A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Xia, Chunyang; Pan, Zengxi; Polden, Joseph; Li, Hui Jun; Xu, Yanling; Chen, Shanben; Zhang, Yuming

doi:10.1016/j.jmsy.2020.08.008

Cited by 249 publications

(89 citation statements)

References 89 publications

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“…For example, NIST customized their own open AM testbed to measure the in-process metrology and real-time process control for a better understanding of the overall AM process [13]. To realize the closed-loop systems, diverse kinds of control strategies such as adaptive control, fuzzy control, neural control, predictive control, and AI control algorithm can be utilized [14]. And, a time-based multi-loop architecture was proposed to realize the AM process with internal real-time and near real-time control loops [15].…”

Section: A Literature Reviewmentioning

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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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: A Literature Reviewmentioning

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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“…Former reviews, specifically on the additive manufacturing of metal parts, have already been published [17,20,21]. Their focus lies on monitoring techniques for powder bed [22] fusion and directed energy deposition [16,[23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

et al. 2021

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Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach.

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“…Additional drawbacks are limited controllability and repeatability of the processes. Still, a lot of manual adjustment is needed to keep the process stable since no sufficiently reliable monitoring and control solutions are available [21]. From the industrial perspective, this highly restricts the technology to companies that execute both research and production within the field.…”

Section: Introductionmentioning

confidence: 99%

“…In large-scale production, the main competing technology to LDEDw is wire-arc additive manufacturing (WAAM). In WAAM, an ordinary arc or a so-called plasma arc is used to melt the metal [21]. In comparison to LDEDw, components produced by WAAM, using an ordinary arc, are characterized by the higher heat input as well as the higher probability of process instabilities, defects [24] and shape distortions [21].…”

Section: Introductionmentioning

confidence: 99%

Hot-Wire Laser-Directed Energy Deposition: Process Characteristics and Benefits of Resistive Pre-Heating of the Feedstock Wire

Kisielewicz

Pandian

Sthen

et al. 2021

Metals

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This study investigates the influence of resistive pre-heating of the feedstock wire (here called hot-wire) on the stability of laser-directed energy deposition of Duplex stainless steel. Data acquired online during depositions as well as metallographic investigations revealed the process characteristic and its stability window. The online data, such as electrical signals in the pre-heating circuit and images captured from side-view of the process interaction zone gave insight on the metal transfer between the molten wire and the melt pool. The results show that the characteristics of the process, like laser-wire and wire-melt pool interaction, vary depending on the level of the wire pre-heating. In addition, application of two independent energy sources, laser beam and electrical power, allows fine-tuning of the heat input and increases penetration depth, with little influence on the height and width of the beads. This allows for better process stability as well as elimination of lack of fusion defects. Electrical signals measured in the hot-wire circuit indicate the process stability such that the resistive pre-heating can be used for in-process monitoring. The conclusion is that the resistive pre-heating gives additional means for controlling the stability and the heat input of the laser-directed energy deposition.

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A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Cited by 249 publications

References 89 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

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

Hot-Wire Laser-Directed Energy Deposition: Process Characteristics and Benefits of Resistive Pre-Heating of the Feedstock Wire

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