Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented.

Social media networking platforms connect people living in every corner of the world. Twitter has now become a popular microblogging service, allowing users to express themselves and keep up with current events. Twitter has attracted spammers because to its popularity and ease of use. As a result, spam identification (ID) has become one of the most pressing issues. It is vital to detect and filter spam tweets as well as their owners in order to provide a spam-free environment. In this article, a spam detection method is proposed using a swarm optimization approach on a tweet-by-tweet basis. A spam tweet detection dataset is used to train the machine learning (ML) model. Metaheuristic features are created based on the input features in the dataset. Whale swam optimization algorithm (WOA) is used to select the important features before classification. The conventional objective function of WOA is modified into stochastic gradient descent (SGD) to perform feature selection. The selected subset of features is used to train the Adaboost (AB) classifier to detect the spam in the tweets. The AB classifier produced the best results in combination with WOA and SGD. The obtained accuracy is 99.85% in testing with a minimum subset of seven features and in the least possible minimum time of 17.9 s.

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.

Additive manufacturing (AM) is gaining popularity as it can produce near-net geometries and work with difficult-to-manufacture materials, such as stainless steel 316L. However, due to the low surface quality of AM parts, machining and other finishing methods are required. Laser powder bed fusion (LPBF) components can be difficult to finish as the surface roughness (Sa) can vary greatly depending on the part’s orientation, even when using the same machining parameters. This paper explored the effects of finishing (milling) SS 316L LPBF components in a variety of part orientations. The effect of layer thickness (LT) variation in LPBF-made components was also studied. LPBF parts of 30, 60, 80, and 100 μm layer thicknesses were created to analyze the effect of the LT on the final milling process. Additionally, the effect of cutting speed during the milling process on the surface roughness of the SS 316L LPBF component was investigated, along with the orientations and layer thicknesses of the LPBF components. The results revealed that the machined surface undergoes significant orientation and layer thickness changes. The investigations employed a factorial design, and analysis of variance (ANOVA) was used to analyze the results. In addition, an artificial neural network (ANN) model was combined with particle swarm optimization (denoted as ANN-PSO) and the genetic algorithm (denoted as ANN-GA) to determine the optimal process conditions for machining an SS 316L LPBF part. When milled along (Direction B) an orientation with a cutting speed of 80 m/min, the LPBF component produced, with a layer thickness of 60 μm, achieves the lowest surface roughness. For instance, the Sa of a milled LPBF part can be as low as 0.133 μm, compared to 7.54 μm for an as-fabricated LPBF part. The optimal surface roughness was 0.155 μm for ANN-GA and 0.137 μm for ANN-PSO, whereas the minimal surface roughness was experimentally determined to be 0.133 μm. Therefore, the surface quality of both hybrid algorithms has improved, making them more efficient.

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.

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.