An Online AM Quality Estimation Architecture From Pool to Layer

Yang, Haw‐Ching; Huang, Chih Hung; Adnan, Muhammad; Hsu, Chih-Hua; Lin, Chun-Hui; Cheng, Fan‐Tien

doi:10.1109/tase.2020.3012622

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…The middle flowrates (6-8 m/s) show the highest MPI-0 of nearly 80% and lower MPI-1 and MPI-2 ratios of 20%; in addition, the powder could be moved by the high flowrates (9-11 m/s) due to the MPI-0 rated being reduced. From (7), the optimal flowrate is 8 m/s.…”

Section: B Determining Flow Thresholdmentioning

confidence: 99%

“…Because the MPI shapes are parts of the significant features used to judge the stability and quality of the AM process, the camera-based in-situ systems are developed to monitor the melt pool and regulate the laser power online and in real time for the stability of the manufacturing process once the system detects some abnormal behaviors [6]. Hence, the chamber image and MPI will be captured by the overview CCD and coaxial CCD, respectively, and the fog or smoke generated during melting would be carried from the chamber by the Nitrogen airflow [7] [8].…”

Section: Introductionmentioning

confidence: 99%

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MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

Adnan

Yang

Kuo

et al. 2021

IEEE Robot. Autom. Lett.

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Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neural network and long short-term memory models, this paper presents a secondary tuning loop (System 2) to classify the types of melt-pool images (MPIs) from a coaxial camera online, suggest polishing parameters, and determine the control thresholds of System 1 offline. Case studies indicate that the thresholds and parameters of System 1 including smoke discharging, powder coating, and laser polishing of control loops of a laser powder bed fusion (LPBF) machine can be more deliberatively and logically decided by the proposed MPI-based System 2.

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Section: B Determining Flow Thresholdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

Adnan

Yang

Kuo

et al. 2021

IEEE Robot. Autom. Lett.

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“…For layer-wise monitoring of melt-pool size, the data must be collected and analyzed before the next layer of printing begins. Since the inter-layer, powder-recoating time takes approximately 10 s [41], both must be completed within the recoating time frame. In the melt-pool size analysis case study, collecting melt-pool data from one layer takes 1 s. It is likely to extract the features within 3 s by using a parallel-computing technique, and our clustering method takes round about 0.80 s. All of this is described in the following sections.…”

Section: L_mp Classi F Y = T R_mp + T F_mp + T Cluster ≤ T Recoatingmentioning

confidence: 99%

A New Architectural Approach to Monitoring and Controlling AM Processes

Adnan

Jones

et al. 2020

Applied Sciences

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The abilities to both monitor and control additive manufacturing (AM) processes in real-time are necessary before the routine production of quality AM parts will be possible. Currently, neither ability exist! The major reason is that AM processes are different from traditional manufacturing processes in many ways and so are the sensors and the monitoring data collected from them. In traditional manufacturing, that data is mostly numeric in nature. To that numeric data, AM monitoring data add large volumes of a variety of in situ, high-speed, image data. Collecting, fusing, and analyzing all that AM data and making the necessary control decisions is not possible using traditional, rigid, hierarchical-control architectures. Therefore, researchers are proposing to use real-time, machine-learning algorithms to analyze the data and to execute the other control functions. This paper identifies those control functions and proposes a new architecture to integrate them. This paper also shows an example of using that architecture to analyze the melt-pool, shape analysis using a clustering method.

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“…These were then examined using the pooling functionalities of the routing process [11]. Earlier methods have concentrated on location, vendor managing services, vehicle routing, and algorithm optimization [12]. High combustion methods can meet the prerequisites, but they have a negative impact, mainly harmful emissions of pollutants such as greenhouse gases (GHG) [13].…”

Section: Introductionmentioning

confidence: 99%

Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization

Alkahtani,

Abidi,

Obaid

et al. 2023

Sustainability

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Due to the emergence of technology, electric motors (EMs), an essential part of electric vehicles (which basically act as engines), have become a pivotal component in modern industries. Monitoring the spare parts of EMs is critical for stabilizing and managing industrial parts. Generally, the engine or motor parts are delivered to factories using packing boxes (PBs). This is mainly achieved via a pooling center that manages the operation and transportation costs. Nevertheless, this process has some drawbacks, such as a high power train, bad press, and greater energy and time consumption, resulting in performance degradation. Suppliers generally take the parts from one place and deliver them to the other, which leads to more operation and transportation costs. Instead, it requires pooling centers to act as hubs, at which every supplier collects the material. This can mitigate the cost level. Moreover, choosing the placement of pooling centers is quite a challenging task. Different methods have been implemented; however, optimal results are still required to achieve better objectives. This paper introduces a novel concept for pooling management and transport optimization of engine parts to overcome the issues in traditional solution methodologies. The primary intention of this model is to deduce the total cost of the system operation and construction. Programming techniques for transporting the PBs, as well as for locating the pooling center, are determined with the aid of an objective function as a cost function. The location of the pooling center’s cost is optimized, and a Modified Gannet Optimization Algorithm (MGOA) is proposed. Using this method, the proposed model is validated over various matrices, and the results demonstrate its better efficiency rate.

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An Online AM Quality Estimation Architecture From Pool to Layer

Cited by 14 publications

References 29 publications

MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

A New Architectural Approach to Monitoring and Controlling AM Processes

Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization

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