Comparison and Evaluation of Deadlock Prevention Methods for Different Size Automated Manufacturing Systems

In automated manufacturing systems (AMSs), deadlocks problems can arise due to limited shared resources. Petri nets are an effective tool to prevent deadlocks in AMSs. In this paper, a simulation based on existing deadlock prevention policies and different Petri net models are considered to explore whether a permissive liveness-enforcing Petri net supervisor can provide better time performance. The work of simulation is implemented as follows. (1) Assign the time to the controlled Petri net models, which leads to timed Petri nets. (2) Build the Petri net model using MATLAB software. (3) Run and simulate the model, and simulation results are analyzed to determine which existing policies are suitable for different systems. Siphons and iterative methods are used for deadlocks prevention. Finally, the computational results show that the selected deadlock policies may not imply high resource utilization and plant productivity, which have been shown theoretically in previous publications. However, for all selected AMSs, the iterative methods always lead to structurally and computationally complex liveness-enforcing net supervisors compared to the siphons methods. Moreover, they can provide better behavioral permissiveness than siphons methods for small systems. For large systems, a strict minimal siphon method leads to better behavioral permissiveness than the other methods

Confusion Control in Generalized Petri Nets Using Synchronized Events

The loss of conflicting information in a Petri net (PN), usually called confusions, leads to incomplete and faulty system behavior. Confusions, as an unfortunate phenomenon in discrete event systems modeled with Petri nets, are caused by the frequent interlacement of conflicting and concurrent transitions. In this paper, confusions are defined and investigated in bounded generalized PNs. A reasonable control strategy for conflicts and confusions in a PN is formulated by proposing elementary conflict resolution sequences (ECRSs) and a class of local synchronized Petri nets (LSPNs). Two control algorithms are reported to control the appeared confusions by generating a series of external events. Finally, an example of confusion analysis and control in an automated manufacturing system is presented

Experimental Analysis on the Influence and Optimization of μ-RUM Parameters in Machining Alumina Bioceramic

Fabrication of precise micro-features in bioceramic materials is still a challenging task. This is because of the inherent properties of bioceramics, such as low fracture toughness, high hardness, and brittleness. This paper places an emphasis on investigating the multi-objective optimization of fabrication of microchannels in alumina (Al2O3) bioceramics by using rotary ultrasonic machining (RUM). The influence of five major input parameters, namely vibration frequency, vibration amplitude, spindle speed, depth of cut, and feed rate on the surface quality, edge chipping, and dimensional accuracy of the milled microchannels was analyzed. Surface morphology and microstructure of the machined microchannels were also evaluated and analyzed. Unlike in previous studies, the effect of vibration frequency on the surface morphology and roughness is discussed in detail. A set of designed experiments based on central composite design (CCD) method was carried out. Main effect plots and surface plots were analyzed to detect the significance of RUM input parameters on the outputs. Later, a multi-objective genetic algorithm (MOGA) was employed to determine the optimal parametric conditions for minimizing the surface roughness, edge chipping, and dimensional errors of the machined microchannels. The optimized values of the surface roughness (Ra and Rt), side edge chipping (SEC), bed edge chipping (BEC), depth error (DE), and width error (WE) achieved through the multi-objective optimization were 0.27 μm, 2.7 μm, 8.7 μm, 8 μm, 5%, and 5.2%, respectively

Developing a Model for Safety Risk Assessment under Uncertainty for the Manufacturing Industry: A Case Study of Pole Factory Hazards in Riyadh, Saudi Arabia

Many occupational injuries occur in the manufacturing industry due to hazardous events. The available studies and statistics on occupational safety in the Kingdom of Saudi Arabia demonstrate the need for improving the work environment by introducing effective techniques for analyzing and assessing safety risks to control the most hazardous events. This study aims to develop a general model for assessing safety risks by integrating Monte Carlo simulation (MCS) and fuzzy set theory (FST) to overcome the uncertainty and unavailability of data on the severity and likelihood of hazards. MCS uses the ModelRisk software for modeling hazards that exhibit randomness and uncertainty and have historical data. In contrast, FST uses a Matlab code to assess expert judgment about hazards featuring epistemic uncertainty or unavailable historical data. The Al-Babtain Pole Factory in Riyadh was selected as a case study in the manufacturing environment to prove the applicability and effectiveness of the developed model. From the 371 hazards identified using the Occupational Health and Safety Assessment Series 18001, only five were analyzed using the two model techniques. The likelihood and severity of these five hazards were collected and analyzed to obtain the risk levels. A list of hazards and their processing priorities were then produced. According to the risk values calculated using both techniques, Hazard5 was found to be the most hazardous event, followed by Hazard1. The results of the proposed model demonstrated the distributions, statistics, percentiles, and risk limits for the selected hazards. These outputs support decision-making and increase the effectiveness and flexibility of safety risk assessments, which means that the proposed model is reliable and applicable for SRA under uncertainty and data unavailability in the manufacturing industry

Another Approach to Characterize Particle Distribution during Surface Composite Fabrication Using Friction Stir Processing

Surface composite fabrication through Friction Stir Processing (FSP) is evolving as a useful clean process to enhance surface properties of substrate. Better particle distribution is key to the success of surface composite fabrication which is achieved through multiple passes. Multiple passes significantly increase net energy input and undermine the essence of this clean process. This study proposes a novel approach and indices to relate the particle distribution with the FSP parameters. It also proposes methodology for predicting responses and relate the response with the input parameter. Unit stirring as derived parameter consisting of tool rotation speed in revolutions per minute (rpm), traverse speed and shoulder diameter was proposed. The particle distribution was identified to be achieved in three stages and all three stages bear close relationship with unit stirring. Three discrete stages of particle distribution were identified: degree of spreading, mixing and dispersion. Surface composite on an aerospace grade aluminum alloy AA7050 was fabricated successfully using TiB2 as reinforcement particles. FSP was performed with varied shoulder diameter, rotational speed and traversing speed and constant tool tilt and plunge depth using single pass processing technique to understand the stages of distribution. Significant relationships between processing parameters and stages of particle distribution were identified and discussed

An Order Effect of Neighborhood Structures in Variable Neighborhood Search Algorithm for Minimizing the Makespan in an Identical Parallel Machine Scheduling

Variable neighborhood search (VNS) algorithm is proposed for scheduling identical parallel machine. The objective is to study the effect of adding a new neighborhood structure and changing the order of the neighborhood structures on minimizing the makespan. To enhance the quality of the final solution, a machine based encoding method and five neighborhood structures are used in VNS. Two initial solution methods which were used in two versions of improved VNS (IVNS) are employed, namely, longest processing time (LPT) initial solution, denoted as HIVNS, and random initial solution, denoted as RIVNS. The proposed versions are compared with LPT, simulated annealing (SA), genetic algorithm (GA), modified variable neighborhood search (MVNS), and improved variable neighborhood search (IVNS) algorithms from the literature. Computational results show that changing the order of neighborhood structures and adding a new neighborhood structure can yield a better solution in terms of average makespan