Process Mapping and In-Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging

Imani, Farhad; Gaikwad, Aniruddha; Montazeri, Mohammad; Rao, Prahalada; Yang, Hui; Reutzel, Edward W.

doi:10.1115/1.4040615

Cited by 102 publications

(28 citation statements)

References 67 publications

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“…The proposed method is applied to a case study example using probe vehicle speeds from several links in downtown Philadelphia, Pennsylvania. The results are used to identify communities of links which share similar spatiotemporal patterns using graph theory methods ( 16 , 17 ). These communities are shown to be remarkably consistent across the days in a week—and even throughout a day—suggesting that they represent relationships caused by the underlying traffic network structure.…”

mentioning

confidence: 99%

Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data

Esfahani

Gayah

2019

Transportation Research Record

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The existence of spatiotemporal correlations in traffic behavior on links in a transportation network is potentially very useful. However, traffic metrics are often strongly correlated simply because of natural variations in travel demand patterns and these temporal trends might obstruct more meaningful relationships caused by the physics of traffic. To overcome this challenge, the present paper proposes a non-parametric, moving average detrending method that can be used to remove these background trends, even during non-stationary periods in which traffic states are changing with time. Cross-correlations performed on the detrended data are then used to identify more meaningful trends. The proposed method can also incorporate temporal lags in correlations between individual links, which accounts for the time it takes for information to travel between them. Links that exhibit strong correlations after detrending can then be grouped into communities which behave together using graph theory methods, and this community structure can be leveraged to improve prediction of link performance when information is missing. The proposed methodology is applied to a case study network using real-time link travel speeds obtained from probe vehicles. The results reveal that the 40 links in the network can be grouped into between eight and 12 communities, depending on the day of the week. This suggests that only a handful of links may need to be monitored to estimate travel speeds across the entire network. Furthermore, the significant overlap in the community structure across these days reveals that the network structure plays a large role in spatiotemporal correlations in link travel speeds in a network.

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confidence: 99%

Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data

Esfahani

Gayah

2019

Transportation Research Record

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“…The proposed control and awareness module is supported by several research studies that focused on the development of an online monitoring and control (OMC) system to detect and isolate defects and to manufacture parts with particular desired qualities (Armini & Chang 2018b;Imani et al 2018;Yao et al 2018). A particularly important aspect of the proposed framework is that it may accommodate any combination of ML and SPC techniques for fault detection.…”

Section: Discussion On Implementation and Validationmentioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module-performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process-diagnosis module-performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module-performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform-an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework's applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

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“…The authors demonstrated that their approach performed at par to highly complex CNN and LSTM-RNN models and required nearly 1/10th of the computation time. Similarly, Imani et al (2018) demonstrated the ability of an ANN to predict laser powder bed fusion process parameter regimes using layer wise optical images. Kumar and Maji (2020) in their recent work employed a Genetic Algorithm-based (GA) framework for extracting the optimal process parameters of wire-arc additive manufacturing process.…”

Section: Prior Work Challenges and Noveltymentioning

confidence: 99%

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

et al. 2022

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In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

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Process Mapping and In-Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging

Cited by 102 publications

References 67 publications

Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data

Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data

A smart process controller framework for Industry 4.0 settings

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

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