Additive ceramics manufacturing with vat-photopolymerisation (VP) is a developing field, and the need for suitable printing materials hinders its fast growth. Binder mixtures significantly influence the mechanical properties of printed ceramic bodies by VP, considering their rheological properties, curing performances and green body characteristics. Improving mechanical characteristics and reducing cracks during printing and post-processes is mainly related to binder formulations. The study aims to develop a binder formulation to provide the printed ceramic specimens with additional green strength. The impact on mechanical properties (ultimate tensile strength, flexural strength, Young’s and strain at breakpoint), viscosity and cure performance of Urethane Acrylate (UA) and thermoplastic Polyether Acrylate (PEA) oligomers to monofunctional N-Vinylpyrrolidone (NVP), 1,6-Hexanediol Diacrylate (HDDA) and Tri-functional Photocentric 34 (PC34) monomers were investigated under varying concentrations. The best mechanical characteristic was showcased when the PC34 was replaced with 20–30 wt.% of UA in the organic medium. The Thermogravimetric Analysis (TGA) and sintering test outcomes revealed that increasing the content of NVP in the organic medium (above 15 wt.%) leads to uncontrolled thermal degradation during debinding and defects on ceramic parts after sintering. The negative effect of UA on the viscosity of ceramic-loaded mixtures was controlled by eliminating the PC34 compound with NVP and HDDA, and optimum mechanical properties were achieved at 15 wt.% of NVP and 65 wt.% of HDDA. PEA is added to provide additional flexibility to the ceramic parts. It was found that strain and other mechanical parameters peaked at 15 wt.% of PEA. The study formulated the most suitable binder formulation on the green body strength of printing silica ceramics as 50 wt.% HDDA, 20 wt.% Urethane Acrylate, 15 wt.% NVP and 15 wt.% PEA.

Vat photopolymerization (VPP) is an additive manufacturing method that requires the design of photocurable resins to act as feedstock and binder for the printing of parts, both monolithic and composite. The design of a suitable photoresin is costly and time-consuming. The development of one formulation requires the consumption of kilograms of costly materials, weeks of printing and performance testing, as well as the need to have developers with the expertise and knowledge of the materials used, making the development process cost thousands. This paper presents a new characterization methodology for acrylates that allows for the computerization of the photoresin formulation development process, reducing the timescale to less than a week. Okoruwa Maximum Saturation Potential (OMSP) is a methodology that uses attenuated total reflection (ATR-FTIR) to study the functional group of acrylates, assigning numerical outputs to characterize monomers, oligomers and formulations, allowing for more precise distinguishment between materials. It utilizes the principles of Gaussian normal distribution for the storage, recall, and computerization of acrylate data and formulation design without the need to database numerous files of spectral data to an average coefficient of determination (R²) of 0.97. The same characterization method can be used to define the potential reactivity of acrylate formulations without knowing the formulation components, something not possible when using properties such as functionality. This allows for modifications to be made to unknown formulations without prior knowledge of their contents. Validation studies were performed to define the boundaries of the operation of OMSP and assess the methodology’s reliability as a characterization tool. OMSP can confidently detect changes caused by the presence of various acrylates made to the photoresin system and distinguish between acrylates of the same viscosity and functionality. OMSP can compare digitally mixed formulations to physically mixed formulations and provides a high degree of accuracy (R² of 0.9406 to 0.9964), highlighting the future potential for building foundations for artificial intelligence in VPP; the streamlining of photoresin formulation design; and transforming the way acrylates are characterized, selected, and used.

Vat Photopolymerization (VPP) is an additive manufacturing method that requires the design of photocurable resins to act as feedstock and binder for the printing of parts, both monolithic or composite. The design of a suitable photoresin is costly and time-consuming. The development of one formulation requires the consumption of kilograms of costly materials, weeks of printing and performance testing, as well as the need to have developers with the expertise and knowledge of the materials used, making the development process cost thousands. This paper presents a new characterization methodology for acrylates that allows for the computerization of the photoresin formulation development process, reducing the timescale to less than a week. Okoruwa Maximum Saturation Potential (OMSP) is a methodology that uses Attenuated Total Reflection (ATR-FTIR) to study the functional group of acrylates, assigning numerical outputs to characterize monomers, oligomers and formulations, allowing for more precise distinguishment between materials. It utilizes the principles of Gaussian normal distribution for the storage, recall and computerization of acrylate data and formulation design without the need to database numerous files of spectral data to an average coefficient of determination (R² ) of 0.97. The same characterization method can be used to define the potential reactivity of acrylate formulations without knowing the formulation components, something not possible when using properties such as functionality. This allows for modifications to be made to unknown formulations without prior knowledge of their contents. Validation studies were performed to define the boundaries of the operation of OMSP and assess the methodology’s reliability as a characterization tool. OMSP can confidently detect changes caused by the presence of various acrylates made to the photoresin system and distinguish between acrylates of the same viscosity and functionality. OMSP can compare digitally mixed formulations to physically mixed formulations and provides a high degree of accuracy (R² of 0.9406 to 0.9964), highlighting the future potential for building foundations for artificial intelligence in VPP, the streamlining of photoresin formulation design and transforming the way acrylates are characterized, selected and used.