Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution

Parsazadeh, Mohammad; Sharma, Shashank; Dahotre, Narendra B.

doi:10.1016/j.pmatsci.2023.101102

Cited by 53 publications

(21 citation statements)

References 225 publications

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“…The incorporation of machine learning techniques offers significant advantages, notably in the precise prediction of target lattice structures. This facilitates not only material and cost efficiencies but also expedites the overall production process [11]. Collectively, these benefits align with a more sustainable and environmentally reliable manufacturing paradigm.…”

Section: Introductionmentioning

confidence: 88%

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

Hussain,

Lee,

Tsang

et al. 2024

Preprint

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Triply Periodic Minimal Surface (TPMS) lattice structures are utilized in diverse fields such as engineering, material design, and biomedical. The use of appropriate TPMS lattice structures in 3D printing can obtain benefits in terms of production efficiency and material reduction towards a greener 3D printing process. However, there is a lack of an automated solution to suggest the appropriate TPMS lattice structure parameters, such that unnecessary material wastage cannot be neglected in the existing practices. To address the above challenges, this study proposes a machine learning-based recommendation framework for generating the TPMS lattice structures based on the engineering requirements. First, we compiled a dataset by producing 144 samples via the material extrusion (ME) technique and conducted compression tests on four TPMS lattice structures (Diamond, Gyroid, Schwarz, and split-P), each with varying parameters, fabricated using Polylactic acid (PLA) material. Second, we train four machine learning algorithms (K-Nearest Neighbors, Decision Tree, Random Forest, and Bayesian Regression) on this dataset to predict TPMS lattice structure (unit cell type, unit cell size, and wall thickness). Extensive experiments assess algorithm performance using R-squared values and Root Mean Square Error (RMSE) as evaluation measures. Our results indicate that the Random Forest and Decision Tree algorithms perform best, achieving R-squared scores of 0.9694 and 0.9689, along with RMSE values of 0.1180 and 0.0795, respectively. This work not only advances the field's understanding of automated selection for TPMS lattice structures but also holds noteworthy implications for eco-design and eco-innovation, particularly in the realm of sustainable and efficient green 3D printing applications.

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Section: Introductionmentioning

confidence: 88%

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

Hussain,

Lee,

Tsang

et al. 2024

Preprint

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“…These factors play a crucial role as they have an impact on the material properties of the manufactured components. When considering the integration of TO and LPBF in aerospace engineering, it is essential for engineers to possess a sufficient level of expertise in design optimization techniques and a deep understanding of how LPBF process parameters collectively influence the quality of manufactured products [43]. Evidently, aerospace design projects are composed of various engineering disciplines operating under strict norms, standards, and regulations that are specific to each project.…”

Section: Advances In the Coupling Of To And Ammentioning

confidence: 99%

A hybrid CAD/CAE/CAM project‐based laboratory framework integrating topology optimization and laser powder bed fusion for engineering education

Afify,

Moubachir,

Guennoun

et al. 2024

Comp Applic In Engineering

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In the era of Industry 4.0, laser‐based additive manufacturing (LBAM) has gained substantial momentum in the production of complex lightweight structures in several domains such as aerospace, civil, and biomedical engineering. The increasing demand for consistent and precise manufacturing processes has urged the manufacturing industry to consider the coupling of topology optimization (TO) and laser powder bed fusion (LPBF) in the design of sophisticatedly novel topologies that are unattainable through traditional processes. Notably, this union has demonstrated a highly efficient and productive capability toward the manufacturing industry. In line with this accelerated pace, engineering programs within universities exert significant effort to revise and update their engineering curriculum design to incorporate emerging manufacturing technologies providing students with skills that are aligned with the actual industrial context. In this article, a computer‐aided design (CAD)/computer‐aided engineering (CAE)/computer‐aided manufacturing (CAM) project‐based laboratory framework is proposed combining TO and LPBF exploiting a simulation‐based environment integrating CAD, CAE, and CAM. The structure of the proposed CAD/CAE/CAM design methodology is revisited to discern the added value of the newly developed framework and its particularity in assisting aerospace engineering students to meet industrial expectations. A new CAD/CAE/CAM project‐based laboratory framework has been developed integrating TO and LPBF using advanced engineering tools within the aerospace engineering education.

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“…Plastic surgery, a specialized medical branch, has also seen the integration of machine learning techniques for various purposes. Below are some areas where machine learning is being used in plastic surgery [1][2][3].…”

Section: Editorialmentioning

confidence: 99%

“…This aids surgeons in preoperative assessment and surgical planning [3,4]. This also allows for more precise and personalized surgical planning [1][2][3][4][5]. Machine learning algorithms can automatically segment different tissues in medical images.…”

Section: Editorialmentioning

confidence: 99%

Precision and Progress: Machine Learning Advancements in Plastic Surgery

Mir¹,

Maurya²

2023

Cureus

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Machine learning has emerged as a powerful tool in various healthcare domains, and its application in plastic surgery has shown significant promise. Plastic surgery aims to enhance and reconstruct physical appearance and function, making it an ideal field for integrating machine learning techniques. This abstract presents an overview of the applications, challenges, and potential benefits of machine learning in plastic surgery.One of the key areas where machine learning has been applied is in the preoperative assessment and surgical planning process. By analyzing large datasets of patient images and clinical data, machine learning algorithms can assist plastic surgeons in predicting surgical outcomes, identifying optimal surgical techniques, and minimizing potential complications. These algorithms can learn from past cases and provide valuable insights to improve surgical decision-making and optimize patient care.Furthermore, machine learning has shown promise in facial recognition and analysis, which is crucial in plastic surgery procedures involving the face. Algorithms can accurately detect facial landmarks, assess facial symmetry, and simulate potential surgical outcomes. This technology gives plastic surgeons a more comprehensive understanding of the patient's facial structure and aids in designing personalized treatment plans.Additionally, machine learning algorithms have been employed to automate the analysis of large-scale clinical databases, assisting in identifying patterns, risk factors, and treatment outcomes. By leveraging these algorithms, plastic surgeons can gain valuable insights into patient populations, surgical trends, and postoperative complications. This information can inform clinical decision-making, improve patient safety, and enhance the overall quality of care.Despite the numerous advantages, several challenges need to be addressed when integrating machine learning into plastic surgery. These include the need for high-quality and diverse datasets, algorithm interpretability, ethical considerations, and regulatory compliance.

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Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution

Cited by 53 publications

References 225 publications

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

A hybrid CAD/CAE/CAM project‐based laboratory framework integrating topology optimization and laser powder bed fusion for engineering education

Precision and Progress: Machine Learning Advancements in Plastic Surgery

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