Iterative Local ANFIS-Based Human Welder Intelligence Modeling and Control in Pipe GTAW Process: A Data-Driven Approach


Combining human welder (with intelligence and flexibility) and automatic welding systems (with precision and consistency) will lead to intelligent welding systems. This paper aims to present a information-driven approach to model human welder intelligence and use the resultant model to manage automated gas tungsten arc welding method. To this finish, an innovative machine–human cooperative virtualized welding platform is teleoperated to conduct training experiments. The welding current is randomly changed to generate fluctuating weld pool surface and the human welder tries to regulate his arm movement (welding speed) based mostly on his observation on the $64000-time weld pool feedback/image superimposed with an auxiliary visual signal that instructs the welder to extend/reduce the speed. Linear model is 1st identified from the experimental information to correlate welder's adjustment on the welding speed to the three-D weld pool surface and a world adaptive neuro-fuzzy inference system (ANFIS) model is then proposed to improve the model accuracy. To better distill the detailed behavior of the human welder, K -means that clustering is performed on the input area such that a native ANFIS model is identified. To additional improve the accuracy, an iterative procedure has been performed. Compared to the linear, global and local ANFIS model, the iterative local ANFIS model provides higher modeling performance and divulges additional detailed intelligence human welders possess. To demonstrate the effectiveness of the proposed model as an efficient intelligent controller, automated management experiments are conducted. Experimental results verified that the controller is sturdy beneath different welding currents and welding speed disturbance.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Iterative Receivers for Downlink MIMO-SCMA: Message Passing and Distributed Cooperative Detection - 2018ABSTRACT:The fast development of mobile communications requires even higher spectral potency. Non-orthogonal
PROJECT TITLE :Diagnosing and Minimizing Semantic Drift in Iterative Bootstrapping Extraction - 2018ABSTRACT:Semantic drift is a common problem in iterative information extraction. Previous approaches for minimizing semantic drift
PROJECT TITLE :Iterative Block Tensor Singular Value Thresholding For Extraction Of Low Rank Component Of Image Data - 2017ABSTRACT:Tensor principal component analysis (TPCA) is a multi-linear extension of principal component
PROJECT TITLE : Efficiently Promoting Product Online Outcome: An Iterative Rating Attack Utilizing Product and Market Property - 2017 ABSTRACT: The prosperity of on-line rating system makes it a popular place for malicious
PROJECT TITLE : On Fault Tolerance for Distributed Iterative Dataflow Processing - 2017 ABSTRACT: Large-scale graph and machine learning analytics widely use distributed iterative processing. Typically, these analytics are

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry