Periodic Reporting for period 2 - FORGE (Development of novel and cost-effective coatings for high-energy processing applications)
Periodo di rendicontazione: 2021-11-01 al 2023-04-30
A radical transformation is required in energy intensive industries in order for production to meet carbon neutral targets by 2050. Low carbon technologies and processes need to be able to address extreme and fluctuating conditions, but existing materials have inherent limitations in extreme conditions.
The FORGE Project aims to overhaul exiting materials and develop new materials creating methodologies and dataset to explore exploit the untap potential of the Compositionally Complex Materials.
These CCM are being developed to address surface degradation problems found in energy-intensive industries, with a particular focus on:
• Corrosion of metallic components from acidic, basic and reactive species
• Hydrogen embrittlement of high-strength steels from hydrolytic and process hydrogen
• Erosion of process plant from particulates, and wear from friction
• Thermal breakdown of ceramic vessel walls due to alkaline attack at pyrolytic temperatures
To solve these challenges the project will seek to design optimal high-performance coatings able to resist the specified set of degradation mechanisms as well as determining the best deposition methods. But the development of Compositionally Complex Materials requires Complex workflows. The ones that FORGE has defined, will bring the partners in a journey from theoretical material modelling to real industrial application. FORGE covers all the steps in between, and planned iterative cross-validations of its results to select down the most reliably performing coatings.
The field of Compositionally Complex Material (CCM) is vast, and Thermodynamic Calculations are not sufficient to provide enough information to identify suitable element composition. In FORGE Machine Learning models are used to support the initial composition selection and the following material synthesis and coating manufacturing.
The synthesis methods will be multiple: Induction Melting, PVD and Mechanical Alloying, providing insights on different aspects of the CCM composition and possibly generating results beyond the targets of FORGE, that is focused on material for the production of Coatings.
Apart the direct benefits from the involved industries, FORGE is working in setting up tools to expand the application of CCM to other sectors and applications, with tools to adapt the material composition and properties at each specific use case and calculation of the expected benefits in terms of LCA.
Finally FORGE is working also to establish procedures that guarantees the performance of its coatings, developing smart monitoring technologies that allows to program coating maintenance or replacement just when it is really needed.
The FORGE Project aims to overhaul exiting materials and develop new materials creating methodologies and dataset to explore exploit the untap potential of the Compositionally Complex Materials.
These CCM are being developed to address surface degradation problems found in energy-intensive industries, with a particular focus on:
• Corrosion of metallic components from acidic, basic and reactive species
• Hydrogen embrittlement of high-strength steels from hydrolytic and process hydrogen
• Erosion of process plant from particulates, and wear from friction
• Thermal breakdown of ceramic vessel walls due to alkaline attack at pyrolytic temperatures
To solve these challenges the project will seek to design optimal high-performance coatings able to resist the specified set of degradation mechanisms as well as determining the best deposition methods. But the development of Compositionally Complex Materials requires Complex workflows. The ones that FORGE has defined, will bring the partners in a journey from theoretical material modelling to real industrial application. FORGE covers all the steps in between, and planned iterative cross-validations of its results to select down the most reliably performing coatings.
The field of Compositionally Complex Material (CCM) is vast, and Thermodynamic Calculations are not sufficient to provide enough information to identify suitable element composition. In FORGE Machine Learning models are used to support the initial composition selection and the following material synthesis and coating manufacturing.
The synthesis methods will be multiple: Induction Melting, PVD and Mechanical Alloying, providing insights on different aspects of the CCM composition and possibly generating results beyond the targets of FORGE, that is focused on material for the production of Coatings.
Apart the direct benefits from the involved industries, FORGE is working in setting up tools to expand the application of CCM to other sectors and applications, with tools to adapt the material composition and properties at each specific use case and calculation of the expected benefits in terms of LCA.
Finally FORGE is working also to establish procedures that guarantees the performance of its coatings, developing smart monitoring technologies that allows to program coating maintenance or replacement just when it is really needed.
The first year of the FORGE project aimed to build solid basis for developing effective CCM material solutions. All the partners were involved in defining the needs for the Energy Intensive Industries, especially those represented in FORGE. From this analysis, the project defined the use case demonstrator and other potential application scenarios for the materials developed in FORGE. The project also defined the elements to be use for the formulation of Compositionally Complex Alloys and Compositionally Complex Ceramic and the Key Performance Indicators for the project stages.
The iterative work of material modelling and experimental validation for both CCA and CCC provided the first Machine Learning Algorithms, which were released once trained with data available from the literature. The collection and organization of data from literature was a non-trivial activity, due to the scattered availability of data that required normalizations and implementation to be compatible and to avoid as many as possible biases.
The experimental activity took most of the effort to complete the datasets, with the challenges implied in the synthesis and characterization of CCA and CCC compositions completely unknown before.
Thirty CCA alloys, selected among the predictions of the first ML algorithm, were synthesized by Induction Melting, or Arc Melting. Tens of material libraries were synthesized by PVD, with more than 60 different compositions in each material library. Then processed by to specimens for the extensive characterization campaign that included, XRD, DSC, SEM, EDX, ICP-MS, LECO, H2 uptake, Corrosion via LPR and CPP, and Hardness.
To support the consistency between composition obtained in bulk and in the form of PVD coatings, a method for Simultaneous Evaluation of Phase Stability (SEPS) allowed was developed. This method, based on CALPHAD calculations, successfully allowed the identification of the compositional window to be avoided experimentally to avoid samples which are not reproducible in bulk, regardless of the estimated material properties. SEPS was used to comprehensively evaluate approximately half a million alloy compositions, considering 54 quinary alloys, 120 permutations each and considering the composition for each element to vary from ~5% to 50%.
