• 한국기계가공학회지
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• 제13권4호
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• pp.44-49
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• 2014

Roll forming technology has a problem in that it depends only on experience without accurate data in the actual field. To solve this problem, it is necessary to procure accurate data during the roll forming process. To this end, we determined the operating force and the material thickness by implementing several changes to those variables during an experiment. This study compares the FEA results and experimental results. Experimental results were used for the basic data of the design. The FEA results show that the roll forming machine is operating accurately and safely. And, a comparison of the results shows that the design of the automatic roll forming machine is operating in the right way. This design of an automatic roll forming machine will be helpful for many areas of the industry.

• 소성∙가공
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• 제29권5호
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• pp.282-289
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• 2020

Flexible roll forming is an advanced sheet-metal-forming process that allows the production of parts with various cross-sections. During the flexible process, material is subjected to three-dimensional deformation such as transverse bending, inhomogeneous elongations, or contraction. Because of the effects of process variables on the quality of the roll-formed products, the approaches used to investigate the roll-forming process have been largely dependent on experience and trial- and-error methods. Web-warping is one of the major shape defects encountered in flexible roll forming. In this study, an SVR model was developed to predict the web-warping during the flexible roll forming process. In the development of the SVR model, three process parameters, namely the forming-roll speed condition, leveling-roll height, and bend angle were considered as the model inputs, and the web-warping height was used as the response variable for three blank shapes; rectangular, concave, and convex shape. MATLAB software was used to train the SVR model and optimize three hyperparameters (λ, ε, and γ). To evaluate the SVR model performance, the statistical analysis was carried out based on the three indicators: the root-mean-square error, mean absolute error, and relative root-mean-square error.