯烃分子结构

• 批准号: 578510-2022
• 负责人: Lee, PatrickChangDongPCD
• 金额: $ 13.91万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759580
• 关键词: Machine; Learning; Guided; Development; Tailored

Across various industries that are major consumers of polyolefin resins, lightweight solutions are highly sought after to replace existing materials in an effort to reduce material and energy consumption within the packaging, construction, and automotive industries. Polymer foaming represents an extremely valuable technique for the production of lightweight materials, and while it is actively employed in many industries, the development of scaled-up foaming processes and foamable polymer resins is a highly intensive and arduous process. Recent progress by our group has demonstrated that a class of high melt strength (HMS) polyolefin resins show promising properties for producing superior lightweight foams across a wide processing window, an aspect that makes them particularly interesting for large scale manufacturing processes. The proposed project aims to employ machine learning (ML) techniques in the study of resin structure-foam property relationships as a tool to accelerate the materials development process and deepen our fundamental understanding. This proposal outlines a systematic plan for the development of polyolefin resins with tailored molecular structures that are optimized for creating lightweight foams with broad processing windows. The comprehensive understanding of the structure-property relationships developed through ML-assisted analysis and modeling will be used to inform and guide the upstream synthesis of resins with tailored molecular structures. This study will proceed following two primary objectives: 1) screening and analysis of HMS resins to develop structure-property relationships for small-scale foaming processes; (2) extension of this knowledge to the development of polyolefin resins tailored for Foam Injection Moulding (FIM) processes.This project is beneficial to Canadian plastics product manufacturing, which is a $27.7 billion industry. This research project will simultaneously help Canadian manufacturers to grow in a highly competitive market with new technologies. The project will also facilitate the training of multiple highly qualified personnel with industry-relevant expertise, and the creation of new jobs in the manufacturing sector of Ontario.

在聚烯烃树脂的主要消费者的各个行业中，轻质解决方案非常受欢迎，以取代现有材料，以减少包装，建筑和汽车行业的材料和能源消耗。聚合物发泡代表了用于生产轻质材料的极其有价值的技术，并且虽然它在许多行业中被积极地采用，但按比例放大的发泡工艺和可发泡聚合物树脂的开发是高度密集和艰巨的工艺。我们小组的最新进展已经证明，一类高熔体强度（HMS）聚烯烃树脂显示出在宽的加工窗口内生产上级轻质泡沫的有前景的性质，这一方面使得它们对于大规模制造工艺特别感兴趣。该项目旨在将机器学习（ML）技术用于树脂结构-泡沫性能关系的研究，作为加速材料开发过程和加深我们基本理解的工具。 该提案概述了开发具有定制分子结构的聚烯烃树脂的系统计划，这些分子结构经过优化，可用于制造具有宽加工窗口的轻质泡沫。通过ML辅助分析和建模开发的结构-性能关系的全面理解将用于通知和指导具有定制分子结构的树脂的上游合成。本研究将进行以下两个主要目标：（1）筛选和分析HMS树脂，以开发小规模发泡工艺的结构-性能关系;（2）将这一知识扩展到开发适用于泡沫注射成型（Foam Injection Moulding，简称FAM）工艺的聚烯烃树脂。该研究项目将同时帮助加拿大制造商在竞争激烈的市场中发展新技术。该项目还将促进培训具有行业相关专业知识的多名高素质人员，并在安大略制造业创造新的就业机会。