IDS-DLA: Sheet Metal Part Identification System for Process Automation Using Deep Learning Algorithms

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.