Springback Prediction Using Gated Recurrent Unit and Data Augmentation

Du Chen*, Frans Coenen, Yang Hai, Mariluz Penalva Oscoz, Anh Nguyen

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Artificial intelligence (AI) has been widely used in manufacturing, healthcare, sports, finance and other fields to model nonlinearities and make reliable predictions. In manufacturing, AI has been applied to improve processes, reduce costs and increase reliability. A new manufacturing process enhanced by AI is single point incremental forming (SPIF), a technique that uses a computer numerical control (CNC) machine to incrementally feed a metal sheet or polymer blank. However, achieving the geometric accuracy of the process is still of primary challenge due to the impact of springback. One of the most common solutions is toolpath correction. In this paper, we proposed a mechanism to capture local geometry using a novel point series representation, which then forms a general global geometry information. Each point series can then be associated with a predicted springback value and learns using deep learning. In particular, this article proposes the use of data augmentation to solve the problem of insufficient data and enable deep learning models to achieve better performance. Intensive experimental results show that we achieved the best R2 or “coefficient of determination” of 0.9228 compared to recent methods. We show that the proposed method provides a realistic solution to the current limitations of SPIF.

Idioma originalInglés
Título de la publicación alojadaAdvances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
EditoresAndrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas1-13
Número de páginas13
ISBN (versión impresa)9789819984978
DOI
EstadoPublicada - 2024
EventoInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China
Duración: 22 ago 202323 ago 2023

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen845
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

ConferenciaInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
País/TerritorioChina
CiudadSuzhou
Período22/08/2323/08/23

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