consultancy

Lucideon’s IMPACT^TM team can develop novel materials and processes through:

• Reducing the number of Design of Experiments for more effective results for client’s materials and processes
• Finite Element Analysis for simulating materials in its natural environment.
• Data science & machine learning for supporting the innovative research completed and driving novel materials and processes to commercialization.

Furthermore, the optimization of current materials and processes is a strong area of expertise at Lucideon, a key pathway to Industry 4.0.

Our primary goals are:

• To promote a culture that harnesses advanced technology to drive step-change solutions to materials/process challenges
• To combine computation, experimental, and synthesis methods to expand the solution toolbox and support overall material/process development efficiency
• To leverage latest development in characterization techniques and to develop high-throughput benchmarking tools
• To bring industry together under common challenges and to promote industry best practice in order to enable clients to compete at the top without the steep learning curves

Our capabilities

• First Principles Guidance – Working with our skilled network of modelling partners, we can explore novel chemical space, design new materials, or optimize existing materials based on their fundamental chemical and structural characteristics. We use targeted experimental methods to directly validate and guide the model output, linking the theoretical to the physical and accelerating the iterative R&D cycle.
• Finite Element Analysis – Combining this with our testing characterization facilities at Lucideon, we can create accurate and robust simulations of materials or systems during production or end-component operation. For example, we can simulate the deformation and failure of materials under different loading conditions and at different temperatures, validate the behaviour with real data from our testing team, and use the predictions to optimize a component’s design or performance.
• Machine Learning – We can use ML techniques to carry out Design of Experiments, improve the quality of the data collected, and identify the optimal conditions for achieving desired material properties. The ML approach can consider a greater number of process parameters than traditional statistics, while utilizing fewer and more efficient experiments. We harness clients’ historical data to predict the behaviour and performance of materials under various operating conditions and scenarios, or to create real-time dashboards that monitor and visualize the status and progress of anything from experiments and projects to online process monitoring.