In-situ process monitoring for metal additive manufacturing through acoustic techniques using wavelet and convolutional neural network (CNN)

Hossain, Md Shahjahan; Taheri, Hossein

doi:10.1007/s00170-021-07721-z

Cited by 35 publications

(10 citation statements)

References 39 publications

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“…Through the extraction and visualization of signal features, the corresponding defects and dimensional errors can be detected, classified, and predicted by the training model. in-situ monitoring by visual and acoustic signals are the main methods for online defect and dimension error prediction [262,263]. Vision-based monitoring has somewhat accomplished industrial readiness, but disadvantages lie in the high time and economic cost.…”

Section: Defect and Surface Quality Monitoringmentioning

confidence: 99%

Review on laser directed energy deposited aluminum alloys

Liu,

Chen,

Qiu

et al. 2024

Int. J. Extrem. Manuf.

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The lightweight aluminum (Al) alloys have been widely used in frontier fields like aerospace and automotive industries, which attracts great interest in additive manufacturing to process high-value Al parts. As a mainstream additive manufacturing technique, laser directed energy deposition (LDED) shows good scalability to meet requirements for large-format components manufacturing and repairing. However, LDED Al alloys are highly challenging due to the inherent poor printability (e.g., low laser absorption, high oxidation sensitivity and cracking tendency). To further promote the development of LDED high-performance Al alloys, this review gains a deep understanding of the challenges and strategies to improve printability in LDED Al alloys. The porosity, cracking, distortion, inclusions, elements evaporation and resultant inferior mechanical properties (than laser powder bed fusion) are the key challenges in LDED Al alloys. Processing parameter optimizations, in-situ alloy design, reinforcing particle addition and field assistance are the efficient approaches to improve the printability and performance of LDED Al alloys. The underlying correlations between processes, alloy innovation, characteristic microstructures, and achievable performances in LDED Al alloys are discussed. The benchmarking mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized. This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys. The future opportunities and perspectives in LDED high-performance Al alloys are also outlooked.

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Section: Defect and Surface Quality Monitoringmentioning

confidence: 99%

Review on laser directed energy deposited aluminum alloys

Liu,

Chen,

Qiu

et al. 2024

Int. J. Extrem. Manuf.

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“…In synergy with additive manufacturing, it can be used to improve different aspects of 3D printing: (i) material tuning, (ii) process optimization, (iii) on-site monitoring, (iv) cloud service and (v) cybersecurity. Some widespread AI applications in additive manufacturing, such as autonomous anomaly detection (Scime and Beuth, 2018; Tan et al , 2019; Scime et al , 2020; Cho et al , 2022) or 3D-printed parts inspection (Zhang et al , 2019; Cui et al , 2020; Davtalab et al , 2020; Hossain and Taheri, 2021), are based on its strong capability to deal with high-dimensional data such as images or videos. DRL implements the control task by the AI (precisely deep learning) capability to manage images and videos.…”

Section: Literature Reviewmentioning

confidence: 99%

A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning

Parisi

Sangiorgio

Parisi

et al. 2023

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Purpose Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process. Design/methodology/approach The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance. Findings The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization. Originality/value In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent. Practical implications The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.

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“…Metal additive manufacturing (MAM) [16] technology is applied to speed up manufacturing for the production of injection mold with CCC. However, the distinct disadvantage is expensive spending [17] and longtime taking [18] in the fabrication of injection mold with CCC. Under certain condition of less expenditure is possible to manufacture injection molds by RT technology with conformal cooling channel.…”

Section: Introductionmentioning

confidence: 99%

Effects of Different Mold Materials and Coolant Media on the Cooling Performance of Epoxy-Based Injection Molds

Kuo

Zhu

et al. 2022

Polymers

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Metal additive manufacturing techniques are frequently applied to the manufacturing of injection molds with a conformal cooling channel (CCC) in order to shorten the cooling time in the injection molding process. Reducing the cooling time in the cooling stage is essential to reducing the energy consumption in mass production. However, the distinct disadvantages include higher manufacturing costs and longer processing time in the fabrication of injection mold with CCC. Rapid tooling technology (RTT) is a widely utilized technology to shorten mold development time in the mold industry. In principle, the cooling time of injection molded products is affected by both injection mold material and coolant medium. However, little work has been carried out to investigate the effects of different mold materials and coolant media on the cooling performance of epoxy-based injection molds quantitatively. In this study, the effects of four different coolant media on the cooling performance of ten sets of injection molds fabricated with different mixtures were investigated experimentally. It was found that cooling water with ultrafine bubble is the best cooling medium based on the cooling efficiency of the injection molded parts (since the cooling efficiency is increased further by about 12.4% compared to the conventional cooling water). Mold material has a greater influence on the cooling efficiency than the cooling medium, since cooling time range of different mold materials is 99 s while the cooling time range for different cooling media is 92 s. Based on the total production cost of injection mold and cooling efficiency, the epoxy resin filled with 41 vol.% aluminum powder is the optimal formula for making an injection mold since saving in the total production cost about 24% is obtained compared to injection mold made with commercially available materials.

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In-situ process monitoring for metal additive manufacturing through acoustic techniques using wavelet and convolutional neural network (CNN)

Cited by 35 publications

References 39 publications

Review on laser directed energy deposited aluminum alloys

Review on laser directed energy deposited aluminum alloys

A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning

Effects of Different Mold Materials and Coolant Media on the Cooling Performance of Epoxy-Based Injection Molds

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