Optimized selection of nanohydroxyapatite‐filled dental restorative composites formulation for best physico‐mechanical, chemical, and thermal properties using hybrid analytical hierarchy process‐multi‐objective optimization on the basis of ratio analysis approach

Abstract

The present research explored and optimized dental restorative composite (DRC) materials filled with nanohydroxyapatite (n-HAP) based on their physico-mechanical, chemical, and thermal properties. In the same context, hybrid analytical hierarchy process (AHP) and multi-objective optimization on the basis of ratio analysis (MOORA) approach was proposed to select the best DRC with optimal combination of physico-mechanical, chemical, and thermal properties. Seven formulations based on Bis-GMA as a base monomer with APTES ((3-aminopropyl) triethoxysilane) and MPTS ((3-methacryloxypropyl) trimethoxy silane) silane-treated n-HAP as a filler were developed and examined for different characteristics. The experiments performed for evaluating various properties of fabricated DRCs formulations are as per the ASTM and ISO 4049 standards. The experimental results proved that APTES-and MPTStreated n-HAP enhances the physico-mechanical as well as chemical and thermal performance significantly. The DRCs show better sorption and solubility behavior when silane-treated n-HAP filler increases and remains in the acceptable range. The optimization of prepared samples was carried out in two phases: (a) finding weight of each attributes using AHP approach and (b) ranking the samples with MOORA approach. The hybrid AHP-MOORA results depict that optimal combination of physical, mechanical, chemical, and thermal characteristics was exhibited by the DRC sample with 8 wt% MPTStreated n-HAP filler.

Dental resin composites: A review on materials to product realizations

“…The combination of these base and diluent monomers has been extensively investigated to improve the mechanical and physical properties of the composites and contribute to extending the dental material industry. A systematic investigation of material property prediction models has been developed to optimize input parameters such as weight fraction and concentration of monomers [67][68][69] and fillers [70]. To date, there are two major types of DRCs classified according to handling characteristics: flowable and packable materials.…”

Experimental analysis with grey relational analysis–analytic hierarchy process‐based hybrid optimization to analyze the effect of graphene nanoparticle reinforced ultra‐high molecular weight polyethylene/n‐HAp for hip joint

“…It provides a high degree of crystalline nature and structural stability to the polymer matrix, which makes it more attractive. [12,13] The high viscosity of UHMWPE when mixed with filler materials results in the clustering of filler materials, which increases void and defects in composite materials. [14] Finally, biocomposite materials have low mechanical strength.…”

Investigation and optimization of Cracked Aluminium Alloy plate Restored for Fatigue Loading Application

“…MOORA technique was used to determine the process parameters like pulse on time, voltage and wire feed rate, metal removal rate and surface roughness for aluminium (A6061) reinforced zinc oxide particle metal matrix (Sadhasivam et al, 2021). Alternative approach of response surface method of experimental design was also used for optimisation (Gangwar et al, 2021;Patil, 2017). It has been observed that researchers focused over optimising fracture toughness and other properties in the field of material science.…”