Fujitsu Laboratories has announced the development of a technology for automatically generating image-recognition programs that accurately detect the positions of components as captured by cameras in automated assembly processes by utilizing images of electronic components and IT equipment.
Problem
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Automatically generated image-processing programs that use machine learning have not been able to detect positions up until now, requiring that experts individually develop image-recognition programs, which has resulted in a lengthy period of time to get a line into operation and making it impossible to respond quickly in the course of operations or to peripheral conditions. As a result, any changes to the manufacturing setup, such as a machine's operating parameters, could involve more than a week's time spent revising the program, during which time the production line would sit idle.
Solution
Fujitsu Laboratories developed a technique for automatically generating image-processing programs that detect positions by controlling the order in which the various image-processing functions that make up a program are combined, and using machine learning based on the similarity of shapes. Samples of the object to be detected are presented as teaching materials, and this makes it possible to automatically generate an image-recognition program in roughly eight hours, or one-tenth the time previously required. In trials to assess positional detection of components during assembly, something that has not previously been amenable to automation, recognition rates, which previously had been stuck below 50%, dramatically improved to 97% or higher. The time required to revise image-recognition programs was also dramatically reduced, to one-tenth the previous time. Additional benefits of this very high recognition rate are that positional deviations during component assembly can be halved and assembly time can be reduced to two-thirds.
Applications
Wide range of automated assembly line requirements such as wafers and solar cell processing and handling.
Platform
To make the machine-learning process more efficient, Fujitsu Laboratories devised three building blocks: the teacher, the grader, and the teaching material.
Availability
September, 2014.
