scholarly journals Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Xinyi Xiao ◽  
Clarke Waddell ◽  
Carter Hamilton ◽  
Hanbin Xiao
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Prediction Model ◽  
Analytical Models ◽  
Process Variables ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Learning Framework ◽  
And Control ◽  
Wire Arc Additive Manufacturing

Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.

Infrared Sensing for On-Line Weld Geometry Monitoring and Control

1995 ◽  
Vol 117 (3) ◽  
pp. 323-330 ◽  
Author(s):  
P. Banerjee ◽  
S. Govardhan ◽  
H. C. Wikle ◽  
J. Y. Liu ◽  
B. A. Chin
Keyword(s):  
Plate Thickness ◽  
Welding Process ◽  
Temperature Gradients ◽  
Monitoring And Control ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Weld Geometry ◽  
On Line ◽  
And Control ◽  
Welding Processes

This paper describes a method for on-line weld geometry monitoring and control using a single front-side infrared sensor. Variations in plate thickness, shielding gas composition and minor element content are known to cause weld geometry changes. These changes in the weld geometry can be distinctly detected from an analysis of temperature gradients computed from infrared data. Deviations in temperature gradients were used to control the bead width and depth of penetration during the welding process. The analytical techniques described in this paper have been used to control gas tungsten arc and gas metal arc welding processes.

Dairy Safety Prediction Based on Machine Learning Combined with Chemicals

2020 ◽  
Vol 16 (5) ◽  
pp. 664-676
Author(s):  
Jiahui Chen ◽  
Guangya Zhou ◽  
Jiayang Xie ◽  
Minjia Wang ◽  
Yanting Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Dairy Products ◽  
Control Measures ◽  
Classification Model ◽  
Sampling Inspection ◽  
Prevention And Control Measures ◽  
Common Items ◽  
And Control

Background: Dairy safety has caused widespread concern in society. Unsafe dairy products have threatened people's health and lives. In order to improve the safety of dairy products and effectively prevent the occurrence of dairy insecurity, countries have established different prevention and control measures and safety warnings. Objective: The purpose of this study is to establish a dairy safety prediction model based on machine learning to determine whether the dairy products are qualified. Methods: The 34 common items in the dairy sampling inspection were used as features in this study. Feature selection was performed on the data to obtain a better subset of features, and different algorithms were applied to construct the classification model. Results: The results show that the prediction model constructed by using a subset of features including “total plate”, “water” and “nitrate” is superior. The SN, SP and ACC of the model were 62.50%, 91.67% and 72.22%, respectively. It was found that the accuracy of the model established by the integrated algorithm is higher than that by the non-integrated algorithm. Conclusion: This study provides a new method for assessing dairy safety. It helps to improve the quality of dairy products, ensure the safety of dairy products, and reduce the risk of dairy safety.

Application of Machine Learning Techniques in Additive Manufacturing: A Review

2022 ◽  
pp. 1-24
Author(s):  
Amithkumar Gajakosh ◽  
R. Suresh Kumar ◽  
V. Mohanavel ◽  
Ragavanantham Shanmugam ◽  
Monsuru Ramoni
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Control Process ◽  
Machine Learning Techniques ◽  
Review Of The Literature ◽  
Learning Techniques ◽  
Manufactured Products ◽  
And Control ◽  
Monitoring Quality

This chapter provides an analysis of the state-of-the-art in ML applications for optimizing the additive manufacturing process. This chapter primarily presents a review of the literature on the use of machine learning (ML) in optimizing the additive manufacturing process at various stages. The chapter identifies ML-researched areas in which ML can be used to optimize processes such as process design, process plan and control, process monitoring, quality enhancement of additively manufactured products, and so on. In addition, general literature on the intersection of additive manufacturing and machine learning will be presented. The benefits and drawbacks of ML for additive manufacturing will be discussed, as well as existing obstacles that are currently limiting applications.

scholarly journals Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

2021 ◽  
Vol 11 (24) ◽  
pp. 11949
Author(s):  
Natago Guilé Mbodj ◽  
Mohammad Abuabiah ◽  
Peter Plapper ◽  
Maxime El Kandaoui ◽  
Slah Yaacoubi
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Prediction Model ◽  
Travel Speed ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Evolutionary Trend ◽  
Deposition Parameters ◽  
Layer Deposition ◽  
Height Prediction

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

A Study on the Machine Learning Framework for the Geometric Modelling of Wire Arc Bead Profile

2020 ◽  
Author(s):  
Xi Yu Oh ◽  
Gim Song Soh
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
Layer By Layer ◽  
Chi Square ◽  
Cross Sectional ◽  
Learning Framework ◽  
Single Bead ◽  
Step Over ◽  
Wire Arc Additive Manufacturing ◽  
Bead Profile

