This paper proposes an optimal button arrangement of a percussion drill bit and its operating condition to improve drilling efficiency. A new evaluation method is introduced for the button arrangement that utilizes the superimposed impact area, blank area, and drilling deviation moment as the quantitative indices to evaluate the impact of buttons on the rock surface. To determine the optimal button arrangement and its operating conditions, a progressive metamodel-based design optimization was conducted using the new evaluation indices as the analysis response, and then the optimal solution was determined through iteration. Consequently, all the button evaluation indices were reduced significantly and the impact areas were distributed uniformly under a specific operating condition. Additionally, the drilling performances of the optimal button arrangement were investigated according to the operating conditions to obtain the maximum drilling performance in terms of the drilling machine operation.

In addition to the textile industry, unwinding of cable or fiber is used in various fields such as electronics, communication, and guided weapons. The cable released from the package exhibit a complicated behavior, entailing a combination of rotational and translational motion. This causes problems such as entangling and fracture. Therefore, it is necessary to study boundary and adhesion conditions to prevent unwinding failure. In this study, an experimental device for the analysis of cable unwinding was developed, and unwinding behavior was analyzed experimentally under various unwinding conditions. The experimental device comprises a jig for high-speed camera measurements, control device, and cable unwinding device. Cable behavior was analyzed according to the unwinding velocity and the distance between the fiber package and the point where the fiber was released. In addition, unwinding behavior with respect of the tension acting on the cable was analyzed experimentally by applying the adhesive.

Waterjet is a device that cuts or crushes materials using water pressure injected through the nozzle. Especially, Low pressure waterjet is used in stripping and cleaning work. The cleaning patterns of the low pressure waterjet is determined by various design variables, such as the number of nozzles, an angle of nozzles, gear ratio and so on. In order to optimize the cleaning pattern, the optimum waterjet design is required depending on the shape of the target structure. To do this, a large number of waterjet analysis models should be used. This study reduced design time by automating the creation of the desired analysis model with simple changes in design variables, and conducted evaluation of the cleaning patterns using numerical method for the most frequently used cylindrical structures. In addition, it analyzed the effects of changes in design variables and suggested improvements. Moreover, the idea of a module type waterjet was proposed to reduce the cost of waterjet replacement due to usage changes. This study can be used to design the waterjet, which suits a particular purpose.

Recent advances in additive manufacturing have provided more freedom in the design of metal parts; hence, the prototyping of fluid machines featuring extremely complex geometries has been investigated extensively. The fabrication of fluid machines via additive manufacturing requires significant attention to part stability; however, studies that predict regions with a high risk of collapse are few. Therefore, a novel algorithm that can detect collapse regions precisely is proposed herein. The algorithm reflects the support span over the faceted surface via propagation and invalidates overestimated collapse regions based on the overhang angle. A heat exchanger model with an extremely complex internal space is adopted to validate the algorithm. Three samples from the model are extracted and their prototypes are fabricated via laser powder bed fusion. The results yielded by the fabricated samples and algorithm with respect to the sample domain are compared. Regions of visible collapse identified on the surface of the fabricated samples are predicted precisely by the algorithm. Thus, the supporting span reflected by the algorithm provides an extremely precise prediction of collapse.