Reinforcement, undercut, and root drop-through during laser hybrid arc welding of steel were studied in dependence of gap width, welding speed, and wire feeding rate. Generalized trends were obtained through design of experiments. Most of the trends could be explained by a mass balance while some parameter impacts relied on more complex mechanisms. In particular, different levels of complexity of parameter dependencies were distinguished, ranging from monotonous behaviour to maxima and to changing signs of the trends. The findings are of high practical relevance to optimize the process with respect to the weld quality. Moreover, the potential and limits of the design of experiments method, of a mass balance, and of the matrix flow chart method are discussed.

Model-checking techniques are often used for theverification of software systems. Such techniques areaccompanied with several advantages. However, state-spaceexplosion is one of the drawbacks to model checking. Duringrecent years, several methods have been proposed based onevolutionary and meta-heuristic algorithms to solve thisproblem. In this paper, a hybrid approach is presented to copewith the SSE problem in model checking of systems modeled byGTS with an ample state space. Most of existence proposedmethods that aim to verify systems are applied to detectdeadlocks by graph transformations. The proposed approach isbased on the fuzzy genetic algorithm and is designed to declinethe safety property by verifying the reachability property anddetecting deadlocks. In this solution, the state space of thesystem is searched by a fuzzy genetic algorithm to find the statein which the specified property is refuted/verified. To implementand evaluate the suggested approach, GROOVE is used as apowerful designing and model checking toolset in GTS. Theexperimental results indicate that the presented hybrid fuzzymethod improves speed and performance by comparing othertechniques.

model checking techniques are often used for the verification of software systems. Such techniques are accompanied with several advantages. However, state space explosion is one of the drawbacks to model checking. During recent years, several methods have been proposed based on evolutionary and meta-heuristic algorithms to solve this problem. In this paper, a hybrid approach is presented to cope with the SSE problem in model checking of systems modeled by GTS with an ample state space. Most of existence proposed methods that aim to verify systems are applied to detect deadlocks by graph transformations. The proposed approach is based on the fuzzy genetic algorithm and is designed to decline the safety property by verifying the reachability property and detecting deadlocks. In this solution, the state space of the system is searched by a fuzzy genetic algorithm to find the state in which the specified property is refuted/verified. To implement and evaluate the suggested approach, GROOVE is used as a powerful designing and model checking toolset in GTS. The experimental results indicate that the presented hybrid fuzzy method improves speed and performance by comparing other techniques.

Nowadays, model checking is applied as an accurate technique to verify software systems. The main problem of model checking techniques is the state space explosion. This problem occurs due to the exponential memory usage by the model checker. In this situation, using meta-heuristic and evolutionary algorithms to search for a state in which a property is satisfied/violated is a promising solution. Recently, different evolutionary algorithms like GA, PSO, etc. are applied to find deadlock state. Even though useful, most of them are concentrated on finding deadlock. This paper proposes a fuzzy algorithm in order to analyze reachability properties in systems specified through GTS with enormous state space. To do so, we first extend the existing PSO algorithm (for checking deadlocks) to analyze reachability properties. Then, to increase the accuracy, we employ a Fuzzy adaptive PSO algorithm to determine which state and path should be explored in each step to find the corresponding reachable state. These two approaches are implemented in an open-source toolset for designing and model checking GTS called GROOVE. Moreover, the experimental results indicate that the hybrid fuzzy approach improves speed and accuracy in comparison with other techniques based on meta-heuristic algorithms such as GA and the hybrid of PSO-GSA in analyzing reachability properties.

model checking techniques are often used for the verification of software systems. Such techniques are accompanied with several advantages. However, state space explosion is one of the drawbacks to model checking. During recent years, several methods have been proposed based on evolutionary and meta-heuristic algorithms to solve this problem. In this paper, a hybrid approach is presented to cope with the SSE problem in model checking of systems modeled by GTS with an ample state space. Most of existence proposed methods that aim to verify systems are applied to detect deadlocks by graph transformations. The proposed approach is based on the fuzzy genetic algorithm and is designed to decline the safety property by verifying the reachability property and detecting deadlocks. In this solution, the state space of the system is searched by a fuzzy genetic algorithm to find the state in which the specified property is refuted/verified. To implement and evaluate the suggested approach, GROOVE is used as a powerful designing and model checking toolset in GTS. The experimental results indicate that the presented hybrid fuzzy method improves speed and performance by comparing other techniques