Abstract Wire Arc Additive Manufacturing (WAAM) is a manufacturing process that deposits weld beads layer-by-layer in a planar fashion, leading to a final part. Thus, the accuracy of the printed geometry is largely dependent on the knowledge of the bead profile employed, which by itself is dependent on a variety of process parameters, such as wire feedrate and torch speed. Existing models for modelling bead profile are based on its width and height, which do not necessarily capture the geometry of the weld bead accurately. This could affect the step over increment strategy, which dictates the geometry of the resulting overlapping valley. In this paper, we formulate and evaluate the performance of a variety of machine learning framework for predicting the bead cross-sectional profiles. To model the geometry of a bead, we explored direct cartesian representations using polynomials and vertical coordinates, as well as a higher dimensional representation using planar quaternions for supervised learning. Experiments are conducted on single bead SS316L material to compare the various framework performance. We found that among these, the planar quaternion representation with a non-linear neural network framework captures and retains the curvature characteristics of the bead during the learning and prediction process most accurately with a mean Chi-Square goodness of fit of 0.026.

Prediction of Protein-protein Interactions in Arabidopsis Thaliana using Partial Training Samples in a Machine Learning Framework

2021 ◽  
Vol 16 ◽  
Author(s):  
Fee Faysal Ahmed ◽  
Mst Shamima Khatun ◽  
Md. Parvez Mosharaf ◽  
Md. Nurul Haque Mollah
Keyword(s):  
Machine Learning ◽  
Arabidopsis Thaliana ◽  
Prediction Model ◽  
Protein Interactions ◽  
Computational Prediction ◽  
Protein Protein Interactions ◽  
Learning Framework ◽  
Training Samples ◽  
Wide Range ◽  
Ppi Prediction

Background: Protein-protein interactions (PPI) play a vital role in a wide range of biological processes starting from cell-cell interactions to developmental control in all organisms. However, experimental identification of PPI is often laborious, time-consuming and costly compared to computational prediction. There are several computational prediction models in the literature based on complete training samples, but none of them dealt with the partial training samples. Objective: The objective of this work was to develop an effective PPI prediction model for Arabidopsis Thaliana using partial training samples in a machine learning framework. Methods: We proposed an effective computational PPI prediction model by combining random forest (RF) classifier and autocorrelation (AC) sequence encoding features with 1:2 ratio of positive-PPI and unknown-PPI samples. Results: We observed that the proposed prediction model produces the highest average performance scores of sensitivity (94.62%), AUC (0.92) and pAUC (0.189) with the training datasets and sensitivity (88.14%), AUC (0.89) and pAUC (0.176) with the test datasets of 5-fold cross-validation compared to other candidate predictors based on LDA, LOGI, ADA, NB, KNN & SVM classifiers. It also computed the highest performance scores of TPR (91.82%) and pAUC (0.174) at FPR= 20% with AUC (0.948) compared to other candidate predictors. Conclusion: Overall performance of the developed model revealed that our proposed predictor might be useful to elucidate the biological function of unseen PPIs from a large number of candidate proteins in Arabidopsis thaliana.

scholarly journals Manufacturing of a Bimetallic Structure of Stainless Steel and Mild Steel through Wire Arc Additive Manufacturing – A Critical Review

2020 ◽  
Vol 5 (6) ◽  
pp. 679-685
Author(s):  
Anirudhan B T ◽  
Jithin Devasia ◽  
Tejaswin Krishna ◽  
Mebin T Kuruvila
Keyword(s):  
Stainless Steel ◽  
Additive Manufacturing ◽  
Mild Steel ◽  
Arc Welding ◽  
Bulk Material ◽  
Arc Discharge ◽  
Metal Wire ◽  
Layer By Layer ◽  
Bead Width ◽  
Wire Arc Additive Manufacturing

Wire and Arc based Additive Manufacturing, shortly known as WAAM, is one of the most prominent tech- nologies, under Additive Manufacturing, used for extensive production of complex and intricate shapes. This layer by layer deposition method avails arc welding technology; Gas Metal Arc Welding (GMAW), a competitive method in WAAM, is the conducted manufacturing process. It is a sum of heat source, originated from the electric arc, and metal wire as feedstock. The metal wire from the feedstock, melted by arc discharge, is deposited layer by layer. Another material can be added on to the top of deposited layer by replacing the feed wire from the stock, to fabricate a bimetallic structure. The purpose of this study is to collect the salient datum from the joining of two dissimilar metals. A combination of stainless steel and mild steel are considered. Proper deposition parameters, welding current along with voltage, bead width efficiency for both the metals were acquired. As a result, the physical properties of the dissimilar joint were approximate to the bulk material.

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