钢棒热处理工序元素与回火淬火温度分析数据

通过分析生产热处理中铁原料的碳(C)含量，锰(Mn)含量，钼(Mo)含量，回火温度，淬火温度，产品合格率的联系，结果应用到原料采购，产品热处理生产，工艺设计中。（1）采购部门通过分析出的原材料热处理数据，要求供应商出具钢材的元素含量来选择产品合格率高的含元素量钢材，作为采购钢材的重要判断标准之一。(2)生产时提供指导热处理回火温度和淬火温度，提高产品的合格率。（3）通过数据改进工艺设计，提高产品的质量，最终在市场中提高产品的竞争能力1.数据来自自有的正山智能大数据平台，是针对紧固件生产专业平台，本数据包括热处理时间、铁原料的碳(C)含量、锰(Mn)含量、钼(Mo)含量、回火温度、淬火温度以及产品合格率等核心字段。2.数据处理：（1）碳(C)含量、锰(Mn)含量、钼(Mo)含量作为产品属性处理；（2）使用TOPSIS算法以元素含量±0.2%为区间选取数据样本，再通过MAX()函数获取记录中的最高合格率，并获取该最优合格率下的回火温度、淬火温度，随着系统数据更新动态计算；（3）通过数据生成坐标图进行展示，要求工人进行学习。

This dataset analyzes the correlations among the carbon (C) content, manganese (Mn) content, molybdenum (Mo) content, tempering temperature, quenching temperature and product qualified rate of iron raw materials during heat treatment, and applies the research findings to raw material procurement, product heat treatment production and process design.
1. For the procurement department: Based on the analyzed heat treatment data of raw materials, suppliers are required to provide the elemental content of steel as one of the core judgment criteria for selecting steel with appropriate elemental contents to achieve a high product qualified rate for procurement.
2. Provide guidance on the setting of tempering temperature and quenching temperature during production to improve the product qualified rate.
3. Optimize process design via data analysis to enhance product quality, and ultimately improve the market competitiveness of the products.

1. Data source: The dataset is derived from the self-owned Zhengshan Intelligent Big Data Platform, a professional platform dedicated to fastener production. It covers core fields including heat treatment duration, carbon (C) content, manganese (Mn) content and molybdenum (Mo) content of iron raw materials, tempering temperature, quenching temperature and product qualified rate.
2. Data processing procedures:
(1) Treat the carbon (C) content, manganese (Mn) content and molybdenum (Mo) content as product attributes;
(2) Use the TOPSIS algorithm with a range of ±0.2% for elemental content to screen data samples, then employ the MAX() function to obtain the highest qualified rate among the records, and acquire the corresponding tempering temperature and quenching temperature under this optimal qualified rate, which is dynamically calculated along with the update of system data;
(3) Generate coordinate graphs for visualization to facilitate worker learning and training.

搜集汇总

数据集介绍

特点

该数据集包含钢棒热处理工序中的元素含量、回火和淬火温度等关键数据，规模为7264条，每日更新。数据应用于原料采购、生产指导和工艺设计优化，帮助企业提高产品质量和竞争力。