Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed parallel Machine scheduling by consideration of Failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, Failure time, and Machine availability after repair and maintenance. Failure times have been predicated on using Machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five Machines. This research addresses the problem in two parts: the first part predicts Failures, and the second part includes the sequence of parallel Machine scheduling operations. After the previous data were received in the first step, Machine Failure was predicted by using Machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, scheduling mode was used to determine operations sequence by consideration of these Failures and Machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and Failures. This study used the Light GBM algorithm and achieved 85% precision in Failure prediction. The rules obtained from this algorithm contributed to cost reduction.

In this study, the Failure analysis of base plate bolts of radial forging Machine is investigated. Premature Failure had occurred from the bolts shank-head fillet and threads zones. Hardness, impact and tensile tests are carried out to investigate the mechanical properties and spectrophotometer is used to evaluate the bolts chemical composition. Optical Microscope (OM) and Scanning Electron Microscope (SEM) are used for the investigation of microstructure, defects, fracture surface and Failure causes. The fracture surface morphology shows that the crack growth consisted of bolts shank-head fillet and threads zones including the initiation zone, fatigue crack growth zone along with the beach marks and ratchet steps and the rapid final fracture zone. Stress analysis shows that the amount of pre-tightening selected lower than the proposed value leads to the joint loosening and shortens the bolt’ s fatigue life. In addition, based on the paper results, the existing flowchart for component fabrication is analyzed and a flowchart based on research field is presented to enhance the quality of radial forging Machine parts.

Machines are a key element in the production system and their Failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of Machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/Machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in smallsized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

Couplings are widely used in the industry and this equipment are always subject to defects and Failures due to continuous rotation. Vibration analysis is a suitable technique for Failure analysis and Failure detection of rotating equipment. The purpose of this research is to analyze the Failures that occurred in a coupling, whose data was collected in normal state and three Failure states with four sensors connected to the coupling. For this purpose, two different types of feature extraction have been used, and seven Machine learning algorithms and one deep learning algorithm have been used to classify situations. In this research, the performance of each of the implemented algorithms and the importance of extracted features have been investigated, and the role of sensors and their importance to reduce the number of sensors have been investigated. From the results of this research, we can point out the high importance of the features of the frequency domain in the accuracy of the implemented models, as well as the high efficiency of two sensors for classification.