scholarly journals Intelligent Scan Sequence Optimization for Uniform Temperature Distribution in Laser Powder Bed Fusion using a Control Theoretic Approach

2021 ◽  
Vol 54 (20) ◽  
pp. 503-508
Author(s):  
Keval S. Ramani ◽  
Chinedum E. Okwudire
Keyword(s):  
Temperature Distribution ◽  
Theoretic Approach ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Uniform Temperature ◽  
Sequence Optimization ◽  
Powder Bed ◽  
Uniform Temperature Distribution

Toward Intelligent Online Scan Sequence Optimization for Uniform Temperature Distribution in LPBF Additive Manufacturing

2021 ◽  
Author(s):  
Keval S. Ramani ◽  
Ehsan Malekipour ◽  
Chinedum E. Okwudire
Keyword(s):  
Additive Manufacturing ◽  
Temperature Distribution ◽  
Laser Scanning ◽  
Open Architecture ◽  
Uniform Temperature ◽  
Computationally Efficient ◽  
Optimal Sequence ◽  
Sequence Optimization ◽  
Uniform Temperature Distribution ◽  
Scan Pattern

Abstract Laser powder bed fusion (LPBF) is an increasingly popular approach for additive manufacturing (AM) of metals. However, parts produced by LPBF are prone to residual stresses, deformations, and other defects linked to nonuniform temperature distribution during the process. Several works have highlighted the important role (laser) scanning strategies, including laser power, scan speed, scan pattern and scan sequence, play in achieving uniform temperature distribution in LPBF. However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. To address these weaknesses, we present a framework for intelligent online scan sequence optimization to achieve uniform temperature distribution in LPBF. The framework involves the use of physics-based models for online optimization of scan sequence, while data acquired from in-situ thermal sensors provide correction or calibration of the models. The proposed framework depends on having: (1) LPBF machines capable of adjusting scan sequence in real-time; and (2) accurate and computationally efficient models and optimization approaches that can be efficiently executed online. The first challenge is addressed via a commercially available open-architecture LPBF machine. As a preliminary step towards tackling the second challenge, an analytical model is explored for determining the optimal sequence for scanning patterns in LPBF. The model is found to be deficient but provides useful insights into future work in this direction.

In-Process Monitoring of Material Cross-Contamination in Laser Powder Bed Fusion

2018 ◽  
Author(s):  
Mohammad Montazeri ◽  
Reza Yavari ◽  
Prahalada Rao ◽  
Paul Boulware
Keyword(s):  
Statistical Error ◽  
Theoretic Approach ◽  
Cross Contamination ◽  
Powder Bed Fusion ◽  
Statistical Control ◽  
Laser Powder Bed Fusion ◽  
Aluminum Powders ◽  
Powder Bed ◽  
Spectral Graph ◽  
Foreign Materials

The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in-situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock used in the process due to reasons, such as poor cleaning of the AM machine after previous builds, or inadequate quality control during production and storage of the feedstock powder material. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time during the build using in-process sensor signatures, such as those acquired from a photodetector. To realize this objective Inconel alloy 625 test parts were made on a custom-built L-PBF apparatus integrated with multiple sensors, including a photodetector (300 nm to 1100 nm). During the process the powder bed was contaminated with two types of foreign materials, namely, tungsten and aluminum powders under varying degrees of severity. Offline X-ray Computed Tomography (XCT) and metallurgical analyses indicated that contaminant particles may cascade to over eight subsequent layers of the build, and enter up to three previously deposited layers. This research takes the first-step towards detecting cross-contamination in AM by tracking the process signatures from the photodetector sensor hatch-by-hatch invoking spectral graph transform coefficients. These coefficients are subsequently traced on a Hoteling T2 statistical control chart. Using this approach, instances of Type II statistical error in detecting the onset of material cross-contamination was 5% in the case of aluminum, in contrast, traditional stochastic time series modeling approaches, e.g., ARMA had corresponding error exceeding 15%.

SmartScan: An Intelligent Online Scan Sequence Optimization Approach for Uniform Thermal Distribution, Reduced Residual Stresses and Deformations in PBF Additive Manufacturing

2021 ◽  
Author(s):  
Keval S. Ramani ◽  
Chuan He ◽  
Yueh-Lin Tsai ◽  
Chinedum E. Okwudire
Keyword(s):  
Additive Manufacturing ◽  
Temperature Distribution ◽  
Residual Stresses ◽  
Single Layer ◽  
Optimization Approach ◽  
Uniform Temperature ◽  
Computationally Efficient ◽  
Sequence Optimization ◽  
Uniform Temperature Distribution ◽  
Online Implementation

Parts produced by laser or electron-beam powder bed fusion (PBF) additive manufacturing are prone to residual stresses, deformations, and other defects linked to non-uniform temperature distribution during the manufacturing process. Several researchers have highlighted the important role scan sequence plays in achieving uniform temperature distribution in PBF. However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. To address these weaknesses, we have articulated a vision for an intelligent online scan sequence optimization approach to achieve uniform temperature distribution, hence reduced residual stresses and deformations, in PBF using physics-based and data-driven thermal models. This paper proposes SmartScan, our first attempt towards achieving our vision using a simplified physics-based thermal model. The conduction and convection dynamics of a single layer of the PBF process are modeled using the finite difference method and radial basis functions. Using the model, the next best feature (e.g., stripe or island) that minimizes a thermal uniformity metric is found using control theory. Simulations and experiments involving laser marking of a stainless steel plate are used to demonstrate the effectiveness of SmartScan in comparison to existing heuristic scan sequences for stripe and island scan patterns. In experiments, SmartScan yields up to 43% improvement in average thermal uniformity and 47% reduction in deformations (i.e., warpage) compared to existing heuristic approaches. It is also shown to be robust, and computationally efficient enough for online implementation.

