DOE Engineering Decision Support Tool V7.9.2

Define Problem

Plan

Experiment Strategy

Strategy

DOE Design

Design

Execute Experiment

Execute

Analyze Evidence

Evidence

1. 定义问题

定义一个响应、优化目标以及 2–6 个两水平因素。

待确认

Experiment Definition
Define why this DOE is performed before selecting factors and generating the design matrix.

Experiment Type / 实验类型

Project ID

Problem Statement / 问题描述

Engineering Hypothesis / 工程假设

Response Definition

Characteristic Type / 特性类型

Target / 目标值

Specification / 规格范围

Study Metadata / 试验追溯信息

Product / 产品

Part Number / 零件号

Material Lot / 物料批次

实验名称

响应名称

响应单位

优化目标

因素与水平

因素	类型	Factor Class	低水平	高水平	操作

Engineering Constraints / 工程约束

Define practical manufacturing limits. Hard constraints gate implementation readiness; soft constraints create a conditional release warning.

Factor	DOE Low	DOE High	Minimum Allowed	Maximum Allowed	Constraint Type

Advanced Study Information / 高级研究信息

Project ID、产品信息、物料批次、工程假设等用于报告追溯，不阻碍快速创建DOE。

2–4 个因素使用全因子设计；5–6 个因素使用预定义的 8 次筛选设计。所有计算均在浏览器本地完成。

Experiment Strategy & Feasibility / 实验策略与可行性

Assess DOE suitability, resource feasibility, information trade-offs and recommended strategy before generating a design matrix.

Not Assessed

Strategy assessment helps decide whether to run DOE, which DOE path to use, what information will be gained or lost, and what resources are required.

Strategy Factor Count

Factor Type

Current Knowledge Level

Experiment Objective

Available Experimental Runs

Cost per Run

Time per Run (days)

Time Constraint (days)

Sample Availability

Design Enhancement / 设计增强

Assess the strategy to review how remaining run budget can improve the design.

Design plan

DOE Expert Recommendation

Request an Expert Decision Engine recommendation for the experiment strategy. This does not change the DOE design engine.

Pending

Experiment Goal

Number of Factors

User Budget

Design Type

—

display_name pending.

Engineering Reason

—

Run Expert recommendation before accepting a strategy.

Next Actions

—

Decision Trace

Rule ID—

Rule Source—

Fallback Reason—

Engine Version—

Backbone Version—

Generator Version—

Timestamp—

Experiment Feasibility

—

Run strategy assessment before design generation.

Recommended Experiment Strategy

—

Experiment Cost Impact

—

Experiment Strategy Trace / 实验策略追溯

Records why this experimental approach was selected.

Strategy trace pending.

2. 实验设计

自动选择设计类型，并保留标准顺序与随机运行顺序。

完成定义问题后生成设计矩阵。

设计类型

—

自动选择

因素数

—

Factors

运行数

—

Runs

运行顺序

标准

Run Order

3. 执行实验

按随机运行顺序执行实验；每次运行可录入一个或多个单点响应。

0 / 0 已完成

生成设计矩阵后可录入实验响应。

模型基于每个试验运行的平均响应值建立。同一运行内的多个测量值不会被当作独立试验。

DOE Data Import

Import completed experimental results for analysis while keeping data processing local in the browser.

CSV format: Run, Factor columns, Response. Mapping must be confirmed before data is applied.

Data Validation / 数据验证

Check DOE data before running analysis.

Pending

Run CountNot Checked

Response CompletenessNot Checked

Factor LevelsNot Checked

Run OrderNot Checked

ReplicatesNot Checked

Missing Design RunsNot Checked

Analysis ReadinessNot Checked

4. 分析与报告

比较高低水平平均响应，排序主效应，并形成确认试验建议。

未分析

完成全部响应值并运行分析后显示结果。

DOE Dashboard / DOE决策摘要
Review dominant factors, recommended settings and confirmation status before implementation.

Measured Results / Experimental Evidence

What actually happened

—

Measured · Source: Experimental Run Data

Model Prediction

What the DOE model estimates

—

Predicted · Source: DOE Model

Engineering Decision

What we should do

—

Decision · Source: Engineering Evaluation

Decision Trace / 决策追溯

Measured evidence, model prediction, constraints, target and confirmation linked to the final decision.

Statistical Evidence Foundation / 统计证据基础

Observed experimental effects are separated from statistical support and engineering readiness.

Observed Effect / 观察效应

Factor	Low Mean	High Mean	Observed Effect	Source

Observed effects are differences in experimental data. They are not statistical significance statements.

Statistical Evidence / 统计证据

Factor	Evidence	p-value	Status	Reason

Never call effects significant unless valid error estimation and statistical testing are available.

Data Readiness

—

Statistical Readiness

—

Engineering Readiness

—

Model Trust Layer

Define what the DOE model can support, where prediction applies, and what limitations remain before engineering decisions.

Pending

Model Assessment

Model assessment pending.

