阀门左右体检测合格率数据

通过采集企业内部数据，对各个型号的左右检测合格率进行统计，计算各个型号的左右体在在材质检测、外观检测、尺寸检测、圆度检测、形位公差检测和粗糙度检测中的合格率，通过分析检测合格率以帮助企业在左右体生产工艺中进行相应的改进，同时通过该数据的行业内分享，能够帮助行业中其他的企业可观的发现阀门左右体在生产过程中容易出现误差和瑕疵的地方，帮助行业中其他的企业进行左右体生产工艺的改进，以及通过该数据对用户的分享，能够帮助用户有效了解阀门左右体中容易出现误差的瑕疵的地方，从而帮助用户提高阀门验收效率。阀门左右检测合格率知识产权数据算法规则包括以下步骤：1、数据采集：查找及采集企业内部各个型号左右体的各个检测数据；2、数据处理：对采集到的数据分别计算，材质检测合格率=材质检测合格左右体数量/检测左右体总量，外观检测合格率=外观检测合格左右体数量/检测左右体总量，尺寸检测合格率=尺寸检测合格左右体数量/检测左右体总量，圆度检测合格率=圆度检测合格左右体数量/检测左右体总量，形位公差合格率=形位公差检测合格左右体数量/检测左右体总量，粗糙度检测合格率=粗糙度检测合格左右体数量/检测左右体总量；3、经过统计、筛选得到各个型号左右体在各个检测中的合格率，4、数据应用：根据各个型号左右体在各个检测中的合格率，直观的发现现有生产工艺中的不足，针对性的对现有生产工艺进行改进，为企业有效优化左右体生产工艺。

This dataset is developed by collecting internal enterprise data to statistically analyze the inspection pass rates of valve left and right bodies across different models. Specifically, it calculates the pass rates of left and right bodies in six inspection categories: material inspection, appearance inspection, dimensional inspection, roundness inspection, geometric dimensioning and tolerancing (GD&T) inspection, and surface roughness inspection. Analyzing these pass rates can help enterprises make targeted improvements to their valve left and right body production processes. Furthermore, sharing this dataset within the industry allows other enterprises to intuitively identify the error and defect-prone stages during valve left and right body production, assisting them in optimizing their own production workflows. Additionally, sharing the dataset with end users helps them effectively understand the common error and defect points of valve left and right bodies, thereby improving their valve acceptance efficiency.

The algorithmic rules for the intellectual property dataset of inspection pass rates of valve left and right bodies are as follows:
1. Data Collection: Collect all inspection data of valve left and right bodies of various models from internal enterprise databases;
2. Data Processing: Calculate the pass rate for each inspection item separately:
- Material inspection pass rate = Number of qualified left and right bodies via material inspection / Total number of inspected left and right bodies
- Appearance inspection pass rate = Number of qualified left and right bodies via appearance inspection / Total number of inspected left and right bodies
- Dimensional inspection pass rate = Number of qualified left and right bodies via dimensional inspection / Total number of inspected left and right bodies
- Roundness inspection pass rate = Number of qualified left and right bodies via roundness inspection / Total number of inspected left and right bodies
- Geometric dimensioning and tolerancing (GD&T) inspection pass rate = Number of qualified left and right bodies via GD&T inspection / Total number of inspected left and right bodies
- Surface roughness inspection pass rate = Number of qualified left and right bodies via surface roughness inspection / Total number of inspected left and right bodies
3. Derive the pass rates of each model's left and right bodies in each inspection item through statistics and screening;
4. Data Application: Based on the pass rates of each model's left and right bodies across all inspection items, intuitively identify the deficiencies in the current production process, implement targeted improvements to the existing production workflow, and effectively optimize the production of valve left and right bodies for the enterprise.