Metal additive manufacturing process system test design and structural data acquisition method

Abstract

Based on comprehensive analysis of characteristics, mechanisms, data and internal complex relation quantitative modeling requirements of a metal additive manufacturing process system, process data composition and a data node relation framework are elaborated in detail; modern test design, advanced material characterization, multi-physical field numerical simulation and big data analysis technologies are integrated into a systematic process test design and a structured process data acquisition process; all data required for establishing a high-quality deep neural network prediction model is obtained by elaborately designing test points and a process data acquisition scheme according to the principle of uniformity and orthogonality, and the comprehensive test quality and test efficiency are focused on by focusing on the common response of a plurality of variables. Structured efficient collection of highly-associated diversified multi-element and multi-dimensional discrete strong coupling process data of a process system is achieved, data support is provided for quantitative analysis and process mechanism modeling of a complex manufacturing process system, and a foundation is laid for exploring the fields of welding big data, high-end additive equipment development and intelligent manufacturing.

Description

technical field [0001] The invention relates to data collection and analysis, in particular to a method for experimental design and structured data collection of a metal additive manufacturing process system Background technique [0002] Metal additive manufacturing (Additive Manufacturing, AM) or 3D printing is the most cutting-edge and most potential additive manufacturing technology, and it is a potentially disruptive technology in important fields such as aerospace, biomedicine, and energy transportation. This technology uses heat sources such as laser beams, electron beams or arcs to melt metal powder or wire, and builds metal parts layer by layer through solidification of the molten pool, which can improve design freedom and manufacturing flexibility, thereby realizing complex structure molding and increasing product quality. Customization and reduced time-to-market while removing the constraints of traditional economies of scale. [0003] The information collection a...

AI Technical Summary

Problems solved by technology

However, the metal additive manufacturing process system is a complex selective area continuous melting casting system involving complex physical effects such as arc heat, force, sound, light, electricity, and magnetism, material melting, molten pool flow, melt solidification, and matrix solid-state phase transition. Among them, the coupling effect of physical fields such as energy field, temperature field, flow field, phase field, and stress field is strong, and the heat and mass transfer process of the rapid transition of solid, liquid, and plasma in metal materials is extremely complicated, resulting in plasma morphology, droplet Significant changes and differences in transition, cladding forming, alloying element distribution, microstructure state, and component strength and toughness can cause defects such as pores, cracks, humps, lack of fusion, and slag inclusions, which seriously affect the stability of the additive manufacturing process and components. Comprehensive performance, but due to the large number of parameters, strong coupling, complex process and high nonlinearity, it is difficult to obtain all the test data necessary to establish the internal quantitative relationship model of the process system through traditional test methods, and process data under the premise of ensuring test quality and efficiency. Systematic and structured collection, providing data support for the quantitative representation modeling analysis of the metal additive manufacturing process system

[0004] In view of the metal additive manufacturing industry's ultimate pursuit of full parametric design, precise process control and efficient intelligent manufacturing, but lack of effective structured process data collection means, a metal additive manufacturing process system experimental design and A structured approach to data capture becomes imperative

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