Developing Machine Learning-empowered Responsive Manufacture Of Industrial Laser Systems
Lead Research Organisation:
Heriot-Watt University
Department Name: Sch of Engineering and Physical Science
Abstract
Aircraft gyroscope, telecommunications, manufacturing, and surgical tools to name a few; optical systems, and especially lasers, are critical components in a host of modern devices. The manufacture of these systems supports a massive, global industry. Many of these are extraordinarily complex with dozens of optical components each of which needs to be placed in the system with enormous accuracy; any misalignment will result in poor performance, or the failure of the entire system.
Currently this is accomplished by using highly qualified (even up to PhD level) and highly experienced system assembly teams who rely on a whole host of diagnostic and test equipment to make minute adjustments to the placement of each component. This is both time consuming and very expensive. It is also very difficult to modify production either terms of scale or specification. As a result these systems are very expensive and slow to respond to changing demand or potential for technical improvement.
This project will develop an automated robotic and mechatronic system for assembling lasers and other optical systems. We will combine; observations of highly skilled human operators; feedback from automated diagnostic and test equipment; robotic alignment tool wielding robots; and a combination of machine learning and search algorithms which will be used to control the alignment process.
The resulting system will be adaptive, able to cope with variations in part production, changes to the supply chain, modifications to the design specification, as well as being able to rapidly adapt to changes in demand. It will also result in a fundamental change to the way these systems are designed and developed and the levels of performance which can be achieved.
Currently this is accomplished by using highly qualified (even up to PhD level) and highly experienced system assembly teams who rely on a whole host of diagnostic and test equipment to make minute adjustments to the placement of each component. This is both time consuming and very expensive. It is also very difficult to modify production either terms of scale or specification. As a result these systems are very expensive and slow to respond to changing demand or potential for technical improvement.
This project will develop an automated robotic and mechatronic system for assembling lasers and other optical systems. We will combine; observations of highly skilled human operators; feedback from automated diagnostic and test equipment; robotic alignment tool wielding robots; and a combination of machine learning and search algorithms which will be used to control the alignment process.
The resulting system will be adaptive, able to cope with variations in part production, changes to the supply chain, modifications to the design specification, as well as being able to rapidly adapt to changes in demand. It will also result in a fundamental change to the way these systems are designed and developed and the levels of performance which can be achieved.
Organisations
- Heriot-Watt University (Lead Research Organisation)
- Oxford Lasers Ltd (Collaboration)
- DIGITAL CATAPULT (Collaboration)
- Leonardo MW Ltd. (Collaboration)
- SMC (Collaboration)
- National Manufacturing Institute Scotland (Collaboration)
- Renishaw (United Kingdom) (Collaboration, Project Partner)
- Manufacturing Technology Centre (MTC) (Collaboration)
- Gooch & Housego (Collaboration)
- Luxinar Limited (Project Partner)
- Gooch & Housego (United Kingdom) (Project Partner)
- Leonardo MW Ltd (Project Partner)
Publications
Rakhmatulin I
(2023)
Addressing Shortcomings in Manual Alignment of Laser Optics via Automation Tools
Rakhmatulin I
(2024)
A review of automation of laser optics alignment with a focus on machine learning applications
in Optics and Lasers in Engineering
Description | Robotic Alignment Advisory Board |
Organisation | Digital Catapult |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | Gooch & Housego |
Country | United Kingdom |
Sector | Private |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | Leonardo MW Ltd. |
Country | United Kingdom |
Sector | Private |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | Manufacturing Technology Centre (MTC) |
Country | United Kingdom |
Sector | Private |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | National Manufacturing Institute Scotland |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | Oxford Lasers Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | Renishaw PLC |
Country | United Kingdom |
Sector | Private |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Advisory Board |
Organisation | SMC |
Country | United Kingdom |
Sector | Private |
PI Contribution | The advisory board has a more limited access to project results and data than the collaboration partners, however we have allowed access to headline results, and future direction plans to be discussed, and dissected, between the board. |
Collaborator Contribution | The board meets on a bi-annual basis at major review points. The key contributions include: keeping the project scope aligned with industry, ensuring up-to-date information and processes are known and informing the research team on current best practice in the field. |
Impact | Nothing specifically linked to the board at this time. |
Start Year | 2022 |
Description | Robotic Alignment Research Consortium |
Organisation | Gooch & Housego |
Country | United Kingdom |
Sector | Private |
PI Contribution | The project goal is to research the capability for machine learning/optimisation processes for the alignment of complex optical systems (e.g. lasers). Our research team has been working on four separate areas, developing novel contributions in each (robotic manipulation, human behaviour analysis, robotic control/AI and integrated optical diagnostics). |
Collaborator Contribution | Collaboration partners have been making direct contributions in terms of person time for review/advisory meetings plus technical advice on target alignment systems, cost benefit protocols. A key input will be access to manufacturing staff for human behaviour observations. |
Impact | Although there are several outputs from the project at present none of these can be specifically tagged as from this collaboration. This is expected within years 2 and 3 of the project. |
Start Year | 2021 |
Description | Robotic Alignment Research Consortium |
Organisation | Leonardo MW Ltd. |
Country | United Kingdom |
Sector | Private |
PI Contribution | The project goal is to research the capability for machine learning/optimisation processes for the alignment of complex optical systems (e.g. lasers). Our research team has been working on four separate areas, developing novel contributions in each (robotic manipulation, human behaviour analysis, robotic control/AI and integrated optical diagnostics). |
Collaborator Contribution | Collaboration partners have been making direct contributions in terms of person time for review/advisory meetings plus technical advice on target alignment systems, cost benefit protocols. A key input will be access to manufacturing staff for human behaviour observations. |
Impact | Although there are several outputs from the project at present none of these can be specifically tagged as from this collaboration. This is expected within years 2 and 3 of the project. |
Start Year | 2021 |
Description | Robotic Alignment Research Consortium |
Organisation | Renishaw PLC |
Country | United Kingdom |
Sector | Private |
PI Contribution | The project goal is to research the capability for machine learning/optimisation processes for the alignment of complex optical systems (e.g. lasers). Our research team has been working on four separate areas, developing novel contributions in each (robotic manipulation, human behaviour analysis, robotic control/AI and integrated optical diagnostics). |
Collaborator Contribution | Collaboration partners have been making direct contributions in terms of person time for review/advisory meetings plus technical advice on target alignment systems, cost benefit protocols. A key input will be access to manufacturing staff for human behaviour observations. |
Impact | Although there are several outputs from the project at present none of these can be specifically tagged as from this collaboration. This is expected within years 2 and 3 of the project. |
Start Year | 2021 |