In this study, an analysis of the influence parameters measured by the static tensile test, thermography and digital image correlation was performed during formation and propagation of the Lüders bands. A new approach to the prediction of stresses, maximum temperature changes and strains during the Lüders band formation and propagation is proposed in this paper. Application of the obtained mathematical models of influence parameters gives a clear insight into the behavior of niobium microalloyed steel at the beginning of the plastic flow, which can improve product quality and reduce costs during the forming of microalloyed steels with the appearance of the Lüders bands. The obtained models of influential parameters during formation and propagation of the Lüders bands have been developed by the regression analysis method. The proposed mathematical models showed low deviations of calculated results ranging from 1.34% to 12.37%.The local stress amounts, important in the forming of microalloyed steels since indicating surface roughness and plastic flow possibilities during the Lüders band propagation, are obtained by the mathematical model. It was found that stress amounts increase during the Lüders band propagation in the area behind the Lüders band front. The difference in stress amount between the start of the Lüders band propagation and advanced Lüders band propagation is 25.53 MPa.
Preliminary communicationThe Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry. When solving complex problems, search based on traditional genetic algorithms has a major drawback -high requirement for computational power. The goal of this research was to develop fast and efficient scheduling method based on genetic algorithm for solving the job-shop scheduling problems. In proposed GA initial population is generated randomly, and the relevant crossover and mutation operation is also designed. This paper presents an efficient genetic algorithm for solving job-shop scheduling problems. Performance of the algorithm is demonstrated in the real-world examples.
Keywords: genetic algorithms; optimization; scheduling; serial production
Učinkoviti genetski algoritam za planiranje proizvodnjePrethodno propćenje Problem planiranja proizvodnje je jedan od najvažnijih ali i najkompleksnijih kombinatornih problema, koji je ujedno i veoma bitan u proizvodnji. Kod rješavanja kompleksnih problema korištenjem uobičajenih genetskih algoritama javlja se potreba za značajnom upotrebom računalnih resursa. Cilj ovog istraživanja je bio razvoj brze i efikasne metode određivanja redoslijeda u proizvodnji bazirane na genetskom algoritmu. U promatranom GA početna populacija se kreira slučajnim odabirom, a predloženi su i modificirani operatori križanja i mutacije. Svojstva testiranog algoritma provjerena su na problemima iz prakse.
Batch sizing and scheduling problems are usually tough to solve because they seek solutions in a vast combinatorial space of possible solutions. This research aimed to test and further develop a scheduling method based on a modified steady-state genetic algorithm and test its performance, in both the speed (low computational time) and quality of the final results as low makespan values. This paper explores the problem of determining the order and size of the product batches in a hybrid flow shop with a limited buffer according to the problem that is faced in real-life. Another goal of this research was to develop a new reliable software/computer program tool in c# that can also be used in production, and as result, obtain a flexible software solution for further research. In all of the optimizations, the initial population of the genetic algorithm was randomly generated. The quality of the obtained results, and the short computation time, together with the flexibility of the genetic paradigm prove the effectiveness of the proposed algorithm and method to solve this problem.
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