Abstract
An intelligent regression technique is applied for sheet metal bending processes to improve bending performance. This study is a part of another extensive study, automated sheet bending assistance for press brakes. Data related to material properties of sheet metal is collected in an online manner and fed to an intelligent system for determining the most accurate punch displacement without any offline iteration or calibration. The overall system aims to reduce the production time while increasing the performance of press brakes.
Highlights
An intelligent regression technique is applied for sheet metal bending processes to improve bending performance
Press brakes performing sheet metal forming are one of the major production machines providing a variety of basic parts to several industries
The physical properties of the raw material used in press brakes affects the overall system significantly and the automatisation of the whole bending process with compensating the effects of different material properties of the raw material would be the key purpose for increasing the efficiency and speed of bending process
Summary
Rd radius of the die sides t thickness of the sheet metal β angle that supplements the bending angle z needed punch penetration to bend the sheet metal to (180-ß) degrees. The formulation takes die and punch types, bending angle and sheet metal thickness as inputs and returns the necessary punch displacement without taking material type of the sheet metal into consideration. This formulation is a starting point for searching the correct and accurate punch penetration value. Data is collected for 90 different bending angles at 4 different die configurations and for 3 different material thicknesses This adds up to 90 × 4 × 3 = 1080 data points. For these 1080 different data points, punch displacement after material touch with 1 μm resolution, cylinder chamber pressures of the press brake and the final bent angle of the materials with 0.1 degrees resolution are collected
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