scholarly journals Intelligent diagnostics and control of 3D printing processes by electric arc surfacing of workpieces made of cold-resistant materials on a CNC machine using machine learning approaches and neuromorphic calculations

2020 ◽  
Vol 971 ◽  
pp. 022001
Author(s):  
Dmitrii Shatagin ◽  
Yuri Kabaldin ◽  
Maksim Anosov ◽  
Pavel Colchin
Keyword(s):  
Machine Learning ◽  
3D Printing ◽  
Electric Arc ◽  
Learning Approaches ◽  
Cnc Machine ◽  
Diagnostics And Control ◽  
Printing Processes ◽  
And Control ◽  
Intelligent Diagnostics

Diagnostics of the 3D printing process on a CNC machine using machine learning approaches

2021 ◽  
pp. 55-59
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
P.V. Kolchin ◽  
A.V. Kiselev
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
3D Printing ◽  
Electric Arc ◽  
Cyber Physical System ◽  
Learning Approaches ◽  
Cnc Machine ◽  
Printing Process ◽  
Optimization Of Process Parameters ◽  
The Stability

Diagnostics and optimization of the dynamics of an electric arc during 3D printing on a CNC machine are considered. The application of nonlinear dynamics methods in assessing the stability of the 3D printing process and the use of artificial neural networks in the classification and optimization of process parameters are shown. Keywords: 3D printing, cyber physical system, machine learning, hybrid processing, neuroform controller, diagnostics, digital twin. [email protected]

scholarly journals Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas

2019 ◽  
Vol 52 (30) ◽  
pp. 30LT02 ◽  
Author(s):  
Ali Mesbah ◽  
David B Graves
Keyword(s):  
Machine Learning ◽  
Diagnostics And Control ◽  
And Control ◽  
Non Equilibrium

scholarly journals Application of Machine Learning to Bending Processes and Material Identification

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1418
Author(s):  
Daniel J. Cruz ◽  
Manuel R. Barbosa ◽  
Abel D. Santos ◽  
Sara S. Miranda ◽  
Rui L. Amaral
Keyword(s):  
Machine Learning ◽  
Sheet Metal ◽  
Bending Test ◽  
Model Parameters ◽  
Learning Approaches ◽  
Additional Information ◽  
The Many ◽  
And Control ◽  
Force Displacement ◽  
Springback Angle

The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and control of the many parameters involved, in processing and material characterization. In this article, two applications are considered to explore the capability of machine learning modeling through shallow artificial neural networks (ANN). One consists of developing an ANN to identify the constitutive model parameters of a material using the force–displacement curves obtained with a standard bending test. The second one concentrates on the springback problem in sheet metal press-brake air bending, with the objective of predicting the punch displacement required to attain a desired bending angle, including additional information of the springback angle. The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was validated by experiments.

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

Cyber-Physical Hybrid Processing System Digital Twin

2021 ◽  
Vol 1037 ◽  
pp. 119-124
Author(s):  
Dmitrii Shatagin ◽  
Andrei Galkin ◽  
Alexander N. Osmehin ◽  
Natalia Klochkova
Keyword(s):  
3D Printing ◽  
Processing System ◽  
Current Strength ◽  
Electric Arc ◽  
Diagnostic Parameter ◽  
Optimal Parameters ◽  
Digital Twin ◽  
Machine Learning Methods ◽  
The Stability ◽  
Printing Processes

The article proposes a method for obtaining a digital twin of the process of 3D printing by electric arc surfacing using an ensemble of machine learning methods. On the basis of the structural-parametric approach, a set of diagnostic parameters for the signals of current strength, voltage and acoustic emission was determined. Using exploratory analysis, the significance of each diagnostic parameter was assessed. A complex of statistical models has been developed to assess the stability of 3D printing processes using decision trees. Their optimal parameters and efficiency have been determined.

scholarly journals 3D-Printing and Machine Learning Control of Soft Ionic Polymer-Metal Composite Actuators

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
James D. Carrico ◽  
Tucker Hermans ◽  
Kwang J. Kim ◽  
Kam K. Leang
Keyword(s):  
Machine Learning ◽  
3D Printing ◽  
Bayesian Optimization ◽  
Metal Composite ◽  
Ionic Polymer Metal Composite ◽  
Printing Process ◽  
Ionic Polymer ◽  
Control Paradigm ◽  
Polymer Metal ◽  
And Control

