A machine learning methodology for porosity classification and process map prediction in laser powder bed fusion

IF 4.4 Q2 ENGINEERING, MANUFACTURING

Progress in Additive Manufacturing Pub Date : 2023-12-23 DOI:10.1007/s40964-023-00544-2

Adrianna Staszewska, Deepali P. Patil, Akshatha C. Dixith, R. Neamtu, D. Lados

引用次数: 0

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激光粉末床熔融中孔隙率分类和工艺图预测的机器学习方法

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来源期刊

Progress in Additive Manufacturing Engineering-Industrial and Manufacturing Engineering

CiteScore

7.20

自引率

0.00%

发文量

113

期刊介绍： Progress in Additive Manufacturing promotes highly scored scientific investigations from academia, government and industry R&D activities. The journal publishes the advances in the processing of different kinds of materials by well-established and new Additive Manufacturing (AM) technologies. Manuscripts showing the progress in the processing and development of multi-materials by hybrid additive manufacturing or by the combination of additive and subtractive manufacturing technologies are also welcome. Progress in Additive Manufacturing serves as a platform for scientists to contribute full papers as well as review articles and short communications analyzing aspects ranging from data processing (new design tools, data formats), simulation, materials (ceramic, metals, polymers, composites, biomaterials and multi-materials), microstructure development, new AM processes or combination of processes (e.g. additive and subtractive, hybrid, multi-steps), parameter and process optimization, new testing methods for AM parts and process monitoring. The journal welcomes manuscripts in several AM topics, including: • Design tools and data format • Material aspects and new developments • Multi-material and composites • Microstructure evolution of AM parts • Optimization of existing processes • Development of new techniques and processing strategies (combination subtractive and additive methods, hybrid processes) • Integration with conventional manufacturing techniques • Innovative applications of AM parts (for tooling, high temperature or high performance applications) • Process monitoring and non-destructive testing of AM parts • Speed-up strategies for AM processes • New test methods and special features of AM parts