This manuscript reports on a vision-based autonomous grain unloading system for a head-feeding combine robot. In order to perform harvesting fully autonomously, the combine robot is also required to autonomously unload the harvested grain into a wagon through the unloading auger, which is a screw auger and modelled as a 2-DOF manipulator. We presented a vision-based method for the autonomous unloading. A machine vision system was developed that consists of a camera on the combine robot and a planar target on the wagon. Based on SURF, the robot locates the wagon and positions its auger spout at an appropriate point over the wagon. We conducted field experiments to evaluate the presented method. Experimental results showed that the combine robot was able to position its auger spout at the predetermined target point with tolerable accuracy.

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We consider an integrated optimization model for production planning and warehouse storage allocation under demand uncertainty. A deterministic problem of the integrated production planning and warehouse storage allocation problem is extended into a stochastic programming problem formulation. A Lagrangian relaxation and fix heuristic is proposed to solve the mixed integer nonlinear programming problem by decomposing the problem into single item capacitated lot sizing and warehouse storage allocation problems. The problem instances based on real data are solved in the numerical experiments. Computational results demonstrate the effectiveness of the proposed method.

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In recent years, efficient and agile manufacturing is aimed at in many manufacturing industries. In sheet metal processing, reducing the waste of raw materials and managing the suitable production schedule are the important factors for such manufacturing. Nesting is executed to determine the optimal layout for raw material. And scheduling is executed to determine the suitable schedule. Generally, nesting and scheduling are executed separately because their optimization objectives are different. However there occurs a certain trade-off between nesting and scheduling. Therefore, those problems should be considered simultaneously in order to increase the efficiency of entire manufacturing activity. Under such understanding, in this study, we propose an nesting method with considering schedule. In this method, we apply Genetic Algorithm to solve the nesting problem. We propose a new fitness function which considers both reduction of waste material and minimizing total tardiness. Finally, we verify the effectiveness of the proposed method through some computational experiments.

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Dynamic changes as machine breakdowns make current production plan infeasible and inaccurate. The influence of dynamic changes spread to whole production planning that consists of several domains such as process planning, production scheduling and so on. In this study, solution space in the production planning is defined as comprehensive. We have applied Zero-Suppressed Binary Decision Diagrams (ZDDs) for the representation of solution candidates that satisfy constraints between such domains so for. In dynamic production planning problems, manufacturing achievements and dynamic changes may make solution space change. In this paper, we define such events as constraints for ZDDs and propose an approach to update the ZDD to deal with the events. Moreover,we propose a genetic algorithm (GA) to find solutions in the ZDD. Experimental results demonstrate that the proposed approach to address such event with ZDDs and the GA are effriciently used to solve the dynamic production planning problem.

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Recent industrial globalization have increased importance of functional verification in product design for various ways / situations of use. In particular, for shortening development period, it is desired to perform functional verification and necessary modification of the design plan in the conceptual design phase. This paper describes a framework for such individual functional verification that can be applied to the conceptual design phase. A study with simple examples focusing on definition of function indicated that ways / situations of use affect functions indirectly through behavior of the product. This result implies the functional verification can be performed by modeling functions,behavior and physical phenomena caused by a way / situation of use respectively, by integrating those models and then by analyzing behavior that occurs on the integrated model. This paper presents a method based on graph modeling approach. Those three elements are modeled by function decomposition tree, Petri net and cause-and-effect graph, respectively. The designer relates the function and behavior models by interpreting transitions as sub-functions. The phenomena model is transformed into a Petri net and then integrated with the behavior model by specific places and transitions. This integration can make some behavior infeasible and shows the corresponding function cannot be satisfied. This method was applied to an example and its potential was proven.

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