Study the Structure Characterization of Porcelain Formulation at Different Sludge Content

Porcelain formulation in the form of pellets have been studied by substituted fluxing materials with treated FGD sludge at different percentages. In this work, treated FGD sludge was added in percentages from 5% up to 15% uniaxially pressed at 11 MPa, dried and then sintered at temperature 1200 °C for 3 hours. Weight loss, volume shrinkage/expand, bulk density, densification, porosity and flexural strength were investigated on sintered samples. This demonstrated that the treated FGD sludge addition in porcelain formulation has influenced on the sintered samples. It was concluded that the treated FGD sludge waste could be used as a suitable raw material source for production of porous porcelain ceramic due to their organic and inorganic content.

Effect of Vacuum Annealing on Microstructure and Hot-Salt Corrosion Behavior of CoNiCrAlY/YSZ/LaMgAl11O19 Double-Ceramic Coating

This study investigated the potential effect of vacuum annealing on the microstructure and hot salt corrosion behavior of CoNiCrAlY/YSZ/LaMgAl11O19 (LMA) double-ceramic coatings. A hot-salt corrosion test revealed that sprayed coatings exhibited an unsatisfactory anti-corrosion performance, and the LMA layer underwent severe fracture and corrosion degradation. Vacuum annealing induced a prominent recrystallization of the amorphous phase in LMA layer, triggering severe volume shrinkage and microcrack initiation. The recrystallization and volume shrinkage of the LMA layer were aggravated by an increase in the annealing temperature. The annealed coating with a higher fraction of the LaMA phase showed superior resistance to hot-salt corrosion. However, the salt mixture diffused simultaneously along the microcracks and eventually eroded into the YSZ layer. These results confirmed that vacuum annealing significantly enhanced the hot-salt corrosion resistance of the LMA layer. However, it deteriorated the barrier effect of the salt mixture through microcrack formation.

Multi-objective optimization of the GFRP injection molding process parameters by using GA-ELM, MOFA and GRA-TOPSIS

Due to the influence of injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by the glass fiber-reinforced plastic (GFRP) injection molding. In order to minimize the two defects, the extreme learning machine optimized by genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA) and a multi-objective decision-making method called GRA-TOPSIS are implemented in this study. All experiments based on Latin hypercubic sampling (LHS) are conducted by Moldflow software to obtain results of warpage and volume shrinkage. The prediction accuracy of defect prediction models based on the extreme learning machine (ELM) and GA-ELM algorithm is compared. The results show that GA-ELM models can better predict defect values. Finally, MOFA is utilized to find the Pareto optimal front, and the GRA-TOPSIS method is used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage are effectively reduced by 12.25% and 6.11% compared with those before optimization, respectively, which indicates the effectiveness and reliability of the optimization method.