Machine learning models for determination of weldbead shape parameters for gas metal arc welded T-joints -- A comparative study

• URL: http://arxiv.org/abs/2206.02794v1
• Date: Mon, 6 Jun 2022 06:11:22 GMT
• Title: Machine learning models for determination of weldbead shape parameters for gas metal arc welded T-joints -- A comparative study
• Authors: R. Pradhan, A.P Joshi, M.R Sunny, and A. Sarkar
• Abstract summary: The shape of a weld bead is critical in assessing the quality of the joint. This study focuses on the statistical design and the artificial neural networks, to predict the weld bead shape parameters of shielded steel plates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The shape of a weld bead is critical in assessing the quality of the welded joint. In particular, this has a major impact in the accuracy of the results obtained from a numerical analysis. This study focuses on the statistical design techniques and the artificial neural networks, to predict the weld bead shape parameters of shielded Gas Metal Arc Welded (GMAW) fillet joints. Extensive testing was carried out on low carbon mild steel plates of thicknesses ranging from 3mm to 10mm. Welding voltage, welding current, and moving heat source speed were considered as the welding parameters. Three types of multiple linear regression models (MLR) were created to establish an empirical equation for defining GMAW bead shape parameters considering interactive and higher order terms. Additionally, artificial neural network (ANN) models were created based on similar scheme, and the relevance of specific features was investigated using SHapley Additive exPlanations (SHAP). The results reveal that MLR-based approach performs better than the ANN based models in terms of predictability and error assessment. This study shows the usefulness of the predictive tools to aid numerical analysis of welding.

Related papers

• GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs [51.02233412547456]
  We introduce a novel PEFT method, called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW) Our method updates only salient columns, while injecting Gaussian noise into non-salient ones. Experiments with LLaMA models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT methods under the same computational budget.
  arXiv  Detail & Related papers  (2024-08-27T14:41:14Z)
• A new method for optical steel rope non-destructive damage detection [3.195044561824979]
  This paper presents a novel algorithm for non-destructive damage detection for steel ropes in high-altitude environments (aerial ropeway) A segmentation model named RGBD-UNet is designed to accurately extract steel ropes from complex backgrounds. A detection model named VovNetV3.5 is developed to differentiate between normal and abnormal steel ropes.
  arXiv  Detail & Related papers  (2024-02-06T09:39:05Z)
• Investigating the ability of deep learning to predict Welding Depth and Pore Volume in Hairpin Welding [0.0]
  This study presents a deep learning model that enables the prediction of two critical welds' Key Performance Characteristics (KPCs): welding depth and average pore volume. Applying DL networks to the small numerical experimental hairpin welding dataset has shown promising results.
  arXiv  Detail & Related papers  (2023-12-04T03:38:17Z)
• A machine learning approach to predict the structural and magnetic properties of Heusler alloy families [0.0]
  Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values.
  arXiv  Detail & Related papers  (2022-08-07T20:46:57Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• A Model for Multi-View Residual Covariances based on Perspective Deformation [88.21738020902411]
  We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
  arXiv  Detail & Related papers  (2022-02-01T21:21:56Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Surrogate-based variational data assimilation for tidal modelling [0.0]
  Data assimilation (DA) is widely used to combine physical knowledge and observations. In a context of climate change, old calibrations can not necessarily be used for new scenarios. This raises the question of DA computational cost. Two methods are proposed to replace the complex model by a surrogate.
  arXiv  Detail & Related papers  (2021-06-08T07:39:38Z)
• Classification of Spot-welded Joints in Laser Thermography Data using Convolutional Neural Networks [52.661521064098416]
  We propose an approach for quality inspection of spot weldings using images from laser thermography data. We use convolutional neural networks to classify weld quality and compare the performance of different models against each other.
  arXiv  Detail & Related papers  (2020-10-24T20:38:12Z)
• Learning to predict metal deformations in hot-rolling processes [59.00006390882099]
  Hot-rolling is a metal forming process that produces a cross-section from an input through a sequence of deformations. In current practice, the rolling sequence and the geometry of their rolls are needed to achieve a given cross-section. We propose a supervised learning approach to predict a given by a set of rolls with given geometry.
  arXiv  Detail & Related papers  (2020-07-22T13:33:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.