Reinforcement of composite coatings using nanoparticles (NPs)
The inclusion of ceramic and 2D NPs (e.g., SiC, Al2O3, BC4, Graphene – Gr, MoS2, WS2) can lead to composite coatings with excellent properties. Additionally, these properties may be tailored to the end applications needs by proper selection of NPs. For example, 2D nanomaterials like Gr nano-platelets in Ni matrix provides attributes such as self-lubrication, and the addition of silicon carbide (SiC) NPs shows significant reduction (around 50%) in the coefficient of friction under dry lubrication. The mechanisms of NPs incorporation is governed by a series of parameters, like the status of NPs in the plating bath, the chemistry of the bath, the applied current. In MOZART, accurate control of these parameters will be held during the process to obtain high quality coatings.
Data driven model using physical informed neural networks
Artificial neural networks (ANN) are designed to replicate the behavior of neural networks found in human brain. They are the foundation of Artificial Intelligence (AI) and are behind many of the most complex applications of machine learning, like recommendation systems for customers in products like streaming services, virtual assistance, and speech and facial recognition. The use of ANN gave rise to data-driven models, which are based on empirical observations of the process. This type of modeling is able to describe real conditions of the process, and it requires little knowledge about the system to be modeled.
MOZART will use generative algorithms with physical informed neural networks to deploy a predictive tool to correlate properties of the bath and the performance of the coatings, considering the SSbD parameters.
Data driven model using physical informed neural networks
Artificial neural networks (ANN) are designed to replicate the behavior of neural networks found in human brain. They are the foundation of Artificial Intelligence (AI) and are behind many of the most complex applications of machine learning, like recommendation systems for customers in products like streaming services, virtual assistance, and speech and facial recognition. The use of ANN gave rise to data-driven models, which are based on empirical observations of the process. This type of modeling is able to describe real conditions of the process, and it requires little knowledge about the system to be modeled.
MOZART will use generative algorithms with physical informed neural networks to deploy a predictive tool to correlate properties of the bath and the performance of the coatings, considering the SSbD parameters.
Simulation/modelling using Computer Aided Engineering (CAE)
Computer-aided engineering (CAE) is the broad usage of computer software to aid in engineering analysis tasks. CAE tools are used, to analyze the robustness and performance of components and assemblies. Its tools encompass simulation, validation, and optimization of products and manufacturing processes. CAE systems aim to be major providers of information to help support design teams in decision-making and have been used in various fields, like automotive, aviation, space, and shipbuilding industries.
MOZART will use CAE strategies for developing tooling systems that allows to plate or electroform parts within thickness specifications, considering the crucial gravitational impact on the NPs content and allowing a deeper analysis of a plating processes with a dispersed NPs load.
Ultrasonication
The ultrasonication technology consists in applying intense ultrasound waves into liquids and slurries. The acoustic cavitation produced by intense sonication creates energy-dense conditions such as high pressure and temperature differences, as well as high shear forces and turbulence. These forces agitate particles, break droplets, and disrupt cells resulting in homogenization, dispersion, emulsification, and extraction effects.
For the electrolytic baths used in Ni-based plating, MOZART will use this technique to homogenize dispersions and to clean surfaces from loose attached impurities, achieving mono-dispersed composite electrolytes.
Ultrasonication
The ultrasonication technology consists in applying intense ultrasound waves into liquids and slurries. The acoustic cavitation produced by intense sonication creates energy-dense conditions such as high pressure and temperature differences, as well as high shear forces and turbulence. These forces agitate particles, break droplets, and disrupt cells resulting in homogenization, dispersion, emulsification, and extraction effects.
For the electrolytic baths used in Ni-based plating, MOZART will use this technique to homogenize dispersions and to clean surfaces from loose attached impurities, achieving mono-dispersed composite electrolytes.
Online monitoring using Optofluidic Force Induction (OF2i®)
The Optofluidic Force Induction (OF2i®) is a highly sensitive online and real-time particle characterization method, developed by our partner BRAVE Analytics, sets particles in motion through actively induced photonic and fluidic forces. The high-speed evaluation of their movements provides statistically relevant process monitoring data (PAT-sensor) such as size, size-distribution, and concentration from several microns down to 20nm.
An on-line monitoring OF2i® sensor-station with MOZART specific models and algorithms will be build, tested at lab scale colloidal suspensions, and validated through offline standardized characterization methods for subsequent pilot line tests, providing online, continuous, and real time particle characterization as process feedback and quality control.
