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Details of Grant 

EPSRC Reference: EP/V051164/1
Title: Developing Machine Learning-empowered Responsive Manufacture Of Industrial Laser Systems
Principal Investigator: Carter, Dr RM
Other Investigators:
Esser, Professor M J D Chantler, Professor M Erden, Dr M
Kong, Dr X
Researcher Co-Investigators:
Project Partners:
Gooch & Housego Leonardo MW Ltd Luxinar Limited
Renishaw plc (UK)
Department: Sch of Engineering and Physical Science
Organisation: Heriot-Watt University
Scheme: Standard Research
Starts: 01 August 2021 Ends: 31 July 2024 Value (£): 1,376,132
EPSRC Research Topic Classifications:
Human-Computer Interactions Information & Knowledge Mgmt
Manufacturing Machine & Plant Optoelect. Devices & Circuits
EPSRC Industrial Sector Classifications:
Manufacturing Electronics
Related Grants:
Panel History:
Panel DatePanel NameOutcome
23 Feb 2021 Responsive Manufacturing Full Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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Summary
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Further Information:  
Organisation Website: http://www.hw.ac.uk