Statistical Analysis of Porosity and Process Parameter Relationships in Metal Additive Manufacturing

Ball, S.; Ghayoor, Milad; Pasebani, Somayeh; Tabei, Ali

doi:10.1016/j.promfg.2021.06.037

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Finally, the properties of the parts produced with these technological processes are influenced by many production variables [16]. Controlling them all is not trivial, although it would be required to guarantee the quality and reproducibility of the whole production process [17]. In the following, the geometric accuracy and mechanical strength of implants produced by AM is briefly analyzed as compared to CPs.…”

Section: Conventional Processes Vs Am Technologymentioning

confidence: 99%

Additive Manufacturing of Metal Load‐Bearing Implants 1: Geometric Accuracy and Mechanical Challenges

Zanetti,

Fragomeni,

Sanguedolce

et al. 2024

Chemie Ingenieur Technik

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Additive manufacturing (AM) of load‐bearing metal implants allows sustainable production of personalized implants with complex shapes and inner architectures. Implants must meet strict requirements to not harm patients, and production technique must certify their performance. Available materials, many production parameters and implant personalization on patient's needs represent limits of AM. Layer‐by‐layer material deposition and repeated thermal cycles typical of AM may also cause discontinuities between layers affecting implant mechanical features, and its match to the host body. In this paper the mechanical challenges that AM must overcome to replace traditional manufacturing techniques are discussed, in order to better understand whether AM has to be limited to implant personalization for exceptional cases.

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Section: Conventional Processes Vs Am Technologymentioning

confidence: 99%

Additive Manufacturing of Metal Load‐Bearing Implants 1: Geometric Accuracy and Mechanical Challenges

Zanetti,

Fragomeni,

Sanguedolce

et al. 2024

Chemie Ingenieur Technik

View full text Add to dashboard Cite

show abstract

“…Building upon the existing literature, there are many recent noteworthy studies on data-driven methodological developments. The Fourier transform has been applied to the two-point correlation function (TPCF) to capture the statistical distribution of porosity and explore the relationship between the porosity and process parameter in SLM of stainless steel 304L (Ball et al 2021). The response surface method (RSM) has been used to explore A c c e p t e d M a n u s c r i p t the effect of the main process parameters on porosity for the process parameter design and porosity prediction by considering AlSi12eZrH2 mixture powder, a porous material (Shim 2021).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical modeling of microstructural images for porosity prediction in metal additive manufacturing via two-point correlation function

et al. 2022

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Porosity is one of the most critical quality issues in additive manufacturing (AM). As process parameters are closely related to porosity formation, it is vitally important to study their relationship for better process optimization. In this paper, motivated by the emerging metal AM application, a three-level hierarchical mixed-effects modeling approach is proposed to characterize the relationship between microstructural images and process parameters for porosity prediction and microstructure reconstruction. Specifically, a two-point correlation function (TPCF) is used to capture the morphology of the pores quantitatively. Then, the relationship between the TPCF profile and process parameters is established. A blocked Gibbs sampling is developed for parameter inference. Our modeling framework can reconstruct the microstructure based on the predicted TPCF through a simulated annealing optimization algorithm. The effectiveness and advantageous features of our method are demonstrated by both the simulation study and the case study with real-world data from metal AM applications.

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Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing

Sani,

Zolfagharian,

Kouzani

2024

Advanced Intelligent Systems

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The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed‐loop artificial intelligence‐augmented additive manufacturing (AI2AM) technology that integrates AI‐based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI‐based 3D printing monitoring, closed‐loop feedback systems, and parameter optimization development. Finally, closed‐loop 3D printing challenges and future directions are discussed. AI‐based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials.

show abstract

Statistical Analysis of Porosity and Process Parameter Relationships in Metal Additive Manufacturing

Cited by 8 publications

References 19 publications

Additive Manufacturing of Metal Load‐Bearing Implants 1: Geometric Accuracy and Mechanical Challenges

Additive Manufacturing of Metal Load‐Bearing Implants 1: Geometric Accuracy and Mechanical Challenges

Hierarchical modeling of microstructural images for porosity prediction in metal additive manufacturing via two-point correlation function

Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing

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