ProductivAI will develop a novel toolkit for process optimisation in PCB manufacturing and vacuum coatings industries, enabling process efficiency improvements resulting in reduced energy consumption and reduced waste and improved ability to meet challenging (and commercially attractive) specifications and short turnaround times. Current lean six-sigma methodologies provide efficiency gains through continuous improvement following highly manual procedures over process iterations. ProductivAI will make a step change in the ability of companies to achieve "right first time" production output to challenging specifications and stringent quality criteria whilst reducing energy consumption, waste and cost through higher yields, more efficient processes and faster throughput with potential for significant annual savings. These benefits will be achieved through the use of a machine-learning optimisation approach tailored, for the first time, specifically to the needs of electronics and coatings industries and integrated within a lean six-sigma framework. The system will automate and massively speed up process improvement and reuse of historical data to optimise process conditions. Novel aspects of our approach include the use of a special two-stage optimisation process which provides rapid global optimisation even for complex processes and the use of data synthesis to achieve faster and more accurate model training. This novel technological approach will be integrated into a lean six-sigma framework for rapid adoption by practitioners within the aforementioned industries. The algorithms will be implemented in a software platform for ease of use and integration with other quality and enterprise software tools. The effectiveness of the toolkit will be demonstrated trough two use cases in PCB manufacture and vacuum coatings, providing the two advanced manufacturing industry companies with direct gains in process performance.

AI6S will develop a novel toolkit for process optimisation in foundation industries (FI), enabling process efficiency improvements resulting in reduced energy consumption and reduced waste and improved ability to meet challenging (and commercially attractive) specifications and short turnaround times. Current lean six-sigma methodologies provide efficiency gains through continuous improvement following highly manual procedures over process iterations. AI6S will make a step change in the ability of companies to achieve "right first time" production output to challenging specifications and stringent quality criteria whilst reducing energy consumption, waste and cost through higher yields, more efficient processes and faster throughput with potential for annual savings 21,800,111 kgCO2e (carbon dioxide equivalent)/ £58M for the UK and 675million kgCO2e/ £1,800million globally. These benefits will be achieved through the use of a machine-learning optimisation approach tailored, for the first time, specifically to the needs of foundation industries (FIs) and integrated within a lean six-sigma framework. The system will automate and massively speed up process improvement and reuse of historical data to optimise process conditions. Novel aspects of our approach include the use of a special two-stage optimisation process which provides rapid global optimisation even for complex processes involved in high energy thermal processes and the use of data synthesis to achieve faster and more accurate model training. This novel technological approach will be integrated into a lean six-sigma framework for rapid adoption by practitioners within the foundation industries. The algorithms will be implemented in a software platform for ease of use and integration with other quality and enterprise software tools. The effectiveness of the toolkit will be demonstrated trough two use cases in metal forging and glass production, providing the two foundation industry companies with direct gains in process performance.

Confined spaces currently account for around 15 deaths per year in the UK alone -- a loss rate which can and should be diminished to zero, through the use of technology in large infrastructure projects. HyBird is developing technology to meet this target through autonomous confined space UAV solutions, comprising a small, lightweight, collision-tolerant smart drone, an autonomous deployment docking station, and an AI-based in-situ material characterisation and threat detection and inspection software. These capabilities will minimise the need to send personnel into potentially hazardous environments -- and will analyse and assess the environment for threats/dangers in case it is required for one to enter. In addition to the human safety cost, extreme environments cost infrastructure projects billions of pounds each year due to defects, site down-time, and labour costs. Deployment of such a system can reduce the cost of inspection by in excess of 80%, whilst drastically improving productivity -- through early defect detection, and reduced down-time. HyBird's autonomous UAV solution will directly benefit asset owners, as well as service providers in the infrastructure/construction space; for a relatively small investment, the return on investment is realised through reduced project costs, lower health and safety risk, greater quality analytics, service transparency, and ultimately more business opportunities.