Sensor-Based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process Using a Spectral Graph Theoretic Approach

2018 ◽  
Vol 140 (9) ◽  
Author(s):  
Mohammad Montazeri ◽  
Prahalada Rao
Keyword(s):  
High Speed ◽  
Sensor Data ◽  
Theoretic Approach ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Thermal Camera ◽  
Powder Bed ◽  
Graph Theoretic ◽  
Spectral Graph ◽  
Graph Theoretic Approach

The goal of this work is to monitor the laser powder bed fusion (LPBF) process using an array of sensors so that a record may be made of those temporal and spatial build locations where there is a high probability of defect formation. In pursuit of this goal, a commercial LPBF machine at the National Institute of Standards and Technology (NIST) was integrated with three types of sensors, namely, a photodetector, high-speed visible camera, and short wave infrared (SWIR) thermal camera with the following objectives: (1) to develop and apply a spectral graph theoretic approach to monitor the LPBF build condition from the data acquired by the three sensors; (2) to compare results from the three different sensors in terms of their statistical fidelity in distinguishing between different build conditions. The first objective will lead to early identification of incipient defects from in-process sensor data. The second objective will ascertain the monitoring fidelity tradeoff involved in replacing an expensive sensor, such as a thermal camera, with a relatively inexpensive, low resolution sensor, e.g., a photodetector. As a first-step toward detection of defects and process irregularities that occur in practical LPBF scenarios, this work focuses on capturing and differentiating the distinctive thermal signatures that manifest in parts with overhang features. Overhang features can significantly decrease the ability of laser heat to diffuse from the heat source. This constrained heat flux may lead to issues such as poor surface finish, distortion, and microstructure inhomogeneity. In this work, experimental sensor data are acquired during LPBF of a simple test part having an overhang angle of 40.5 deg. Extracting and detecting the difference in sensor signatures for such a simple case is the first-step toward in situ defect detection in additive manufacturing (AM). The proposed approach uses the Eigen spectrum of the spectral graph Laplacian matrix as a derived signature from the three different sensors to discriminate the thermal history of overhang features from that of the bulk areas of the part. The statistical accuracy for isolating the thermal patterns belonging to bulk and overhang features in terms of the F-score is as follows: (a) F-score of 95% from the SWIR thermal camera signatures; (b) 83% with the high-speed visible camera; (c) 79% with the photodetector. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, linear discriminant analysis were evaluated with F-score in the range of 40–60%.

scholarly journals In-Process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Mohammad Montazeri ◽  
Reza Yavari ◽  
Prahalada Rao ◽  
Paul Boulware
Keyword(s):  
Time Series ◽  
Theoretic Approach ◽  
Type Ii ◽  
Cross Contamination ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Spectral Graph ◽  
Stochastic Time Series ◽  
Stochastic Time

The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock used in the process due to reasons such as poor cleaning of the machine after previous builds or inadequate quality control during production and storage of the powder. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time as the part is being built using in-process sensors. The central hypothesis is that transforming the process signals in the spectral graph domain leads to early and more accurate detection of material cross-contamination in L-PBF compared to the traditional delay-embedded Bon-Jenkins stochastic time series analysis techniques, such as autoregressive (AR) and autoregressive moving average (ARMA) modeling. To test this hypothesis, Inconel alloy 625 (UNS alloy 06625) test parts were made at Edison Welding Institute (EWI) on a custom-built L-PBF apparatus integrated with multiple sensors, including a silicon photodetector (with 300 nm to 1100 nm optical wavelength). During the process, two types of foreign contaminant materials, namely, tungsten and aluminum particulates, under varying degrees of severity were introduced. To detect cross-contamination in the part, the photodetector sensor signatures were monitored hatch-by-hatch in the form of spectral graph transform coefficients. These spectral graph coefficients are subsequently tracked on a Hotelling T2 statistical control chart. Instances of Type II statistical error, i.e., probability of failing to detect the onset of material cross-contamination, were verified against X-ray computed tomography (XCT) scans of the part to be within 5% in the case of aluminum contaminant particles. In contrast, traditional stochastic time series modeling approaches, e.g., ARMA, had corresponding Type II error exceeding 15%. Furthermore, the computation time for the spectral graph approach was found to be less than one millisecond, compared to nearly 100 ms for the traditional time series models tested.

Effect of laser jump speed on temperature distribution and thermal stress in laser powder bed fusion

2021 ◽  
Vol 142 ◽  
pp. 107275
Author(s):  
Changpeng Chen ◽  
Zhongxu Xiao ◽  
Wenqi Zhang ◽  
Yilong Wang ◽  
Haihong Zhu
Keyword(s):  
Thermal Stress ◽  
Temperature Distribution ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

scholarly journals On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 538 ◽  
Author(s):  
Fabrizia Caiazzo ◽  
Vittorio Alfieri ◽  
Giuseppe Casalino
Keyword(s):  
Surface Roughness ◽  
Energy Density ◽  
Process Parameters ◽  
Vickers Hardness ◽  
Powder Bed Fusion ◽  
Processing Conditions ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Fractional Density ◽  
Experimental Variation

Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

Heat source model calibration for thermal analysis of laser powder-bed fusion

2020 ◽  
Vol 106 (7-8) ◽  
pp. 3367-3379 ◽  
Author(s):  
Shahriar Imani Shahabad ◽  
Zhidong Zhang ◽  
Ali Keshavarzkermani ◽  
Usman Ali ◽  
Yahya Mahmoodkhani ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Heat Source ◽  
Model Calibration ◽  
Source Model ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Heat Source Model ◽  
Powder Bed
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