Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes

2022 ◽  
pp. 75-95
Author(s):  
Ranjit Barua ◽  
Sudipto Datta ◽  
Pallab Datta ◽  
Amit Roychowdhury
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Quality Evaluation ◽  
Mechanical Characteristics ◽  
Manufacturing Processes ◽  
Superior Part ◽  
Machine Learning Methods ◽  
Excellent Control ◽  
Part Feature ◽  
Counting Model

Additive manufacturing (AM) make simpler the manufacturing of difficult geometric structures. Its possibility has quickly prolonged from the manufacture of pre-fabrication conception replicas to the making of finish practice portions driving the essential for superior part feature guarantee in the additively fabricated products. Machine learning (ML) is one of the encouraging methods that can be practiced to succeed in this aim. A modern study in this arena contains the procedure of managed and unconfirmed ML algorithms for excellent control and forecast of mechanical characteristics of AM products. This chapter describes the development of applying machine learning (ML) to numerous aspects of the additive manufacturing whole chain, counting model design, and quality evaluation. Present challenges in applying machine learning (ML) to additive manufacturing and possible solutions for these problems are then defined. Upcoming trends are planned in order to deliver a general discussion of this additive manufacturing area.

scholarly journals Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes

2021 ◽  
Author(s):  
Shyam Panjwani ◽  
Ivan Cui ◽  
Konstantinos Spetsieris ◽  
Michal Mleczko ◽  
Wensheng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Safety Evaluation ◽  
Manufacturing Processes ◽  
Learning Methods ◽  
Machine Learning Methods

THE APPLICATION OF WELD-BASED ADDITIVE MANUFACTURING STEEL TO STRUCTURAL ENGINEERING

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vittoria Laghi ◽  
Michele Palermo ◽  
Giada Gasparini ◽  
Stefano Silvestri ◽  
Tomaso Trombetti
Keyword(s):  
Additive Manufacturing ◽  
Structural Engineering ◽  
Mechanical Characteristics ◽  
Manufacturing Processes ◽  
Building Structures ◽  
Welded Steel ◽  
3D Printed ◽  
Printing Processes ◽  
Suitable Technique ◽  
Printing Parameters

The present work aims at providing the first considerations upon the application of innovative manufacturing technology for civil engineering purposes. In particular, among the 3D printing processes currently available, Weld-Based Additive Manufacturing (WAM) results to be the most suitable technique for the realization of innovative structural forms in metal material. The great potential of taking the printing head "out of the box" allows for the construction of innovative shapes by adding layer upon layer of welded steel. In particular, the study is focused on the realization of the first 3D-printed steel footbridge by a Dutch company held in Amsterdam, called MX3D, and its Additive manufacturing process, which results in specific constraints and limitations to be taken into account for design purposes. First, the design issues are described, by considering the printing parameters to be adopted for the realization of large-dimensions structures, and then the implications in terms of specific geometrical and mechanical characteristics are studied. These first engineering evaluations are intended to pave the way towards the development of a ground-breaking technology for the fully-automated design and construction of novel 3D-printed building structures through innovative robotic manufacturing processes whose parameters are still not fully known

scholarly journals Machine Learning for Additive Manufacturing

Encyclopedia ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 576-588
Author(s):  
Dean Grierson ◽  
Allan E. W. Rennie ◽  
Stephen D. Quayle
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Design Process ◽  
Manufacturing Processes ◽  
Wide Range ◽  
Production Techniques ◽  
End Use ◽  
Industrial Adoption ◽  
Further Development ◽  
Modelling Data

Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

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

2020 ◽  
pp. 1253-1263
Author(s):  
Jonnel D. Alejandrino ◽  
Ronnie S. II Concepcion ◽  
Sandy C. Lauguico ◽  
Rogelio Ruzcko Tobias ◽  
Lenardo Venancio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Manufacturing Processes ◽  
Learning Approach ◽  
Material Efficiency ◽  
Machine Learning Approach
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