Machine Learning for Additive Manufacturing

Grierson, Dean; Rennie, Allan; Quayle, Stephen

doi:10.3390/encyclopedia1030048

Cited by 20 publications

(10 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…b An NN having one input layer, 2 hidden layers, and one output layer that is fully interconnected. (Grierson & Quayle, 2021). Neurons are symbolized by circles, which apply activation functions to the total sum of the activation and weight pairs from the previous layer Shah, 2018).…”

Section: In Additive Manufacturingmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

View full text Add to dashboard Cite

For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

show abstract

Section: In Additive Manufacturingmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For the process, two commonly used methods for building metal components from powder feedstock are the Directed Energy Deposition (DED) and Powder Bed Fusion (PBF) [ 34 ]. Another aspect of AM is the use of machine learning (ML), which has been proven in the optimization of the material properties, strength, material mix, and array of the lattice-structured additive manufactured materials [ 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Scientometric Review for Research Patterns on Additive Manufacturing of Lattice Structures

et al. 2022

View full text Add to dashboard Cite

Over the past 15 years, interest in additive manufacturing (AM) on lattice structures has significantly increased in producing 3D/4D objects. The purpose of this study is to gain a thorough grasp of the research pattern and the condition of the field’s research today as well as identify obstacles towards future research. To accomplish the purpose, this work undertakes a scientometric analysis of the international research conducted on additive manufacturing for lattice structure materials published from 2002 to 2022. A total of 1290 journal articles from the Web of Science (WoS) database and 1766 journal articles from the Scopus database were found using a search system. This paper applied scientometric science, which is based on bibliometric analysis. The data were subjected to a scientometric study, which looked at the number of publications, authorship, regions by countries, keyword co-occurrence, literature coupling, and scientometric mapping. VOSviewer was used to establish research patterns, visualize maps, and identify transcendental issues. Thus, the quantitative determination of the primary research framework, papers, and themes of this research field was possible. In order to shed light on current developments in additive manufacturing for lattice structures, an extensive systematic study is provided. The scientometric analysis revealed a strong bias towards researching AM on lattice structures but little concentration on technologies that emerge from it. It also outlined its unmet research needs, which can benefit both the industry and academia. This review makes a prediction for the future, with contributions by educating researchers, manufacturers, and other experts on the current state of AM for lattice structures.

show abstract

“…Additive manufacturing (AM) is a process that fabricates objects by fusing materials in discrete planar layers [ 1 ]. The process involves developing a tangible model of a computer-aided design (CAD) file through a program that sends instructions to the printer to perform a layer-by-layer development of the object [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Given the vulnerabilities to mechanical faults, there is a need for more accurate monitoring of mechanical faults in AM. This is currently a work in progress, as addressed in various studies [ 1 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities

Sampedro

Rachmawati

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring.

show abstract

Machine Learning for Additive Manufacturing

Cited by 20 publications

References 57 publications

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Scientometric Review for Research Patterns on Additive Manufacturing of Lattice Structures

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities

Contact Info

Product

Resources

About