<i>In situ</i> 3D monitoring and control of geometric signatures in wire and arc additive manufacturing

Tang, Shangyong; Wang, Guilan; Zhang, Haiou

doi:10.1088/2051-672x/ab1c98

Cited by 25 publications

(7 citation statements)

References 31 publications

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“…In the above experiment, the accuracy of the sensor was discussed in our early work (Tang et al, 2019). The sensor is stable and feasible for the DPE method.…”

Section: Discussionmentioning

confidence: 94%

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A novel method of bead modeling and control for wire and arc additive manufacturing

Tang

Wang

Song

et al. 2021

RPJ

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Purpose Modeling and control of bead geometry in wire and arc additive manufacturing is significant as it affects the whole manufacturing process. The purpose of this paper is to establish an efficient model to control the bead geometry with fewer experiments in wire and arc additive manufacturing (WAAM). Design/methodology/approach A multi-sensor system is established to monitor the process parameters and measure the bead geometry information. A dynamic parameters experimental method is proposed for rapid modeling without dozens of experiments. A deep learning method is used for bead modeling and control. To adaptively control the bead geometry in real-time, a closed-loop control system was developed based on the bead model and in situ monitoring. Findings A series of experiments were conducted to train, test and verify the feasibility of the method and system, and the results showed that the proposed method can build the bead model rapidly with high precision, and the closed-loop system can control the forming geometry adaptively. Originality/value The proposed modeling method is novel as the experiment number is reduced. The dynamic parameters experimental method is effective with high precision. The closed-loop control system can control the bead geometry in real-time. The forming accuracy is elevated.

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“…In the above experiment, the accuracy of the sensor was discussed in our early work (Tang et al, 2019). The sensor is stable and feasible for the DPE method.…”

Section: Discussionmentioning

confidence: 94%

“…The weld bead geometry was measured by a one-off scanning process using the LATM sensor (scanning the shaped part from one end to the other end at one time) (Tang et al, 2019). Then the surface profile data of shaped bead was processed at every cross profile, as shown in Figure 8.…”

Section: The Data Processing Methods For Dynamic Parameters Experiments Methodsmentioning

confidence: 99%

A novel method of bead modeling and control for wire and arc additive manufacturing

Tang

Wang

Song

et al. 2021

RPJ

Self Cite

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“…In these geometry signatures, the widths and heights of the deposited layers 15 and the profile of the molten pool 9,10 can be monitored by a single industrial camera, and the entire outline of the deposited layers are commonly obtained by a 3-D camera. 16,17 To improve the calculation speed and efficiency, Tang et al 18 used a laser-based areal topography measurement sensor to measure the surface and developed a depth image method to process the data and extract the 3-D features. Moreover, a 3-D scanner of structural light was used to measure the height of the deposited layers from an external fixed position by Garmendia et al 17 However, the strong metal vapor in the 3-D reconstruction method based on structural light may contaminate the optical device and affect the monitoring results.…”

Section: Geometry Signature Monitoringmentioning

confidence: 99%

Review on adaptive control of laser-directed energy deposition

et al. 2020

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Laser directed energy deposition (LDED) is one of the most important parts of metal additive manufacturing, which can provide fast building speed, allows for large building volumes, and is suitable for part repair. LDED can manufacture components layer by layer through processes of rapid heating, melting, solidification, and cooling with the laser beam as a heat source. However, deposition quality and repeatability of components produced by LDED are poor because of the complex thermal cycle and processing environment, hindering the spread of this technique. Adaptive control technology (ACT) is consistently considered an effective and potential way to solve the problem. Many studies have focused on LDED and established the relations of process parameters, process signatures, and product qualities, which promote the rapid development of ACT, with the development of monitoring devices and data processing technology. We review and discuss the problems existing in the ACT of LDED.

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“…Closed-loop manufacturing is a specific application of this concept, which aims to optimize and control the manufacturing operations by using live data collected by sensors from the workpiece being manufactured. Optical measurement sensors have become more popular than contact coordinate measurement machines (CMM) for in-process coordinate metrology of the surface due to their fastness, higher resolution, and noncontact approach [1][2][3][4]. Cyber-Physical Integrated Inspection System (IIS) adds value to digital metrology to befit Industry 4.0 by reducing uncertainty based on the closed-loop implementation of three sequential tasks of coordinate metrology, i.e., Point Measurement Planning (PMP), Substitute Geometry Estimation (SGE), and Deviation Zone Evaluation (DZE).…”

Section: Introductionmentioning

confidence: 99%

Skin Imaging: A Digital Twin for Geometric Deviations on Manufactured Surfaces

Ghanbary Kalajahi,

Mahboubkhah,

Barari

2023

Applied Sciences

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Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces.

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In situ 3D monitoring and control of geometric signatures in wire and arc additive manufacturing

Cited by 25 publications

References 31 publications

A novel method of bead modeling and control for wire and arc additive manufacturing

A novel method of bead modeling and control for wire and arc additive manufacturing

Review on adaptive control of laser-directed energy deposition

Skin Imaging: A Digital Twin for Geometric Deviations on Manufactured Surfaces

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