Development of Data-Driven In-Situ Monitoring and Diagnosis System of Fused Deposition Modeling (FDM) Process Based on Support Vector Machine Algorithm

Kim, Jung Sub; Lee, Chang Su; Kim, Sung-Min; Lee, Sang Won

doi:10.1007/s40684-018-0051-4

Cited by 51 publications

(19 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…LS-SVMs are closely related to regularization networks and Gaussian processes, but additionally emphasize and exploit primal-dual interpretations [41,42]. SVM has been adopted to identify the FFF machine conditions and it achieves good results [8,10]. However, the computational cost of SVM is high due to the constrained optimization programming [42], especially when the data is very large.…”

Section: Methodology and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Several methods are currently under development for reliable in-situ monitoring based on the sensors for defect detection and location [8,9,10,11,12,13,14,15,16]. The sensors applied for in-situ monitoring during the FFF process contain acoustic emission (AE) [8], accelerometer sensors [10], infrared temperature sensors [11], fiber Bragg grating (FBG) sensors [15], visual camera [16], and more. According to previous studies, the objectives of in-situ monitoring and diagnosis of the FFF process can be divided into two groups: one for the states of the FFF machine [8,9,10,11,12,13], and the other for the quality of the building parts [14,15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al [8,9] developed an AE monitoring method based on the support vector machine (SVM) and hidden semi-Markov model (HSMM) for identification of normal state, run-out-of-material state, and nozzle plugged state of the FFF process. Kim et al [10] presented a system containing three accelerometers and an AE sensor to diagnose the loosened bolt in the nozzle head; the SVM-based models for the odd- and even-numbered layers of one FFF specimen are developed and the diagnosis accuracy is better than 87.5%. Liu et al [11] introduced a data-driven predictive modeling approach to predict surface roughness of manufactured parts.…”

Section: Introductionmentioning

confidence: 99%

“…To handle the large amount of data from sensors, data-driven methods are used owing to its flexibility in modeling, fast processing of prediction, and consideration of defect formation mechanisms [34]. The data driven methods used in-situ monitoring and diagnosing during the FFF process contains K-means [11], SVM [8,10], HSMM [9], compressive sensing [13], unsupervised density-based clustering method [11], and so on. In this paper, two data driven methods—the least squares support vector machine (LS-SVM) and the back-propagation neural network (BPNN) model—are introduced for in-situ monitoring and diagnosing during the FFF process.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

Zhao

et al. 2019

Sensors

View full text Add to dashboard Cite

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.

show abstract

Section: Methodology and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

Zhao

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

Ng,

Goh,

Goh

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

In recent years, there has been a widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well‐curated datasets, thereby unveiling latent knowledge crucial for informed decision‐making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM‐printed parts. By leveraging the copious data generated in AM processes, ML algorithms can significantly enhance design optimization. This is achieved by employing forward problem analysis in tandem with iterative optimization techniques or generative artificial intelligence tools. The approach involves reverse‐engineering from desired outcomes to yield valuable insights, ultimately streamlining the AM design process. This review paper delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML‐related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation) and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM‐related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM‐related applications. We envision that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM‐related research areas. This article is protected by copyright. All rights reserved

show abstract

Chaotic neural network model for SMISs reliability prediction based on interdependent network SMISs reliability prediction by chaotic neural network

Zhu

Zhu-ping

Sun

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

With the development of industrial Internet, smart manufacturing information systems (SMISs) are faced with more uncertainties, dynamics, and complexity. These problems bring more challenges to the reliability operation of SMISs. To solve the above problem, a prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability. First, we decompose failure data series into some subdata series components with strong regularity by using C‐C algorithm and Cao algorithm. On this basis, we use the maximum Lyapunov index to identify chaotic characteristics of failure data series. And then, we establish BP neural network prediction model by using reconstructing failure data to predict SMISs failure behaviors. Finally, we use two groups of failure data series to verify the effectiveness of chaotic BP neural network model, and the experiment results verify that chaotic BP neural network model has more accurate prediction results compared with BP network, support vector machine, long short term memory networks (LSTM), and autoregressive model (AR). LEAD PARAGRAPH SMISs are open and complex systems, human error and external environment cause the uncertainty of reliability. The threat of the external environment mainly comes from malicious attacks and the threat of the human error mainly comes from the wrong operation of the operator. Human error and external environment often cause frequent failures of software and hardware of the system or physical failures of devices. As an open complex system, the reliability operation of SMISs is very important for manufacturing enterprises. However, in the daily use of SMISs, the most common failures are time failure caused by human error and external environment. Therefore, it is very important to study the time failure of SMISs. Main points of this paper: (1) SMISs are complex and open systems, so we establish an interdependent network based on the characteristics of SMISs, and use the cascade effect of the complex network to point out that when SMISs fail, the system will easily fall into failure. (2) The phase space reconstruction method is used to restore the real data characteristics of failure data. (3) By using the reconstructed data and the neural network, the failure behavior can be predicted accurately. Compared with other popular prediction methods, it is found that general machine learning methods cannot predict data with chaotic characteristics. The research results of this paper find that when SMISs fail, the failure behavior can easily lead SMISs into chaos through the propagation of interdependent network. Therefore, when future scholars conduct fault analysis on SMISs, they should consider the chaos of the data, otherwise the systems fault analysis and diagnosis cannot be carried out accurately.

show abstract

Development of Data-Driven In-Situ Monitoring and Diagnosis System of Fused Deposition Modeling (FDM) Process Based on Support Vector Machine Algorithm

Cited by 51 publications

References 17 publications

In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

Chaotic neural network model for SMISs reliability prediction based on interdependent network SMISs reliability prediction by chaotic neural network

Contact Info

Product

Resources

About