Input value prediction of parameters in laser bending using Fuzzy and PSO
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This paper presents a novel Fuzzy-based bending angle predictor in laser bending process. Upon the case, and situations, different input data, membership functions and rules are developed dynamically for the Fuzzy predictor. Our main focus in developing the proposed controller is keeping generality of design. So, the controller can be adapted in different cases easily. To compensate for the possible lack of knowledge of experts for developing rule base of a Fuzzy controller, here, nonlinear regression is used as an alternative approach for developing the rule base. Furthermore, the performance of the controller is improved using particle swarm optimization (PSO) method. Also, based on the proposed Fuzzy controller and PSO, another predictor able to find one possible set of input values to catch a predefined angle is proposed. Several experimental tests were conducted to evaluate performance of the proposed controllers. Comparing experimental and predicted results shows that they are in a proper agreement with our claim.
KeywordsLaser forming Bending angle Fuzzy PSO
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Conflict of interest
The authors of the paper certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
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This article does not contain any studies with human participants performed by any of the authors.
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