The turning, which consists of the removal of metal from the outer diameter of a rotating cylindrical workpiece, is one of the most common techniques for cutting, especially when finishing the product. The values of the cutting parameters, such as feed rate, cutting speed, and depth of cut, in a turning operation must be selected carefully to improve the profit of operations by enhancing productivity and reducing the total manufacturing cost for each component. A high vibration leads to poor surface finish and reduced productivity and shortens the tool life; therefore, this parameter should be controlled. In this study, an experiment is conducted to investigate the effects of these cutting parameters in the turning process of a workpiece, composed of AISI 1040 steel, using the response surface method. Statistical tools were used to design the experiments. These parameters are optimized by using analysis of variance, regression, and optimization techniques to achieve the condition of minimum vibration and chip frequency, therefore improving the surface roughness after the turning process.

The Wireless Body Area Network (WBAN) plays a pivotal role in providing ubiquitous computing and has applications in different fields, especially in health monitoring. The advancement in wearable devices has revolutionized the concept of medical services and brought ease to our daily lives. However, the latent threat imposed by attackers has increased concerns related to the security and privacy of patient’s data due to the open nature of the wireless network. The authentication schemes are used to secure patient’s critical data from different types of cyber-attacks. In this paper, we extend our previous work by presenting an anonymous, hybrid authentication scheme that utilized physiological signals in combination with a lightweight cryptographic method to provide robust security against well-known attacks especially key escrow, base station compromise, and untraceability of sessions. The broadly accepted BAN logic is utilized to offer formal proof of mutual authentication and key agreement. The informal verification is performed by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. Furthermore, the comparative analysis of the proposed scheme with peer work highlighted that it accomplished better security at low computational, communicational, energy consumption, and storage overheads.

In this paper, a model for integrating production scheduling and maintenance planning is proposed for flow shop production system. The suggested model in this paper is based on the optimal jobs sequence for jobs that will be processed in multiple machines connected in series. The objective of this study is to find the optimal sequence for jobs while reducing the total production and maintenance costs. The model works by generating an initial solution using longest processing time (LPT) dispatching rule. Then, tabu search algorithm is established to obtain the optimal sequence for jobs. Computational experiments are performed on problems with five serially machines which are assigned to process eight diverse jobs from the same product family. The result is compared with the genetic algorithm optimization technique under individual PM scheme for obtaining superior solutions that has been proved in the literature to be one of the best approach. The computational results show that the recommended approach is qualified over the simulation based genetic algorithm optimization technique.

Reliability modeling and deadlock control have become important issues in automated manufacturing systems (AMSs) with reliable and unreliable resources. Therefore, this paper develops a reliability modelling approach and deadlock control based on colored resource-oriented timed Petri net (CROTPN) and neural networks to find crucial reliability measures in AMSs. In the first step, a CROTPN with considering resource failures is developed to obtain “sufficient and necessary conditions” for the CROTPN liveness. In the second step, a fault diagnosis and treatment approach is proposed that integrates the obtained unreliable net with neural networks to ensure that the system is reliable. Furthermore, a neural unreliable CROTPN is used to analyze the system reliability. A simulation is carried out to illustrate the approach and compare the results to those found in the literature. The developed approach has been proved to be simpler in structure and can solve the deadlock problem and model AMS reliability.

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations.

This paper proposes a genetic algorithm (GA) for scheduling two identical parallel machines subjected to release times and delivery times, where the machines are periodically unavailable. To make the problem more practical, we assumed that the machines are undergoing periodic maintenance rather than making them always available. The objective is to minimize the makespan ($C_{max}$). A lower bound (LB) of the makespan for the considered problem was proposed. The GA performance was evaluated in terms of the relative percentage deviation (RPD) (the relative distance to the LB) and central processing unit (CPU) time. Response surface methodology (RSM) was used to optimize the GA parameters, namely, population size, crossover probability, mutation probability, mutation ratio, and pressure selection, which simultaneously minimize the RPD and CPU time. The optimized settings of the GA parameters were used to further analyze the scheduling problem. Factorial design of the scheduling problem input variables, namely, processing times, release times, delivery times, availability and unavailability periods, and number of jobs, was used to evaluate their effects on the RPD and CPU time. The results showed that increasing the release time intervals, decreasing the availability periods, and increasing the number of jobs increase the RPD and CPU time and make the problem very difficult to reach the LB.