Neuro-fuzzy model optimization for laser sensor-based quality control for robotic welding of AISI 1030 steel
subject: Class topper optimization; Laser sensor; Neuro-fuzzy model; Optimization; Robotic welding
description: Robotic welding demonstrates considerable potential in the automation of metal joining processes, resulting in enhanced consistency. This study proposes a methodology for evaluating weld quality by utilizing a laser sensor in conjunction with a hybrid neuro-fuzzy model. The system, designed for AISI 1030 mild steel, utilizes a Design of Experimentation (DOE) methodology to collect empirical data and train the model. A MOTOMAN MA1440 robotic arm, integrated with an AccuFast-II laser sensor, was utilized to acquire real-time weld characteristics. The proposed model integrates fuzzy logic with artificial neural networks (ANNs) for predicting weld quality and is subsequently optimized using the Class Topper Optimization (CTO) algorithm. The model exhibited a high level of prediction accuracy, as indicated by R-squared values of 1.0, 0.99677, 0.99851, and 0.97561 for the training, testing, validation, and overall WQCI datasets, respectively. The process parameters obtained from the CTO analysis yielded a WQCI of 0.824, exceeding the highest experimental value of 0.808, which reflects a 1.98% enhancement in weld quality. The system demonstrated strong performance on both straight and curved weld paths, achieving a positional error of less than 0.29 mm, which falls within the acceptable weld gap range of 11.6 mm. This study emphasizes the practical implementation of a neuro-fuzzy prediction system integrated with an innovative metaheuristic for quality control in robotic arc welding. The integration improves weld consistency, minimizes defects, and increases production efficiency, representing a notable advancement in intelligent manufacturing. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026.
creator: Rout, Amruta; Mahanta, Golak Bihari; Champatiray, Chiranjibi; Deepak, B.B.V.L.; Biswal, Bibhuti Bhusan
source: International Journal of Intelligent Robotics and Applications;
publisher: Springer
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23665971;
format: online
language: English
type: Article
coverage: Rout A., Department of Mechanical and Automobile Engineering, Christ University, Karnataka, Bangalore, 560074, India; Mahanta G.B., Department of Mechatronics and Automation Engineering, National Institute of Technology Patna, Bihar, Patna, 800005, India; Champatiray C., Department of Mechanical and Automobile Engineering, Christ University, Karnataka, Bangalore, 560074, India; Deepak B.B.V.L., Department of Industrial Design, National Institute of Technology Rourkela, Odisha, Rourkela, 769008, India; Biswal B.B., Department of Mechanical Engineering, Odisha University of Technology and Research, Odisha, Bhubaneswar, 751003, India