Investigating the ability of deep learning to predict Welding Depth and Pore Volume in Hairpin Welding
- URL: http://arxiv.org/abs/2312.01606v3
- Date: Mon, 6 May 2024 14:51:19 GMT
- Title: Investigating the ability of deep learning to predict Welding Depth and Pore Volume in Hairpin Welding
- Authors: Amena Darwish, Stefan Ericson, Rohollah Ghasemi, Tobias Andersson, Dan Lönn, Andreas Andersson Lassila, Kent Salomonsson,
- Abstract summary: This study presents a robust deep learning model that enables the prediction of two critical welds: welding depth and average pore volume.
Applying deep learning networks to the small numerical experimental hairpin welding dataset has shown promising results.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: To advance quality assurance in the welding process, this study presents a robust deep learning model that enables the prediction of two critical welds Key Performance Characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a comprehensive range of laser welding Key Input Characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two deep learning networks are employed with multiple hidden dense layers and linear activation functions to showcase the capabilities of deep neural networks in capturing the intricate nonlinear connections inherent within welding KPCs and KICs. Applying deep learning networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving Mean Absolute Error (MAE) values as low as 0.1079 for predicting welding depth and 0.0641 for average pore volume. Additionally, the validity verification demonstrates the reliability of the proposed method. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying merely on monitoring for defect classification.
Related papers
- Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows [7.91363551513361]
Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting.
We present a fully unsupervised approach which requires no exposure to OOD data, avoiding researcher bias in OOD sample selection.
This is a post-hoc method which can be applied to any pretrained model, and involves training a lightweight auxiliary normalizing flow model to perform the out-of-distribution detection via density thresholding.
arXiv Detail & Related papers (2024-02-09T16:51:01Z) - Uncertainty Estimation by Fisher Information-based Evidential Deep
Learning [61.94125052118442]
Uncertainty estimation is a key factor that makes deep learning reliable in practical applications.
We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL)
In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
arXiv Detail & Related papers (2023-03-03T16:12:59Z) - Machine learning models for determination of weldbead shape parameters
for gas metal arc welded T-joints -- A comparative study [0.0]
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.
arXiv Detail & Related papers (2022-06-06T06:11:22Z) - Deep Learning Model Explainability for Inspection Accuracy Improvement
in the Automotive Industry [0.0]
This work aims to apprehend and emphasize the contribution of deep learning model explainability to the improvement of welding seams classification accuracy and reliability.
We implement a novel hybrid method that relies on combining the model prediction scores and visual explanation heatmap of the model.
The results show that the hybrid model performance is relatively above our target performance and helps to increase the accuracy by at least 18%.
arXiv Detail & Related papers (2021-10-07T12:23:00Z) - Unsupervised Scale-consistent Depth Learning from Video [131.3074342883371]
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training.
Thanks to the capability of scale-consistent prediction, we show that our monocular-trained deep networks are readily integrated into the ORB-SLAM2 system.
The proposed hybrid Pseudo-RGBD SLAM shows compelling results in KITTI, and it generalizes well to the KAIST dataset without additional training.
arXiv Detail & Related papers (2021-05-25T02:17:56Z) - Deep Learning Based Steel Pipe Weld Defect Detection [0.0]
State-of-the-art single-stage object detection algorithm YOLOv5 is proposed to be applied to the field of steel pipe weld defect detection.
The experimental results show that applying YOLOv5 to steel pipe weld defect detection can greatly improve the accuracy, complete the multi-classification task, and meet the criteria of real-time detection.
arXiv Detail & Related papers (2021-04-30T11:15:13Z) - Learning Neural Network Subspaces [74.44457651546728]
Recent observations have advanced our understanding of the neural network optimization landscape.
With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks.
With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks.
arXiv Detail & Related papers (2021-02-20T23:26:58Z) - Solving Sparse Linear Inverse Problems in Communication Systems: A Deep
Learning Approach With Adaptive Depth [51.40441097625201]
We propose an end-to-end trainable deep learning architecture for sparse signal recovery problems.
The proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase.
arXiv Detail & Related papers (2020-10-29T06:32:53Z) - 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) - DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D
Salient Object Detection [107.96418568008644]
We propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity.
By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner.
arXiv Detail & Related papers (2020-03-19T07:27:54Z)
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.