Khaled N. Alqahtani

Due to its near-net-shape manufacturing and ability to treat challenging-to-manufacture materials such as titanium alloys, Additive manufacturing (AM) is growing in popularity. However, due to the poor surface quality of AM components, finishing processes such as machining are required. One of the most difficult aspects of finishing AM components is the fact that even when using the same machining parameters, the surface roughness can vary significantly depending on the orientation of the part. In this study, electron beam melting (EBM) Ti6Al4V parts are subjected to the finishing (milling) process in three potential orientations relative to the direction of the tool feed. The impact of the feed rate, radial depth of cut, and cutting speed on the surface roughness and cutting force of the Ti6Al4V EBM part is studied while taking the orientations of the EBM components into consideration. It is found that the machined surface changes in noticeable ways with respect to orientation. A factorial design is used for the experiments, and analysis of variance (ANOVA) is used to evaluate the results. Furthermore, the grey relational analysis (GRA) method coupled with entropy weights is utilized to determine the optimal process variables for machining a Ti6Al4V EBM component. The results show that the feed rate has the greatest impact on the multi-response optimization, followed by the cutting speed, faces, and radial depth of cut.

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.

Internet of things (IoT) applications, which include environmental sensors and control of automated manufacturing systems (AMS), are growing at a rapid rate. In terms of hardware and software designs, communication protocols, and/or manufacturers, IoT devices can be extremely heterogeneous. Therefore, when these devices are interconnected to create a complicated system, it can be very difficult to detect and fix any failures. This paper proposes a new reliability design methodology using “colored resource-oriented Petri nets” (CROPNs) and IoT to identify significant reliability metrics in AMS, which can assist in accurate diagnosis, prognosis, and resulting automated repair to enhance the adaptability of IoT devices within complicated cyber-physical systems (CPSs). First, a CROPN is constructed to state “sufficient and necessary conditions” for the liveness of the CROPN under resource failures and deadlocks. Then, a “fault diagnosis and treatment” technique is presented, which combines the resulting network with IoT to guarantee the reliability of the CROPN. In addition, a GPenSIM tool is used to verify, validate, and analyze the reliability of the IoT-based CROPN. Comparing the results to those found in the literature shows that they are structurally simpler and more effective in solving the deadlock issue and modeling AMS reliability.