以机器学习为导向的高强高导铝合金成分与铸锻成形工艺参数实验数据

采用热力学计算与实验相结合，以及OM、SEM、TEM、等组织分析方法，研究铝合金成分与铸锻成形工艺参数对原子团结构、析出相晶格种类与析出相大小与分数、晶粒尺寸等的影响规律，至少分析10张不同区域组织图片，并选择代表性图片作为实验数据，图片存储为.tif/.jpg格式。针对图片中晶粒尺、析出相大小及分数等，经统计后绘制图片，原始数据为.opj/.xlsx格式。利用万能拉伸试验机和热导率测量仪测量铝合金的抗拉强度、伸长率和热导率，取3-5次实验结果平均值作为实验数据，数据为图片及.xlsx/.doc格式。基于中奥双方已有研究数据、合作企业现有的工艺数据、国内外文献的数据，建立高强高导铝合金成分和工艺数据共享云平台，其中，大连交通大学占比23%，莱奥本大学占比10%，项目产生的数据占比14%，国内外文献占比44%，大连创新压铸、大连亚明等合作企业占比9%。数据量1.76GB。Combined thermodynamic calculations and experimental studies, together with microstructure characterization methods including optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and other techniques, were employed to investigate the influence rules of aluminum alloy composition and casting-forging forming process parameters on atomic cluster structure, lattice types of precipitated phases, size and volume fraction of precipitated phases, grain size and other microstructural characteristics. At least 10 sets of microstructure images from different regions were analyzed, with representative ones selected as experimental data and stored in .tif or .jpg formats. For indicators including grain size, size and volume fraction of precipitated phases in the images, statistical analysis was conducted followed by plotting, and the raw data was saved in .opj or .xlsx formats. A universal tensile testing machine and thermal conductivity measuring instrument were used to measure the tensile strength, elongation and thermal conductivity of aluminum alloys; the average value of 3 to 5 repeated experimental results was taken as the final experimental data, with related data stored in image files, .xlsx or .doc formats. A shared cloud platform for composition and process data of high-strength and high-conductivity aluminum alloys was established based on existing research data from China and Austria, current process data from cooperative enterprises, and data from domestic and foreign literatures. The data source proportions are as follows: Dalian Jiaotong University accounts for 23%, University of Leoben accounts for 10%, project-generated data accounts for 14%, domestic and foreign literatures account for 44%, and cooperative enterprises including Dalian Innovation Die Casting and Dalian Yaming account for 9%. The total data volume of the dataset is 1.76 GB.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于高强高导铝合金，通过热力学计算、实验及多种显微组织分析，研究了合金成分与铸锻成形工艺参数对微观组织与性能的影响。数据内容包括组织图片、力学性能（抗拉强度、伸长率）和热导率测量结果，来源于大连交通大学、莱奥本大学、合作企业及国内外文献，总数据量为1.76GB。