Green manufacturing has been becoming more critical in response to changing a demand for manufacturing sustainability from lower carbonization to strict decarbonizaton. This paper extends our previous work on energy awareness in scheduling and investigates the relation between energy efficiency and productivity based on the physically measured data including power consumption and uncertainties that can be observed through the developed manufacturing physical simulator. It is also demonstrated, through physical scheduling simulations in a brute-force way, that our manufacturing simulator is capable of evaluating energy-efficient operations of manufacturing systems.
A method to design the two-dimensional shapes of patterns of two piece brassiere cup is proposed. A brassiere cup consists of several patterns and their shapes are designed by repeatedly making a paper cup model and then checking its three-dimensional shape. This process requires much trial and error and would benefit from an improvement in design efficiency. The form of a brassiere is characterized by two curves: the ridge line and the lower edge of the cup. If their three-dimensional shapes are determined, the two-dimensional shape of a pattern must be designed so that its edges coincide with individual curves. When the two-dimensional shape of an edge of a paper pattern and the three-dimensional shape of a target curve are given, the surface of the pattern is uniquely determined. This means that the surface of the pattern is obtained from one edge and one target curve and another surface of the pattern is obtained from the other edge and the other target curve. Then, the pattern can be designed by optimizing both shapes of edges so that both surfaces completely overlap each other. It was experimentally verified that the edges of a designed pattern formed the target shapes.
Recently, the environment of manufacturing industries greatly changes according to diversification of users’ demands and production machines’ condition, and then the effective scheduling method to deal with changes of various situation is required. In this paper, a scheduling method for realizing the mass-customization is proposed using a combinatorial auction technique which is one of the autonomous distributed optimization method. The users’ demands, the number of running machines and the equalization of the running machines’ usage ratio each term are considered in the proposed method. Computational experiments are performed for evaluating the effectiveness of the proposed method. The suitable schedules in consideration of the users’ demands and machines’ conditions are acquired using the proposed method. Thus, the proposed method has the potential to make the effective scheduling in realization of mass-customization.
According to the change of manufacturing style from mass production to customized production, it is important to manage the process plan and to control the machining quality of products. On machine measurement (OMM) has an advantage that it eliminates the operations of remove and reattachment of the workpiece and eliminates the positioning error at the reattachment. The contact measurement using a touch probe is one of the OMM. However, this measurement requires decision of measuring points and paths in order to conduct the measurement using touch probe. Currently, measuring paths of the touch probe is generated by an operator who can recognize the shape of the workpiece, determine the measuring regions and the measuring points. The objective of this study is automatically generation of the NC program to instruct OMM using a touch probe. This study realizes automatically generation of the NC program to instruct OMM by focusing on recognition of the geometrical property of the product shape based on the removal volume and determination of the measuring points and paths.
This paper proposes a concept of the task-oriented software synergy (TOSS) for anthropomorphic hands and designs a control system of multi-fingered hand that realizes TOSS. By using TOSS, we can perform various tasks with fewer principal components than those of conventional synergies by switching synergy corresponding to each task. We show that TOSS can be realized with lower approximation error and higher contribution rate than the conventional synergy for a given task. Moreover, we verified through physics simulation that an adverse effect of dimensionality reduction is smaller than conventional synergy. These results show that, by using TOSS, we can properly perform various tasks with fewer inputs.