Mistake-proof detection method for structural member assembly in manufacturing industries of automobiles, machines or home appliances

Abstract

The invention discloses a mistake-proof detection method for a structural member assembly in manufacturing industries of automobiles, machines or home appliances. The mistake-proof detection method comprises the following steps: I, defining a structural member assembly to be detected; II, setting a detection mold, and arranging the detection mold in detection equipment, wherein the detection mold is matched with the size of the structural member assembly to be detected; III, arranging a plurality of photoelectric switches in the detection equipment; IV, arranging a plurality of RGB digital optic fiber sensors in the detection equipment; V, arranging a plurality of digital laser sensors in the detection equipment; VI, calibrating the RGB digital optic fiber sensors and the digital laser sensors; VII, placing the structural member assembly to be detected in the detection mold, starting the detection equipment and an industrial control computer, analyzing and processing received data by the industrial control computer, and outputting a detection result to a display for displaying. The mistake-proof detection method can be widely applied to automated test systems in the fields of the automobiles, the motorcycles, the machines, the home appliances and the like.

Description

Technical field: [0001] The invention relates to an error-preventing detection method, in particular to an error-proof detection method for structural parts assembly in automobile, machinery or household appliance manufacturing industries. technical background: [0002] The identification of product configuration in conventional production is generally done manually by employees. This method is highly influenced by human factors, unstable, and error-prone. In particular, some structural parts are assembled and assembled from various parts, and there are more than a dozen configurations. Different configurations also have differences in part structure, color and material. Due to the large number of parts and many color matching methods, the differences in color, structure, and material are very small. It is difficult to completely distinguish them through visual inspection by employees during production. Risk of scrapped product, wrong delivery. Invention content: [0003...

AI Technical Summary

Problems solved by technology

[0002] The identification of product configuration in conventional production is generally done manually by employees. This method is highly influenced by human factors, unstable, and error-prone. In particular, some structural parts are assembled and assembled from various parts, and there are more than a dozen configurations. Different configurations also have differences in part structure, color and material

Due to the large number of parts and many color matching methods, the differences in color, structure, and material are very small. It is difficult to completely distinguish them through visual inspection by employees during production. Risk of scrapped products, wrong shipments

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