Mustafa M. Nasr

The practical applications of integrated maintenance policies and quality for a multi-component system are more complicated, still rare, and incomplete to meet the requirements of Industry 4.0. Therefore, this work aims to extend the integration economic model for optimizing maintenance policies and quality control parameters by incorporating the Taguchi loss function for a multi-component system. An optimization model is developed based on preventive maintenance, corrective maintenance policies, and quality control parameters with the CUSUM (Cumulative Sum) chart, which is widely used for detecting small shifts in the process mean. The model was developed to minimize the expected total cost per unit of time and to obtain the optimal values of decision variables: the size of samples, sample frequency, decision interval, coefficient of the CUSUM chart, and preventive and corrective maintenance intervals. The solution steps were employed by selecting a case study in the Alahlia Mineral Water Company (AMWC). Then, the design of experiments based on one-factor-at-a-time was used to evaluate the effect of selected decision variables on the expected total cost. Finally, sensitivity analysis was performed on the selected decision variables to demonstrate the robustness of the developed model. A predictive maintenance plan was developed based on the optimal value of preventive maintenance interval, and the results showed that the performance of the maintenance plan realizes the full potential of the integrated model. In addition, the case study results indicate that the extended integrated model for multicomponent is the new standard for the quality production of multi-component systems in future works.

Studies about adding graphene reinforcement to improve the microfabrication performance of alumina (Al2O3) ceramic materials are still too rare and incomplete to satisfy sustainable manufacturing requirements. Therefore, this study aims to develop a detailed understanding of the effect of graphene reinforcement to enhance the laser micromachining performance of Al2O3-based nanocomposites. To achieve this, high-density Al2O3 nanocomposite specimens were fabricated with 0 wt.%, 0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.% graphene nanoplatelets (GNPs) using a high-frequency induction heating process. The specimens were subjected to laser micromachining. Afterward, the effects of the GNP contents on the ablation depth/width, surface morphology, surface roughness, and material removal rate were studied. The results indicate that the micro-fabrication performance of the nanocomposites was significantly affected by the GNP content. All nanocomposites exhibited improvement in the ablation depth and material removal rate compared to the base Al2O3 (0 wt.% GNP). For instance, at a higher scanning speed, the ablation depth was increased by a factor of 10 times for the GNP-reinforced specimens compared to the base Al2O3 nanocomposites. In addition, the MRRs were increased by 2134%, 2391%, 2915%, and 2427% for the 0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.% GNP/Al2O3 nanocomposites, respectively, compared to the base Al2O3 specimens. Likewise, the surface roughness and surface morphology were considerably improved for all GNP/Al2O3 nanocomposite specimens compared to the base Al2O3. This is because the GNP reinforcement reduced the ablation threshold and increased the material removal efficiency by increasing the optical absorbance and thermal conductivity and reducing the grain size of the Al2O3 nanocomposites. Among the GNP/Al2O3 nanocomposites, the 0.5 wt.% and 1 wt.% GNP specimens showed superior performance with minimum defects in most laser micromachining conditions. Overall, the results show that the GNP-reinforced Al2O3 nanocomposites can be machined with high quality and a high production rate using a basic fiber laser system (20 Watts) with very low power consumption. This study shows huge potential for adding graphene to alumina ceramic-based materials to improve their machinability.