Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach
Abstract Minimising cycle time without inducing quality defects is a major challenge in injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of injection moulding, however existing methods have limitations, including the need for a large number of exp...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-80405-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850216090255228928 |
|---|---|
| author | Mandana Kariminejad David Tormey Caitríona Ryan Christopher O’Hara Albert Weinert Marion McAfee |
| author_facet | Mandana Kariminejad David Tormey Caitríona Ryan Christopher O’Hara Albert Weinert Marion McAfee |
| author_sort | Mandana Kariminejad |
| collection | DOAJ |
| description | Abstract Minimising cycle time without inducing quality defects is a major challenge in injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of injection moulding, however existing methods have limitations, including the need for a large number of experiments within a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, an experimental ADoE approach based on Bayesian optimisation was developed for injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential ( $$\Delta T$$ ) in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and Nondominated Sorting Genetic Algorithm (NSGA-II) using the Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50 $$\%$$ reduction in the number of experiments required for the single optimisation of $$\Delta T$$ , and an almost 30 $$\%$$ decrease for the optimisation of $$\Delta T$$ and cycle time together compared to composite desirability function and NSGA-II. The optimal settings identified by ADoE for multiobjective optimisation were similar to the selected Pareto optimal solution found by NSGA-II. |
| format | Article |
| id | doaj-art-b0b8d0d39ef848fe97aac707e8a9d91a |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b0b8d0d39ef848fe97aac707e8a9d91a2025-08-20T02:08:24ZengNature PortfolioScientific Reports2045-23222024-11-0114111910.1038/s41598-024-80405-2Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approachMandana Kariminejad0David Tormey1Caitríona Ryan2Christopher O’Hara3Albert Weinert4Marion McAfee5Centre for Precision Engineering, Materials and Manufacturing Research (PEM Centre), Atlantic Technological University SligoCentre for Precision Engineering, Materials and Manufacturing Research (PEM Centre), Atlantic Technological University SligoI-Form, Research Centre for Advanced Manufacturing, John Hume Institute, University College DublinCentre for Precision Engineering, Materials and Manufacturing Research (PEM Centre), Atlantic Technological University SligoCentre for Precision Engineering, Materials and Manufacturing Research (PEM Centre), Atlantic Technological University SligoCentre for Precision Engineering, Materials and Manufacturing Research (PEM Centre), Atlantic Technological University SligoAbstract Minimising cycle time without inducing quality defects is a major challenge in injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of injection moulding, however existing methods have limitations, including the need for a large number of experiments within a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, an experimental ADoE approach based on Bayesian optimisation was developed for injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential ( $$\Delta T$$ ) in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and Nondominated Sorting Genetic Algorithm (NSGA-II) using the Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50 $$\%$$ reduction in the number of experiments required for the single optimisation of $$\Delta T$$ , and an almost 30 $$\%$$ decrease for the optimisation of $$\Delta T$$ and cycle time together compared to composite desirability function and NSGA-II. The optimal settings identified by ADoE for multiobjective optimisation were similar to the selected Pareto optimal solution found by NSGA-II.https://doi.org/10.1038/s41598-024-80405-2Injection mouldingGaussian processBayesian adaptive design of experimentsMulti-objective optimisationNondominated sorting genetic algorithm |
| spellingShingle | Mandana Kariminejad David Tormey Caitríona Ryan Christopher O’Hara Albert Weinert Marion McAfee Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach Scientific Reports Injection moulding Gaussian process Bayesian adaptive design of experiments Multi-objective optimisation Nondominated sorting genetic algorithm |
| title | Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach |
| title_full | Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach |
| title_fullStr | Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach |
| title_full_unstemmed | Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach |
| title_short | Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach |
| title_sort | single and multi objective real time optimisation of an industrial injection moulding process via a bayesian adaptive design of experiment approach |
| topic | Injection moulding Gaussian process Bayesian adaptive design of experiments Multi-objective optimisation Nondominated sorting genetic algorithm |
| url | https://doi.org/10.1038/s41598-024-80405-2 |
| work_keys_str_mv | AT mandanakariminejad singleandmultiobjectiverealtimeoptimisationofanindustrialinjectionmouldingprocessviaabayesianadaptivedesignofexperimentapproach AT davidtormey singleandmultiobjectiverealtimeoptimisationofanindustrialinjectionmouldingprocessviaabayesianadaptivedesignofexperimentapproach AT caitrionaryan singleandmultiobjectiverealtimeoptimisationofanindustrialinjectionmouldingprocessviaabayesianadaptivedesignofexperimentapproach AT christopherohara singleandmultiobjectiverealtimeoptimisationofanindustrialinjectionmouldingprocessviaabayesianadaptivedesignofexperimentapproach AT albertweinert singleandmultiobjectiverealtimeoptimisationofanindustrialinjectionmouldingprocessviaabayesianadaptivedesignofexperimentapproach AT marionmcafee singleandmultiobjectiverealtimeoptimisationofanindustrialinjectionmouldingprocessviaabayesianadaptivedesignofexperimentapproach |