Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

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.

This paper proposes a deadlock prevention policy for flexible manufacturing systems (FMSs) based on a think-globally-act-locally approach and a structurally minimal method. First, by using the think-globally-act-locally approach, a global idle place is temporarily added to a Petri net model with deadlocks. Then, at each iteration, an integer linear programming problem is formulated to design a minimal number of maximally permissive control places. Therefore, a supervisor with a low structural complexity is obtained since the number of control places is

The Ladder Diagram (LD) is an industry-standard programming language for constructing automated manufacturing systems (AMSs) control algorithms. Petri net (PN) theory, on the other hand, is a mathematical and graphical modeling tool for AMSs. Multiple types of PN-based LDs are designed for AMSs with highly complicated LD structures. Thus, it is necessary to propose a technique that can assist in the minimization of the structural complexity of LDs. The main purpose of this study is to propose a methodology for the implementation of LDs in AMSs. First, a colored resource-oriented Petri net (CROPN) is developed for modeling and guaranteeing the deadlock-free behavior of AMS. Second, a ladder diagram CROPN (LDCROPN) is constructed in order to transform the CROPN into an LD. The proposed LDCROPN is assessed using instances from the literature. The results show that the LDCROPN is effective, has a simpler structure, and has less computational overhead than existing techniques.

Electron beam melting (EBM) is one example of a 3D printing technology that has shown great promise and advantages in the fabrication of medical devices such as dental and orthopedic implants. However, these products require high surface quality control to meet the specifications; thus, post-processing, such as with machining processes, is required to improve surface quality. This paper investigates the influence of two-part orientations of Ti6Al4V EBM parts on the CNC machining (turning) process. The two possible EBM part orientations used in this work are across EBM layers (AL) and parallel to the EBM layer (PL). The effect of the EBM Ti6Al4V part orientations is examined on surface roughness, power consumption, chip morphology, tool flank wear, and surface morphology during the dry turning, while using uncoated carbide tools at different feed rates and cutting speeds. The results showed that the AL orientation had better surface quality control and integrity after machining than PL orientation. Using the same turning parameters, the difference between the roughness (Ra) value for AL (0.36 μm) and PL (0.79 μm) orientations is about 54%. Similarly, the power consumption in AL orientation differs by 19% from the power consumption in PL orientation. The chip thickness ratio has a difference of 23% between AL and PL orientations, and the flank wear shows a 40% difference between AL and PL orientations. It is found that, when EBM components are manufactured along across-layer (AL) orientations, the impact of part orientation during turning is minimized and machined surface integrity is improved.