基于机器学习的氧化铁陶瓷烧结晶相含量预测数据

该数据包含13个字段，用于记录氧化铁陶瓷烧结工艺参数、模型权重及质量控制结果。“烧结批次”为字符串类型，标识每个烧结批次；"烧结温度T（℃）"、"烧结时间t（h）"和"烧结压力P（MPa）"均为浮点数，分别记录烧结过程中的温度（摄氏度）、时间（小时）和压力（兆帕）等核心工艺参数。权重系数字段包括α、β、γ、δ、ε五个浮点数，分别代表烧结温度、时间、压力及它们交互项（温度×时间、温度×压力）对晶相含量的非线性影响权重，这些系数通过大型模型训练优化获得。字段"修正实验环境误差C"为浮点数，用于校正实验环境导致的系统性误差。基于这些参数，通过晶相含量预测公式计算得出"晶相含量指数Y"（浮点数），该指数直接反映产品晶相含量水平。根据Y值自动判定"产品晶相含量等级"（字符串类型），分为优秀级（Y>110）、良好级（90≤Y≤110）和改进级（Y<90）；同时，当Y值低于75时，"是否自动触发返工程序"字段（布尔值）自动标记为真，触发返工流程，否则为假。该数据精准反映各变量对晶相含量的复杂影响，并通过等级划分和返工触发机制，有效提升产品质量控制效率和自动化水平。

This dataset contains 13 fields for recording iron oxide ceramic sintering process parameters, model weights and quality control results. The "Sintering Batch" field is of string type, used to identify each sintering batch. "Sintering Temperature T (℃)", "Sintering Time t (h)" and "Sintering Pressure P (MPa)" are all floating-point numbers, which respectively record the core process parameters including temperature (Celsius), holding time (hours) and pressure (Megapascals) during the sintering process. The weight coefficient fields include five floating-point numbers α, β, γ, δ and ε, which respectively represent the nonlinear influence weights of sintering temperature, time, pressure and their interaction terms (temperature × time, temperature × pressure) on crystal phase content. These coefficients are obtained through the training and optimization of a large-scale predictive model. The field "Corrected Experimental Environment Error C" is a floating-point number used to correct systematic errors caused by the experimental environment. Based on these parameters, the "Crystal Phase Content Index Y" (a floating-point number) is calculated via the crystal phase content prediction formula, which directly reflects the crystal phase content level of the product. The "Product Crystal Phase Content Grade" (string type) is automatically determined based on the Y value, which is divided into three categories: Excellent Grade (Y > 110), Good Grade (90 ≤ Y ≤ 110) and Needs Improvement Grade (Y < 90). Meanwhile, when the Y value is lower than 75, the "Whether to Automatically Trigger the Rework Procedure" field (boolean type) is automatically marked as True to trigger the rework process, otherwise it is marked as False. This dataset accurately reflects the complex impacts of various variables on crystal phase content, and effectively improves product quality control efficiency and automation level through grade classification and rework trigger mechanism.

搜集汇总

背景与挑战

背景概述

该数据集专注于氧化铁陶瓷材料在烧结过程中的结晶相含量预测，利用机器学习方法构建预测模型。它可能包含与陶瓷烧制工艺相关的实验或生产数据，旨在通过数据分析优化材料性能。数据集适用于材料科学和工业制造领域的研究与应用，帮助提高陶瓷产品的质量控制效率。