Optimization of thermoplastic composite manufacturing with digital process intelligence
Thermoplastic composites are gaining traction in industries such as aerospace and automotive due to their mechanical toughness, recyclability, and scalable manufacturing. However, the relative nascency of thermoplastic composites and their complex production means optimal manufacturing parameters ar...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156039 |
| _version_ | 1826193023730450432 |
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| author | Haas, Evan |
| author2 | Frey, Daniel |
| author_facet | Frey, Daniel Haas, Evan |
| author_sort | Haas, Evan |
| collection | MIT |
| description | Thermoplastic composites are gaining traction in industries such as aerospace and automotive due to their mechanical toughness, recyclability, and scalable manufacturing. However, the relative nascency of thermoplastic composites and their complex production means optimal manufacturing parameters are not well characterized. Processes are often developed through trial-and-error with limited understanding of the underlying drivers of material behavior, reducing yields and stretching development timelines. This work describes a digital intelligence infrastructure built to close this knowledge gap with high-resolution manufacturing data collection. This inexpensive system, comprised of a series of Programmable Logic Controller (PLC)s, Raspberry Pi-based telemetry units, and SQL database, captures high resolution data across hundreds of shop-floor sensors. Since this effort began, scrap rates for the targeted product dropped 85%. We also describe experiments probing composites behavior during thermoforming; by monitoring parameters including pressure, temperature, cooling rate, and dimensions, the production process is characterized and controlled. A Design of Experiments (DOE) based on this platform identified temperature as the determining factor of outcome quality. Furthermore, controlling temperature by closing the loop with current sensors and infrared imaging effectively sustained high quality. Lastly, we describe the early stages of a digitally-informed New Product Development (NPD) process to reduce development times using data from this system. |
| first_indexed | 2024-09-23T09:32:27Z |
| format | Thesis |
| id | mit-1721.1/156039 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:32:27Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1560392024-08-13T03:50:56Z Optimization of thermoplastic composite manufacturing with digital process intelligence Haas, Evan Frey, Daniel Ramakrishnan, Rama Massachusetts Institute of Technology. Department of Mechanical Engineering Sloan School of Management Thermoplastic composites are gaining traction in industries such as aerospace and automotive due to their mechanical toughness, recyclability, and scalable manufacturing. However, the relative nascency of thermoplastic composites and their complex production means optimal manufacturing parameters are not well characterized. Processes are often developed through trial-and-error with limited understanding of the underlying drivers of material behavior, reducing yields and stretching development timelines. This work describes a digital intelligence infrastructure built to close this knowledge gap with high-resolution manufacturing data collection. This inexpensive system, comprised of a series of Programmable Logic Controller (PLC)s, Raspberry Pi-based telemetry units, and SQL database, captures high resolution data across hundreds of shop-floor sensors. Since this effort began, scrap rates for the targeted product dropped 85%. We also describe experiments probing composites behavior during thermoforming; by monitoring parameters including pressure, temperature, cooling rate, and dimensions, the production process is characterized and controlled. A Design of Experiments (DOE) based on this platform identified temperature as the determining factor of outcome quality. Furthermore, controlling temperature by closing the loop with current sensors and infrared imaging effectively sustained high quality. Lastly, we describe the early stages of a digitally-informed New Product Development (NPD) process to reduce development times using data from this system. S.M. M.B.A. 2024-08-12T14:17:07Z 2024-08-12T14:17:07Z 2024-05 2024-06-25T18:12:19.917Z Thesis https://hdl.handle.net/1721.1/156039 Attribution 4.0 International (CC BY 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Haas, Evan Optimization of thermoplastic composite manufacturing with digital process intelligence |
| title | Optimization of thermoplastic composite manufacturing with digital process intelligence |
| title_full | Optimization of thermoplastic composite manufacturing with digital process intelligence |
| title_fullStr | Optimization of thermoplastic composite manufacturing with digital process intelligence |
| title_full_unstemmed | Optimization of thermoplastic composite manufacturing with digital process intelligence |
| title_short | Optimization of thermoplastic composite manufacturing with digital process intelligence |
| title_sort | optimization of thermoplastic composite manufacturing with digital process intelligence |
| url | https://hdl.handle.net/1721.1/156039 |
| work_keys_str_mv | AT haasevan optimizationofthermoplasticcompositemanufacturingwithdigitalprocessintelligence |