Prediction Boundary

Factor	Recommended	Tested Range	Status

Prediction Boundary

—

Model Diagnostic Status

—

Trust Status

—

Model Limitations

DOE Decision Summary / 工程决策摘要

Experiment Objective

—

Key Factors

—

Statistical Evidence

—

Model Trust

—

Engineering Direction

—

Recommended Setting

—

Validation Status

—

Engineering Constraints

—

Target Achievement

—

DOE Implementation Readiness

—

最大效应因素

—

最佳已观察运行

—

优化目标

—

Response Objective

完整运行

—

Complete Runs

主效应图

每个因素低水平与高水平的平均响应。

效应排序

按主效应绝对值从大到小排序。

主效应明细

因素	低水平平均值	高水平平均值	主效应	Evidence	Confidence	Recommended Action

基于主效应的预测推荐设定

以下组合用于形成确认试验，不应直接视为已经验证的最优组合。

确认试验：按推荐设定再执行一次实验，并将实际响应与当前最佳结果比较。

DOE Evidence Package / DOE证据包

Structured evidence for engineering review, confirmation and implementation decisions.

Executive Summary

Experimental Design Summary

Measured Results

Main Effects Analysis

Effect Ranking

Statistical Evidence / ANOVA

Interaction Analysis

Engineering Recommendation

Confirmation Plan

Model Analysis

Method Notes

V7.9 Model Trust

Improvement Verification and Closure

Appendix

DOE Study Record / 实验记录

Planning information retained for engineering review and reproducibility.

Factor Definition / 因素定义

Factor	Classification	Low Level	High Level

Confirmation Experiment / 确认实验

Validate the recommended settings before engineering implementation.

Predicted Response / 预测响应

Actual Response / 实际响应

Prediction Error

Decision

Pending

Confirmation is required before treating the DOE recommendation as an implemented process condition.

Improvement Verification / 改善验证

Compare historical baseline performance with post-implementation verification data.

Pending

Data separation: Baseline data comes from historical / pre-DOE process data. Improved data comes from post-implementation verification data. Neither overwrites DOE run data or confirmation results.

Baseline Mean

Baseline Standard Deviation

Baseline Sample Size (optional)

Improved Mean

Improved Standard Deviation

Improved Sample Size (optional)

Mean Improvement

—

Historical baseline vs improved process.

Variation Reduction

—

Baseline standard deviation vs improved standard deviation.

Improvement Verification Decision

—

Post-implementation evidence pending.

Process Capability Verification / 过程能力验证

Connection placeholder for Cp/Cpk/Ppk verification after DOE improvement is verified.

NOT CONNECTED

DOE Improvement Verification → Process Capability Analysis. Full capability analysis is handled by the Process Capability Tool.

DOE Study Closure / DOE研究关闭

Final engineering closure gate after implementation and improvement verification.

OPEN

Closure Checklist

—

Final Status

—

Closure pending.

Remaining Risks

Improvement verification and capability linkage pending.

研究摘要

当前 DOE 项目的结构与状态。

当前研究手动研究

因素数

2–6 supported

运行数

—

Auto generated

响应

Seal Strength

N/15mm

完成度

Run completion

分析就绪检查

运行分析前的最小完整性检查。

因素范围通过

高低水平通过

设计矩阵未生成

响应数据未完成

分析状态未就绪

方法说明

MVP 当前支持范围。

2–4 FactorsFull Factorial

5–6 Factors8-Run Screening

效应计算Avg High − Avg Low

数据处理浏览器本地

DOE Engineering Decision Support Tool

1. 定义问题

因素与水平

Engineering Constraints / 工程约束

Experiment Strategy & Feasibility / 实验策略与可行性

DOE Expert Recommendation

Experiment Strategy Trace / 实验策略追溯

2. 实验设计

3. 执行实验

DOE Data Import

Data Validation / 数据验证

4. 分析与报告

Measured Results / Experimental Evidence

Model Prediction

Engineering Decision

Decision Trace / 决策追溯

Statistical Evidence Foundation / 统计证据基础

Observed Effect / 观察效应

Statistical Evidence / 统计证据

Model Trust Layer

Model Assessment

Prediction Boundary

DOE Decision Summary / 工程决策摘要

主效应图

效应排序

主效应明细

基于主效应的预测推荐设定

DOE Evidence Package / DOE证据包

Executive Summary

Experimental Design Summary

Measured Results

Main Effects Analysis

Effect Ranking

Statistical Evidence / ANOVA

Interaction Analysis

Engineering Recommendation

Confirmation Plan

Model Analysis

Method Notes

V7.9 Model Trust

Improvement Verification and Closure

Appendix

DOE Study Record / 实验记录

Factor Definition / 因素定义

Confirmation Experiment / 确认实验

Improvement Verification / 改善验证

Process Capability Verification / 过程能力验证

DOE Study Closure / DOE研究关闭

DOE Data Exchange / 数据交换

Design Matrix Export

Data Preparation Checklist