Abstract
Sheet metal-based manufacturing industries operate on several varieties of sheet metal parts. Previously QR codes stickers were put on sheet metal parts for identification by manual workers as per their respective shape and size features, thus ensuring synchronized raw material flow for the manufacturing process. However, identifying a particular type of sheet metal part based on its different features is still a challenge for a trained manual operator. Currently, in the market, there exist some automation solutions for solving such kind of problem but are incapable of achieving better performance and possess accuracy issues. So our goal is to provide process automation by overcoming manual work-based dependency and limitations. Therefore, a system is required that can take input a high definition camera captured sheet metal part image and provide an accurately identified type as output by utilizing a deep learning classification model in computer vision. The automation of sheet metal part identification by using ERP, CAD files and scheduling among them would make a smooth workflow by IDS-DLA. This paper aims to solve the identification problem by using the design and implementation of a sheet metal part identification, given as sheet metal part IDentification System for process automation using Deep Learning Algorithms (IDS-DLA). Considering the sheet metal parts there exists a large volume of types but fewer quantities. IDS-DLA performs high accuracy sheet metal part identification from the CAD model database by using the Geometric and CNN triplet filter. The IDS-DLA also evaluates the Hu moment ranking to choose the top 5 rank predictions as final ranking results. The applications can be given as manufacturing process automation for industry, 2D CAD search, 2D measuring solution, closet formation, etc. Ultimately, from the experiments, it can be observed that better accuracy is obtained as compared with the previous benchmarks. This multi-filtering approach using a deep feature extraction algorithm concludes to be the better approach and achieves higher performance.
Highlights
Factories of the sheet metal industry are consisting of several isolated working stages, where materials are moved manually between them, and working tasks could be completed heavily counting on the human-machine interaction
DATASET As shown in the above-given Table 2 we have used the training data called the Training Set of the CAD models consisting of different shapes and size part images as the standard model to be matched for the new sheet metal cut parts
A test data/set is used to be tested on the industry environment, which will be compared with the CAD models dataset to generate the identification of parts accurately based on its deep feature extraction
Summary
Factories of the sheet metal industry are consisting of several isolated working stages, where materials are moved manually between them, and working tasks could be completed heavily counting on the human-machine interaction. It means that the right metal parts should be transferred to the right destination stage for the manufacturing process with the right operational recipe and with the good training workers. Sheet metal part identification would be a very successful factor to fetch the manufacturing process information from ERP and scheduling system, and right instruction or recipes for each machine from the design CAD files. It means that once the operator can get the identification of a sheet metal part, he will know the routing path to transfer the metal object to the stage.
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