OpenLMD, an open source middleware and toolkit for laser-based additive manufacturing of large metal parts

García-Díaz, Antón; Panadeiro, Verónica; Lodeiro, Baltasar; Rodríguez-Araújo, Jorge; Stavridis, John; Papacharalampopoulos, Alexios; Stavropoulos, Panagiotis

doi:10.1016/j.rcim.2018.04.006

Cited by 21 publications

(2 citation statements)

References 18 publications

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“…To move on to the architecture, each one of the sensors publishes the data acquired through its IoT node accompanied by a timestamp allowing the real-time recording of the data accurately referenced to a specific part point as described in [23,24]. "Real-time" is referring to the sum of processing time and exposure time of the camera as they described in the sections below.…”

Section: Laser Welding Cyber-physical System and Platform Architecturementioning

confidence: 99%

A three-stage quality diagnosis platform for laser-based manufacturing processes

Stavropoulos

Papacharalampopoulos

Stavridis

et al. 2020

Int J Adv Manuf Technol

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Diagnosis systems for laser processing are being integrated into industry. However, their readiness level is still questionable under the prism of the Industry’s 4.0 design principles for interoperability and intuitive technical assistance. This paper presents a novel multifunctional, web-based, real-time quality diagnosis platform, in the context of a laser welding application, fused with decision support, data visualization, storing, and post-processing functionalities. The platform’s core considers a quality assessment module, based upon a three-stage method which utilizes feature extraction and machine learning techniques for weld defect detection and quality prediction. A multisensorial configuration streams image data from the weld pool to the module in which a statistical and geometrical method is applied for selecting the input features for the classification model. A Hidden Markov Model is then used to fuse this information with earlier results for a decision to be made on the basis of maximum likelihood. The outcome is fed through web services in a tailored User Interface. The platform’s operation has been validated with real data.

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Section: Laser Welding Cyber-physical System and Platform Architecturementioning

confidence: 99%

A three-stage quality diagnosis platform for laser-based manufacturing processes

Stavropoulos

Papacharalampopoulos

Stavridis

et al. 2020

Int J Adv Manuf Technol

Self Cite

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“…Then, they applied a PID controller to increase and decrease the laser power to compensate for the rise and fall of the melt pool width, respectively. Similar studies used PID controllers to adjust the laser power based on the variation of melt pool area [11,12]. Enhancements have also been made to the conventional PID method, aiming to improve the controller performance under the varying dynamics of the DED process.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Adaptive Control for Laser-Based Additive Manufacturing with Automatic Controller Tuning

et al. 2020

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Closed-loop control is desirable in direct energy deposition (DED) to stabilize the process and improve the fabrication quality. Most existing DED controllers require system identifications by experiments to obtain plant models or layer-dependent adaptive control rules, and such processes are cumbersome and time-consuming. This paper proposes a novel data-driven adaptive control strategy to adjust laser voltage with the melt pool size feedback. A multitasking controller architecture is developed to incorporate an autotuning unit that optimizes controller parameters based on the DED process data automatically. Experimental validations show improvements in the geometric accuracy and melt pool consistency of controlled samples. The main advantage of the proposed controller is that it can adapt to DED processes with different part shapes, materials, tool paths, and process parameters without tweaking. System identification is not required even when process conditions are changed, which reduces the controller implementation time and cost for end-users.

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Artificial Intelligence in Manufacturing Processes

Chryssolouris

Alexopoulos

Arkouli

2023

A Perspective on Artificial Intelligence in Manufacturing

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OpenLMD, an open source middleware and toolkit for laser-based additive manufacturing of large metal parts

Cited by 21 publications

References 18 publications

A three-stage quality diagnosis platform for laser-based manufacturing processes

A three-stage quality diagnosis platform for laser-based manufacturing processes

Data-Driven Adaptive Control for Laser-Based Additive Manufacturing with Automatic Controller Tuning

Artificial Intelligence in Manufacturing Processes

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