University of Manchester
Assembly Line
Watercycle Technologies Achieves Major UK Breakthrough in the Production of Large-Scale Battery-Grade Lithium Carbonate from UK Brines
Watercycle Technologies Ltd, a climate tech spinout from the University of Manchester specialising in the development of sustainable mineral recovery systems, has produced over 100kg of battery-grade lithium carbonate from UK-sourced brines. This result represents a major achievement towards building a robust battery innovation ecosystem in the UK and developing a globally competitive battery minerals supply chain.
Dr Ahmed Abdelkarim, CTO and Co-Founder highlighted ‘These results mark yet another technological breakthrough by our DLEC™ technology, one of the first in Europe to produce such quantities of lithium carbonate crystals. We understand our customers’ needs to obtain this product more efficiently, so we’ve designed our end-to-end solution to meet this demand. With the ability to generate refined lithium carbonate onsite, our technology offers customers the ability to capture more of the value chain. We are now positioning ourselves to supply lithium salts at the ton-scale for OEMs and chemical suppliers.’
Process incidence monitoring in material identification during drilling stacked structures using support vector machine
Drilling of stacks comprising carbon fibre-reinforced polymers (CFRP) and aluminium in a single shot is a typical operation in the assembly of aircraft. This paper proposes a novel approach to identify incidences in CFRP/Al stack drilling with 94 % classification accuracy based on signal features and support vector machine (SVM). This enables the application of adaptive drilling which aerospace industry tries to introduce, and cutting parameters (cutting speed, feed) are automatically adjusted based on features extracted from signals obtained to achieve optimal machining. The t-distributed stochastic neighbour embedding (T-SNE) algorithm is applied to evaluate the separability and invariance of features with the significant influence of tool wear. Collinear analysis and hierarchy dendrogram are conducted to test the accuracy and robustness of the new approach, and a distance-based feature pruning is then proposed to compress data while maintaining the algorithm’s performance. The proposed SVM model achieves an accurate and reliable incidence identification, thereby enhancing the decision-making for adaptive drilling in machining stacked structures.