Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data

Yavari, Reza; Riensche, Alex; Tekerek, Emine; Jacquemetton, Lars; Halliday, Harold; Vandever, Marcie; Tenequer, A.; Perumal, Vignesh; Kontsos, Antonios; Smoqi, Ziyad; Cole, Kevin D.; Rao, Prahalada

doi:10.1016/j.matdes.2021.110167

Cited by 53 publications

(12 citation statements)

References 59 publications

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“…Biosensing technologies based on tunable seesawlike 3D fiber pyro-and piezo-electric nanogenerators, AI-biophysics, and computational and digitally twinned additive manufacturing have been used extensively for monitoring and data collection. [38][39][40][41][42] Furthermore, meta-analysis, integrated digital light processing, 3D printing, mobile health, real-time processing, motion understanding, synchrotron radiation utilization, and ultrasound and nanomaterial-based technologies facilitate quality and quantitative data results. [43][44][45][46][47][48][49][50][51][52] Table 2 lists some biosensing materials, including conductive polymers, nanoparticles, and biocompatible materials.…”

Section: Sensors and Diagnosticsmentioning

confidence: 99%

Emergence of integrated biosensing-enabled digital healthcare devices

Mishra,

Singh,

Chauhan

et al. 2024

Sens. Diagn.

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Section: Sensors and Diagnosticsmentioning

confidence: 99%

Emergence of integrated biosensing-enabled digital healthcare devices

Mishra,

Singh,

Chauhan

et al. 2024

Sens. Diagn.

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“…Currently, considerable time and cost are being devoted to AM for offline metrology, defect identification and process optimization. DT can mitigate AM defects [ 258 ], enhance AM process repeatability [ 259 ] and assure AM part quality [ 123 ] through real-time closed-loop feedback control. Thus, a DT system looks to reliably address the shortcomings of AM through computational intelligence and real-time collaborative data management [ 260 ].…”

Section: Future Prospectmentioning

confidence: 99%

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

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Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

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“…IBSim was used to characterise a component manufactured with a bonding procedure for dissimilar materials used in water-cooled heat exchange components, identifying a defective joining process within ‘digital twins’ that would otherwise have comprised the component and surrounding substructure [ 189 ]. IBSim was used with a digital twin approach for detecting in-situ flaw formation in stainless steel (316L) impeller-shaped parts manufactured by L-PBF [ 190 ]. The digital twin approach was shown to be effective for detection of the three types of flaw formation causes studied in this research.…”

Section: Review Of Hvm Applications Of Ibsimmentioning

confidence: 99%

A Review of Image-Based Simulation Applications in High-Value Manufacturing

Evans

Sozumert

Keenan

et al. 2023

Arch Computat Methods Eng

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Image-Based Simulation (IBSim) is the process by which a digital representation of a real geometry is generated from image data for the purpose of performing a simulation with greater accuracy than with idealised Computer Aided Design (CAD) based simulations. Whilst IBSim originates in the biomedical field, the wider adoption of imaging for non-destructive testing and evaluation (NDT/NDE) within the High-Value Manufacturing (HVM) sector has allowed wider use of IBSim in recent years. IBSim is invaluable in scenarios where there exists a non-negligible variation between the ‘as designed’ and ‘as manufactured’ state of parts. It has also been used for characterisation of geometries too complex to accurately draw with CAD. IBSim simulations are unique to the geometry being imaged, therefore it is possible to perform part-specific virtual testing within batches of manufactured parts. This novel review presents the applications of IBSim within HVM, whereby HVM is the value provided by a manufactured part (or conversely the potential cost should the part fail) rather than the actual cost of manufacturing the part itself. Examples include fibre and aggregate composite materials, additive manufacturing, foams, and interface bonding such as welding. This review is divided into the following sections: Material Characterisation; Characterisation of Manufacturing Techniques; Impact of Deviations from Idealised Design Geometry on Product Design and Performance; Customisation and Personalisation of Products; IBSim in Biomimicry. Finally, conclusions are drawn, and observations made on future trends based on the current state of the literature.

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Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data

Cited by 53 publications

References 59 publications

Emergence of integrated biosensing-enabled digital healthcare devices

Emergence of integrated biosensing-enabled digital healthcare devices

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

A Review of Image-Based Simulation Applications in High-Value Manufacturing

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