Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

Hu, Guang; Cao, Zhi; Hopkins, Michael; Hayes, Conor; Daly, Mark; Zhou, Haijiang; Devine, Declan M.

doi:10.1016/j.promfg.2020.01.016

Cited by 21 publications

(16 citation statements)

References 17 publications

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“…were used as input to build and train the ANN model with the emphasis on hardness as output. This study proves that the hardness of the SLA-ed objects can be optimized up to 1% error using ANN method [76].…”

Section: Robotics Simulations and Machine Visionsupporting

confidence: 53%

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa¹,

Arif²

2021

Int J Signal Process Anal

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Machine learning based artificial neural networking (ANN) has been acknowledged to be an authentic way to accomplish intricate pattern identification and regression analysis without any obvious necessity to paradigm and resolve the primary physical models. ANN has been introduced and adopted in various fields of life based-on their key advantages including learning and adapting ability, parallel distributed computation, robustness, and many more. This review articles discussesthe working principle, classes and structure of ANN. The potential applicable areas of ANN, the challenges and their solutions that have been identified through literature survey, are discussed and summarized. KeywordsMachine learning, Artificial neural networking (ANN), Potential applications of ANN, Challenges and solutions in ANN chine learning algorithms to detect anomalies correlations while performing test searching for patterns across the various data feeds, so facilitating the manufacturing process and making it easier for people to work [1].Although, there are tremendous applications of AI in various aspects of manufacturing systems but expert systems are the most commonly used application of AI. Expert systems are commonly used in designing, scheduling, process planning,

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Section: Robotics Simulations and Machine Visionsupporting

confidence: 53%

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa¹,

Arif²

2021

Int J Signal Process Anal

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“…Similar prints could take between 20 min to 1 h to print using SLA depending on the set up utilised. 22 In contrast to layer-bylayer AM techniques, support structures are not needed in volumetric printing, as demonstrated here with prints of different size which have overhanging features that would require support. This is because the viscous resin itself acts as a support structure during printing, analogous to how unsintered polymeric particles act as a support during selective laser sintering (SLS).…”

Section: †)mentioning

confidence: 97%

Glycerol-based sustainably sourced resin for volumetric printing

Krumins,

Lentz,

Sutcliffe

et al. 2024

Green Chem.

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“…Figure 14 displays a schematic representation of the build parameters, which include layer thickness, hatch spacing, fill spacing, border overcure, hatch overcure, and fill cure depth [ 93 , 94 ]. Another important build parameter is part orientation, which influences not only the component accuracy and surface quality but also the need for supporting structures, part strength, hardness, part build time, and, as a result, the part cost [ 102 , 103 ].…”

Section: Manufacturing Parameters For Am Of Advanced Biopolymersmentioning

confidence: 99%

Influence of Process Parameters on the Characteristics of Additively Manufactured Parts Made from Advanced Biopolymers

et al. 2023

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Over the past few decades, additive manufacturing (AM) has become a reliable tool for prototyping and low-volume production. In recent years, the market share of such products has increased rapidly as these manufacturing concepts allow for greater part complexity compared to conventional manufacturing technologies. Furthermore, as recyclability and biocompatibility have become more important in material selection, biopolymers have also become widely used in AM. This article provides an overview of AM with advanced biopolymers in fields from medicine to food packaging. Various AM technologies are presented, focusing on the biopolymers used, selected part fabrication strategies, and influential parameters of the technologies presented. It should be emphasized that inkjet bioprinting, stereolithography, selective laser sintering, fused deposition modeling, extrusion-based bioprinting, and scaffold-free printing are the most commonly used AM technologies for the production of parts from advanced biopolymers. Achievable part complexity will be discussed with emphasis on manufacturable features, layer thickness, production accuracy, materials applied, and part strength in correlation with key AM technologies and their parameters crucial for producing representative examples, anatomical models, specialized medical instruments, medical implants, time-dependent prosthetic features, etc. Future trends of advanced biopolymers focused on establishing target-time-dependent part properties through 4D additive manufacturing are also discussed.

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Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

Cited by 21 publications

References 17 publications

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Glycerol-based sustainably sourced resin for volumetric printing

Influence of Process Parameters on the Characteristics of Additively Manufactured Parts Made from Advanced Biopolymers

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