Challenge
Process Optimisation is fundamental in manufacturing. While AI has the potential to greatly enhance process optimisation, its adoption in the manufacturing sector has been slower than anticipated. The challenge lies in the need to tailor AI solutions for each specific machine tool and context, such as the material used or the shape of the manufactured object. This approach would require "tens of thousands" of unique AI models, making it impractical and unfeasible for widespread implementation.
Solution
Instead of developing an AI-solution for each machine tool and context, HLOOP builds a single AI-model exploiting the synergy between AI and human-workers. Assessing the quality of the manufacturing process is more effectively done by humans than AI, while AI is better at dealing with high-dimensional decision problems. We use the feedback of machine operators to train on-the-fly (that is without using any pre-collected dataset) and in real-time an AI-model that can predict when the manufacturing process is good/bad, and then optimise it.
Have you ever faced the disappointment of your manufactured product not turning out as desired? The "recipe" you are using may not always produce the desired result, leaving you wondering which parameters to change next in order to maximize the product quality. HLOOP aims to automatise and simplify process optimisation in manufacturing. It uses your qualitative (or quantitative) feedback about the quality of your product to quickly optimise your manufacturing parameters and process. Let's consider for example the process of baking a cake (figure-left). What is process optimisation in this case? Firstly, we carefully select the parameters to optimise, like amount of butter, sugar, and oven temperature. Next, we may experiment with four different recipes, each resulting in a different cake. We then taste each cake and rank the recipes based on our preferences. Using this valuable human feedback, Hloop can iteratively improve the recipe until we achieve the perfect cake (figure-right). Remarkably, this approach can be applied to any manufacturing process to achieve optimal products/processes, such as for instance in metal injection moulding (figure-centre).
Discover cutting-edge projects at the forefront of innovation. Our research spans from optimizing metal 3D printed stents, vehicle logistics to enhancing manufacturing processes and refining vibration analysis in milling. Contact us to learn more and try our technology to see how we can push the boundaries of what's possible in your industry.
Would you like to learn more about our theoretical framework for learning from human feedback in HLoop?
PI
Associate Professor
School of Computer Science and Statistics, TCD
Expertise: Probabilistic Machine Learning, Optimisation, Automatic control, Decision Making under Uncertainity, AI-based process optimisation, Smart Manufacturing.
CO-PI
Associate Professor
Dept. Mechanical, Manufacturing & Biomedical Engineering, TCD
Expertise: Additive Manufacturing (3D Printing), Laser Processing, Metallurgy, Post Processing, Powder Bed Fusion, Selective Laser Melting (SLM), Surface Engineering