Honda Motor Co., Ltd.

Tetsuya Yoshino

Shinji Aoki

Masao Shimoyama

Junya Tanabe

Masahiro Yamanaka

A spatter production information storage unit stores spots where spatter has been produced. For the spots where spatter has been produced, a comparison information generation unit acquires weld design information stored in a design information storage unit and weld instruction information stored in an instruction information storage unit, compares both pieces of information, and generates comparison information. An image information output unit outputs the generated comparison information to a display device. The display device displays the comparison information on a screen so as to allow checking by an operator. Hence, it is possible to check, on the screen, comparison information for weld instruction information and weld design information highly likely as a cause for spatter being produced, and to efficiently perform the task of analyzing the causes for spatter being produced.

Spatter analysis method and device

After selecting a robot for carrying out a welding operation at a given welding spot, the cross-section of a welding gun with which that robot is provided and the cross-section of a workpiece at the welding spot are compared. Furthermore, a determination is made as to whether or not welding is possible without the welding gun and the workpiece coming into contact. When it is determined to be impossible, a substitute robot for carrying out the welding of the welding spot is selected. With the present invention, the automatic selection of the robot for carrying out the welding operation for the welding spot becomes possible. Therefore, the number of steps for robot selection can be reduced.

Robot selection method and robot selection device

The present invention relates to a teaching data preparation device and a teaching data preparation method for an articulated robot. Pre-existing position data that approximates new position data is retrieved from a database that, from among data related to working points for a pre-existing workpiece, stores for each working point pre-existing position data that indicates the position of the working point and pre-existing orientation data that indicates the orientation of an end effector at the working point. Next, pre-existing orientation data that is associated with the retrieved pre-existing position data is extracted. Next, incomplete new orientation data is completed using the extracted pre-existing orientation data. As a result of this series of processes, the precision of teaching data can be improved.

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Tochigi-ken, Japan

Tetsuya Yoshino