AbstractThis paper presents a new manufacturing and control paradigm for developing soft ionic polymer-metal composite (IPMC) actuators for soft robotics applications. First, an additive manufacturing method that exploits the fused-filament (3D printing) process is described to overcome challenges with existing methods of creating custom-shaped IPMC actuators. By working with ionomeric precursor material, the 3D-printing process enables the creation of 3D monolithic IPMC devices where ultimately integrated sensors and actuators can be achieved. Second, Bayesian optimization is used as a learning-based control approach to help mitigate complex time-varying dynamic effects in 3D-printed actuators. This approach overcomes the challenges with existing methods where complex models or continuous sensor feedback are needed. The manufacturing and control paradigm is applied to create and control the behavior of example actuators, and subsequently the actuator components are combined to create an example modular reconfigurable IPMC soft crawling robot to demonstrate feasibility. Two hypotheses related to the effectiveness of the machine-learning process are tested. Results show enhancement of actuator performance through machine learning, and the proof-of-concepts can be leveraged for continued advancement of more complex IPMC devices. Emerging challenges are also highlighted.

scholarly journals Assessment of Cold Resistance of 09G2S Steel Obtained Using 3D Printing Technology by Electric Arc Surfacing at a CNC Machine

2020 ◽  
Vol 23 (2) ◽  
pp. 16
Author(s):  
Yu. G. Kabaldin ◽  
A. A. Khlybov ◽  
D. A. Shatagin ◽  
M. S. Anosov ◽  
D. A. Ryabov
Keyword(s):  
3D Printing ◽  
Cold Resistance ◽  
Electric Arc ◽  
Cnc Machine ◽  
Printing Technology ◽  
09G2s Steel ◽  
3D Printing Technology

Приводятся результаты исследований образцов из стали 09Г2С при пониженных температурах, полученных с использованием технологии 3D-печати электродуговой наплавкой. Для сравнения приводятся данные исследований на образцах, полученных из проката.Для достижения поставленной цели были изготовлены и испытаны образцы на ударный изгиб из стали 09Г2С. Образцы печатались с использованием технологии 3D-печати на станке с ЧПУ путем послойного нанесения наплавляемого материала из проволоки 09Г2С. Качество и стабильность структуры материала получаемых образцов обеспечивались за счет постоянной диагностики устойчивости динамической системы «источник питания – дуга – материал».Основным диагностическим параметром, характеризующим степень устойчивости, был показатель фрактальной размерности аттрактора динамической системы.Образцы для исследований вырезались в продольном и поперечном направлениих наплавки, аналогично изготавливались образцы из проката. Исследования полученных образцов проводились с использованием испытаний на ударный изгиб в широком диапазоне пониженных температур от –80 до +20 °C. Для выявления особенностей механизма разрушения и температуры вязкохрупкого перехода металлов проводились фрактографические исследования изломов образцов.В ходе исследований установлено, что температура вязкохрупкого перехода стали 09Г2С, полученной с использованием технологии 3D-печати электродуговой наплавкой, составляет порядка –40 °С, что незначительно выше температуры вязкохрупкого перехода стали 09Г2С, полученной из листового проката с последующим отжигом –47 °С. Следует отметить, что образцы, вырезанные вдоль наплавки, имеют более высокие значения ударной вязкости и температуры вязкохрупкого перехода.Для образцов, полученных электродуговой наплавкой, значения ударной вязкости не более чем на 20 % ниже, чем значения ударной вязкости образцов, полученных механической обработкой из листового проката во всем диапазоне исследуемых температур.Приведенная технология электродуговой наплавки, управляемой компьютером, может быть использована как для изготовления сложных изделий, так и для ремонта. Используя сварочные материалы с низкой температурой вязкохрупкого перехода, в частности используя сталь 09Г2С, можно получить высокие эксплуатационные свойства изделия в короткие сроки даже в арктических условиях.

scholarly journals Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

2020 ◽  
Author(s):  
Riya Tapwal ◽  
Nitin Gupta ◽  
Qin Xin
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Bandwidth Utilization ◽  
Data Caching ◽  
Learning Techniques ◽  
Future Demands ◽  
Iot Devices ◽  
And Control

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

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