A path planning method of lattice structural components for additive manufacturing

Zhou, Bo; Tian, Tongtong

doi:10.1007/s00170-021-07092-5

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…It is believed that the recent advancements in designing lattice materials using machine learning tools and enhancing their fabrication procedure based on intelligent algorithms would be one of the powerful solutions to mitigate the defects of additively manufactured lattice materials. For example, Zhou and Tian [82] utilized a machine learning algorithm to automatically determine a suitable filling path (i.e., the trajectory of the printing tool) for each sub-domain of the slicing layer of a lattice material, where different kinds of filling paths were utilized to demonstrate the efficiency of the proposed algorithm. Besides, Abdulla et al [83] demonstrated the capability of kernel ridge regression in predicting the relative density of 3D printed specimens fabricated by the SLM technique based on the process parameters.…”

Section: Optimizing the Fabrication Procedures Of Polymeric Metamater...mentioning

confidence: 99%

Review of Additively Manufactured Polymeric Metamaterials: Design, Fabrication, Testing and Modeling

Almesmari,

Baghous,

Ejeh

et al. 2023

Polymers

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Metamaterials are architected cellular materials, also known as lattice materials, that are inspired by nature or human engineering intuition, and provide multifunctional attributes that cannot be achieved by conventional polymeric materials and composites. There has been an increasing interest in the design, fabrication, and testing of polymeric metamaterials due to the recent advances in digital design methods, additive manufacturing techniques, and machine learning algorithms. To this end, the present review assembles a collection of recent research on the design, fabrication and testing of polymeric metamaterials, and it can act as a reference for future engineering applications as it categorizes the mechanical properties of existing polymeric metamaterials from literature. The research within this study reveals there is a need to develop more expedient and straightforward methods for designing metamaterials, similar to the implicitly created TPMS lattices. Additionally, more research on polymeric metamaterials under more complex loading scenarios is required to better understand their behavior. Using the right machine learning algorithms in the additive manufacturing process of metamaterials can alleviate many of the current difficulties, enabling more precise and effective production with product quality.

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Section: Optimizing the Fabrication Procedures Of Polymeric Metamater...mentioning

confidence: 99%

Review of Additively Manufactured Polymeric Metamaterials: Design, Fabrication, Testing and Modeling

Almesmari,

Baghous,

Ejeh

et al. 2023

Polymers

View full text Add to dashboard Cite

show abstract

“…This data type can be collected from different phases of AM processes such as design [102] and post-process [103]. In a relevant application, 3D data from sliced lattice models is used in an SVM model to predict optimal filling paths for lattice structures [104]. X-ray computed tomography (XCT) generates 3D volumes of parts that are sliced to 2D, cropped, and de-noised before being fed to a CNN for the prediction of build orientation [103].…”

Section: Process Parameters and Process Statesmentioning

confidence: 99%

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

et al. 2022

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Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications on AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods and suggestions are given.

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Applications in Data-Driven Additive Manufacturing

Safdar

Lamouche

Paul

et al. 2023

Engineering of Additive Manufacturing Features for Data-Driven Solutions

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A path planning method of lattice structural components for additive manufacturing

Cited by 9 publications

References 37 publications

Review of Additively Manufactured Polymeric Metamaterials: Design, Fabrication, Testing and Modeling

Review of Additively Manufactured Polymeric Metamaterials: Design, Fabrication, Testing and Modeling

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

Applications in Data-Driven Additive Manufacturing

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