An Optimization Framework for Elastomer Machine Tools
(2022) MMKM10 20212Innovation
- Abstract
- A framework for optimizing the design of elastomer packaging machine tools, so called plunge tools, has been developed and has reached a proofofconcept state in this thesis. The framework indicates the capability of decreasing prototype expenses and total development time of new plunge tools by roughly three times compared to the existing iterative design methodology.
A finite element analysis (FEA) model serves as the core of the framework. Physical experiments based on Design of Experiments (DoE) theory have been conducted to validate the model and, with regression analysis, screen system factors influential on plunge tool performance. The experiments were also useful in detecting large differences in plunge tool performance... (More) - A framework for optimizing the design of elastomer packaging machine tools, so called plunge tools, has been developed and has reached a proofofconcept state in this thesis. The framework indicates the capability of decreasing prototype expenses and total development time of new plunge tools by roughly three times compared to the existing iterative design methodology.
A finite element analysis (FEA) model serves as the core of the framework. Physical experiments based on Design of Experiments (DoE) theory have been conducted to validate the model and, with regression analysis, screen system factors influential on plunge tool performance. The experiments were also useful in detecting large differences in plunge tool performance between the regular plunge tools and the additively manufactured plunge tools currently used as prototypes.
Response surface methodology (RSM) has been used together with the FEA model to optimize the design. An optimal spacefilling design point scheme used together with a Kriging interpolation response surface was found to be more capable of predict ing the FEA model responses than three other configurations. An optimized plunge design was generated with the framework after roughly 24 hours of computation time on an average desktop computer from 2013. The design had large similarities to a production design developed with manual iterative design development but performed even better in the simulation. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9076440
- author
- Montan Larsson, Love LU
- supervisor
- organization
- course
- MMKM10 20212
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Polyurethane, Elastomer, packaging industry, Design of Experiments, Finite Element Analysis, Response Surface Methodology, multiple linear regression analysis, Additive Manufacturing.
- language
- English
- id
- 9076440
- date added to LUP
- 2022-03-10 08:49:28
- date last changed
- 2022-03-10 08:49:28
@misc{9076440,
abstract = {{A framework for optimizing the design of elastomer packaging machine tools, so called plunge tools, has been developed and has reached a proofofconcept state in this thesis. The framework indicates the capability of decreasing prototype expenses and total development time of new plunge tools by roughly three times compared to the existing iterative design methodology.
A finite element analysis (FEA) model serves as the core of the framework. Physical experiments based on Design of Experiments (DoE) theory have been conducted to validate the model and, with regression analysis, screen system factors influential on plunge tool performance. The experiments were also useful in detecting large differences in plunge tool performance between the regular plunge tools and the additively manufactured plunge tools currently used as prototypes.
Response surface methodology (RSM) has been used together with the FEA model to optimize the design. An optimal spacefilling design point scheme used together with a Kriging interpolation response surface was found to be more capable of predict ing the FEA model responses than three other configurations. An optimized plunge design was generated with the framework after roughly 24 hours of computation time on an average desktop computer from 2013. The design had large similarities to a production design developed with manual iterative design development but performed even better in the simulation.}},
author = {{Montan Larsson, Love}},
language = {{eng}},
note = {{Student Paper}},
title = {{An Optimization Framework for Elastomer Machine Tools}},
year = {{2022}},
}