A Machine Learning Approach of Lattice Infill Pattern for Increasing Material Efficiency in Additive Manufacturing Processes

Alejandrino, Jonnel; Concepcion, Ronnie; Lauguico, Sandy; Tobias, Rogelio Ruzcko; Venancio, Lenardo; Macasaet, Dailyne; Dadios, Elmer P.

doi:10.18178/ijmerr.9.9.1253-1263

Cited by 32 publications

(5 citation statements)

References 16 publications

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“…Different infill patterns are used to achieve high-strength and lowweight structures for UAV parts. Also using different infill patterns can improve material usage efficiency [29,32,33]. Fig.…”

Section: Uav Desingn Proces For Additive Manufacturingmentioning

confidence: 99%

Multirotor UAV Design and Development – Case Study

Krznar,

Piljek,

Keran

2023

Teh. glas. (Online)

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This paper proposes the development and production of multirotor UAV parts using additive manufacturing. A new smart design approach is needed to take advantage of additive manufacturing in terms of reducing the product weight and making the product more customizable and specific purpose-oriented while also reducing the time and cost of product development and production. This paper provides a brief overview of three additive technologies: fused deposition modelling, stereolithography, and selective laser sintering. Two different UAV modules, the avionics module and GPS holder assembly, are described and produced. Also, some design ideas and approaches are explained, such as snap-fit joints and thread joints using hex bolt pockets and metal screws. The goal of this paper is to develop and manufacture special purpose UAV parts that are durable, sustainable, and low cost. For this purpose, the additive manufacturing process is proposed and described, from the idea to the final product.

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Section: Uav Desingn Proces For Additive Manufacturingmentioning

confidence: 99%

Multirotor UAV Design and Development – Case Study

Krznar,

Piljek,

Keran

2023

Teh. glas. (Online)

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“…These can relate to the macro characteristics of a single path, single layer, or multiple layers. As a result, several works focused on predicting geometry deviations in AM printed parts [115][116][117][118][119]. Inspecting visual defects has been a common way to judge product quality and is used in several ML models with tabular [63], graphic [120,121], spectrum [74] data as inputs.…”

Section: Macro Levelmentioning

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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“…In their study, they illustrate the differences between the classical 2D structures such as honeycomb structures and the Gyroid 3D structures. In the findings of the study, they determined that the 2D structures provide greater compression and strength along one axis compared to the 3D patterns, yet offer less support along all dimensions due to the nature of the patterns [20]. One study that focuses on bioinspired infill pattern designs provides details on optimization for specific properties such as strength and resilience through the use of cellular or lattice structures, but does not provide information on the opportunities for more bioinspired infill pattern designs and how they can be used to increase strength of structures [3].…”

Section: Introductionmentioning

confidence: 97%

“…Podroužek et al performed a comparison test of 14 different infill patterns for additive manufacturing and structural applications to determine the compression strengths relative to one another [20]. In their study, they illustrate the differences between the classical 2D structures such as honeycomb structures and the Gyroid 3D structures.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Design of Material Architecture for Additive Manufacturing

Pleasant,

Gavin,

Redden

et al. 2023

Machines

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This research explores the enhancement of mechanical properties in material architectures, such as strength-to-weight ratio and resilience, through the inspiration of natural systems. Historically, designs for additive manufacturing have relied on simple, repetitive structures like honeycombs, often leading to unnecessary material expenditure. This study aims to examine the compressive mechanical attributes of designs inspired by natural systems, including bird nests, cocoons, and the layered structure of skull bones. Through a comparative analysis, we assessed peak load capacity, strength-to-weight ratio, and resilience between these bioinspired architectures and a standard 3D infill pattern utilized in additive manufacturing. Findings indicate that structures inspired by sandwiched bone layers excel in resilience and peak load, whereas those based on bird nests are notably lighter and, in some cases, exhibit the highest strength-to-weight ratio. The insights provided here will help design engineers with empirically backed mechanical properties of bioinspired architectures, offering a novel methodology for the development of material systems influenced by biological paradigms.

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A Machine Learning Approach of Lattice Infill Pattern for Increasing Material Efficiency in Additive Manufacturing Processes

Cited by 32 publications

References 16 publications

Multirotor UAV Design and Development – Case Study

Multirotor UAV Design and Development – Case Study

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

Bioinspired Design of Material Architecture for Additive Manufacturing

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