Eighty CCC were formulated and realized with Sol-Gel and direct dry powder mixing techniques. This led to a set of about 160 samples that have been fired at increasing temperatures (1300, 1500, 1700) to identify which conditions lead to the formation of a single-phase CCC. The characterization performed on CCC and CCA determines the dataset for the next iterations of the ML models.
The identification of promising CCA and CCC allowed FORGE to move into the second phase of the project, which involved the synthesis of the new material, by Mechanical Alloying, and the production of thick coating by multiple deposition techniques: Laser Cladding, HVOF, HVAF, Cold Spraying.
The iterative work of material modelling and experimental validation for both CCA and CCC provided the first Machine Learning Algorithms, which were released once trained with data available from the literature. The collection and organization of data from literature was a non-trivial activity, due to the scattered availability of data that required normalizations and implementation to be compatible and to avoid as many as possible biases.
The experimental activity took most of the effort to complete the datasets, with the challenges implied in the synthesis and characterization of CCA and CCC compositions completely unknown before.
Thirty CCA alloys, selected among the predictions of the first ML algorithm, were synthesized by Induction Melting, or Arc Melting. Tens of material libraries were synthesized by PVD, with more than 60 different compositions in each material library. Then processed by to specimens for the extensive characterization campaign that included, XRD, DSC, SEM, EDX, ICP-MS, LECO, H2 uptake, Corrosion via LPR and CPP, and Hardness.
To support the consistency between composition obtained in bulk and in the form of PVD coatings, a method for Simultaneous Evaluation of Phase Stability (SEPS) allowed was developed. This method, based on CALPHAD calculations, successfully allowed the identification of the compositional window to be avoided experimentally to avoid samples which are not reproducible in bulk, regardless of the estimated material properties. SEPS was used to comprehensively evaluate approximately half a million alloy compositions, considering 54 quinary alloys, 120 permutations each and considering the composition for each element to vary from ~5% to 50%.
Eighty CCC were formulated and realized with Sol-Gel and direct dry powder mixing techniques. This led to a set of about 160 samples that have been fired at increasing temperatures (1300, 1500, 1700) to identify which conditions lead to the formation of a single-phase CCC. The characterization performed on CCC and CCA determines the dataset for the next iterations of the ML models.
The identification of promising CCA and CCC allowed FORGE to move into the second phase of the project, which involved the synthesis of the new material, by Mechanical Alloying, and the production of thick coating by multiple deposition techniques: Laser Cladding, HVOF, HVAF, Cold Spraying.
FORGE is developing new materials solutions which will enable more efficient industries, the four main performance targets (PT) are Corrosion resistance, Resistance to H2 embrittlement, Wear resistance and high-temperature resistance.
Main Objectives have been reached in the second period of the project:
WP2 To complete the characterization of the first CCAs obtained by induction melting
WP2 To produce the first and second series of CCA PVD coatings
WP2 To create the SEPS* method to obtain crystalline PVD coatings
WP2 To establish and perform characterization of CCA grown on PVD for PT1, PT2, PT3 WP2 To use data from PVD coatings for improvement of ML algorithm
WP2 To identify PT3 material
WP3 To improve machine learning (ML) algorithm based on high throughput experiments
WP3 To synthesize and characterize tens of CCC and Spinel-CCC for data acquisition and CCC theory validation
WP3 To combine supervised ML and high throughput experiments to guide the discovery of SPINEL CCCs
WP5 To deliver CANTOR powder feedstock for HVOF, HVAF, CS, LC
WP5 To produce powder feedstocks of CCA by mechanical alloying
WP6 To deposit CCA powder by HVOF, HVAF, CS, and LC onto different steels
WP6 To deposit CCC powder onto refractory bricks
WP8 To produce and deliver taggant for smart monitoring and establish a mixing procedure to include taggant in CCA powder
WP8 To produce and deliver a benchtop sensor, calibrated for FORGE’s taggant on CCA
WP9 To identify the timing and case for industrial validation and long-term exposure in an industrial environment
Main Objectives have been reached in the second period of the project:
WP2 To complete the characterization of the first CCAs obtained by induction melting
WP2 To produce the first and second series of CCA PVD coatings
WP2 To create the SEPS* method to obtain crystalline PVD coatings
WP2 To establish and perform characterization of CCA grown on PVD for PT1, PT2, PT3 WP2 To use data from PVD coatings for improvement of ML algorithm
WP2 To identify PT3 material
WP3 To improve machine learning (ML) algorithm based on high throughput experiments
WP3 To synthesize and characterize tens of CCC and Spinel-CCC for data acquisition and CCC theory validation
WP3 To combine supervised ML and high throughput experiments to guide the discovery of SPINEL CCCs
WP5 To deliver CANTOR powder feedstock for HVOF, HVAF, CS, LC
WP5 To produce powder feedstocks of CCA by mechanical alloying
WP6 To deposit CCA powder by HVOF, HVAF, CS, and LC onto different steels
WP6 To deposit CCC powder onto refractory bricks
WP8 To produce and deliver taggant for smart monitoring and establish a mixing procedure to include taggant in CCA powder
WP8 To produce and deliver a benchtop sensor, calibrated for FORGE’s taggant on CCA
WP9 To identify the timing and case for industrial validation and long-term exposure in an industrial environment