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质量工程

Quality Engineering

课程介绍 Course Introduction

学分:3 | 先修课:应用统计学、概率论 | 学期:秋季

质量工程是研究如何通过系统的方法和工具实现产品和过程质量保证与持续改进的学科。课程内容包括质量管理发展史、全面质量管理、质量成本分析、统计过程控制(SPC)、控制图、过程能力分析、抽样检验、质量功能展开(QFD)、失效模式与影响分析(FMEA)、田口方法、六西格玛管理等。学生将学习运用Minitab等工具进行质量数据分析和改进。

Quality Engineering studies how to achieve quality assurance and continuous improvement of products and processes through systematic methods and tools. Topics include quality management history, total quality management, quality cost analysis, statistical process control (SPC), control charts, process capability analysis, acceptance sampling, quality function deployment (QFD), failure mode and effects analysis (FMEA), Taguchi methods, and Six Sigma management. Students learn to use Minitab for quality data analysis and improvement.

大作业 Final Project

作业标题:生产过程统计过程控制与质量改进 / Production Process SPC and Quality Improvement

针对指定生产过程开展统计过程控制,包括控制图设计、过程能力分析、FMEA与改进方案。使用Minitab进行质量数据分析。

Conduct statistical process control for a specified production process, including control chart design, process capability analysis, FMEA and improvement schemes. Use Minitab for quality data analysis.

实施步骤 Implementation Steps

📋 示例:为一个真实的制造过程建立质量控制体系,比如手机外壳的注塑成型工序。你需要采集样本数据画Xbar-R控制图,计算过程能力指数,然后做FMEA分析潜在失效模式,提出改进措施让不良率从5000ppm降到500ppm以下。
步骤 1
数据收集与分析
本步骤对工业工程问题进行系统分析,识别问题本质和改进机会。工业工程以系统优化为核心,通过数据分析和流程建模,从效率、质量、成本多个维度评估系统现状。采用价值流图、流程分析、数据统计等方法诊断问题根源。

• 进行现场调研和数据收集,使用秒表法、工作抽样法或视频分析法获取作业时间数据
• 绘制价值流图(VSM)或流程图,识别瓶颈工序、浪费环节和改进点
• 运用统计学方法进行数据分析,区分偶然波动和异常因素,确定关键影响变量
产出:现状分析报告(含数据收集表、流程图/价值流图、问题诊断、改进机会分析) | 质量标准:数据充分、分析深入、问题定位准确、改进机会清晰
步骤 2
控制图设计
本步骤进行工业工程系统的方案设计,将改进目标转化为具体的实施方案。工业工程设计强调系统化和整体优化,需在生产效率、产品质量、运营成本和人员安全之间寻求平衡。采用IE方法和工具设计最优解决方案。

• 运用SLP(系统布置设计)方法或流程程序分析进行设施布局和工艺流程设计
• 应用人因工程学原理进行工作站设计,考虑人体测量数据、作业姿势和认知负荷
• 设计质量管理体系或生产控制系统,明确运行流程、职责分工和评价指标
产出:设计方案(含布局图、流程图、作业标准、人员配置、指标体系) | 质量标准:方案系统、方法科学、参数合理、可操作性强
步骤 3
过程能力分析
本步骤对工业工程问题进行系统分析,识别问题本质和改进机会。工业工程以系统优化为核心,通过数据分析和流程建模,从效率、质量、成本多个维度评估系统现状。采用价值流图、流程分析、数据统计等方法诊断问题根源。

• 进行现场调研和数据收集,使用秒表法、工作抽样法或视频分析法获取作业时间数据
• 绘制价值流图(VSM)或流程图,识别瓶颈工序、浪费环节和改进点
• 运用统计学方法进行数据分析,区分偶然波动和异常因素,确定关键影响变量
产出:现状分析报告(含数据收集表、流程图/价值流图、问题诊断、改进机会分析) | 质量标准:数据充分、分析深入、问题定位准确、改进机会清晰
步骤 4
FMEA分析
本步骤对工业工程问题进行系统分析,识别问题本质和改进机会。工业工程以系统优化为核心,通过数据分析和流程建模,从效率、质量、成本多个维度评估系统现状。采用价值流图、流程分析、数据统计等方法诊断问题根源。

• 进行现场调研和数据收集,使用秒表法、工作抽样法或视频分析法获取作业时间数据
• 绘制价值流图(VSM)或流程图,识别瓶颈工序、浪费环节和改进点
• 运用统计学方法进行数据分析,区分偶然波动和异常因素,确定关键影响变量
产出:现状分析报告(含数据收集表、流程图/价值流图、问题诊断、改进机会分析) | 质量标准:数据充分、分析深入、问题定位准确、改进机会清晰
步骤 5
改进方案与报告
本步骤对工业系统进行优化改进,在满足约束条件下寻求系统性能的最优解。工业工程优化涉及多目标决策,需在效率、质量、成本、交付等多个目标间寻求平衡。采用精益生产、六西格玛、运筹学等方法持续改进系统性能。

• 确定优化目标和约束条件,建立优化模型,选择合适的优化算法(遗传算法、粒子群、模拟退火等)
• 运用精益生产工具(5S、SMED、看板、TPM等)消除浪费,提高生产效率和质量
• 进行多方案比较和敏感性分析,评估优化效果和风险,选择技术经济最优方案
产出:优化方案(含优化模型、改进措施、效果预测、成本效益分析、实施路线图) | 质量标准:目标明确、方法得当、效果显著、可实施性强

Steps

Step 1
Data Collection and Analysis
This step performs systematic analysis of industrial engineering problems, identifying the nature of problems and improvement opportunities. Industrial engineering focuses on system optimization, evaluating system status from efficiency, quality, cost dimensions through data analysis and process modeling. Diagnose root causes using value stream mapping, process analysis, statistical data and other methods.

• Conduct site investigation and data collection, obtain operation time data using stopwatch method, work sampling or video analysis
• Draw value stream map (VSM) or process flow chart, identify bottleneck processes, waste links and improvement points
• Apply statistical methods for data analysis, distinguish random fluctuations from abnormal factors, determine key influencing variables
Deliverable: Current state analysis report (including data collection table, process flow/VSM, problem diagnosis, improvement opportunity analysis) | Quality standard: Sufficient data, in-depth analysis, accurate problem定位, clear improvement opportunities
Step 2
Control Chart Design
This step performs scheme design of industrial engineering systems, translating improvement goals into specific implementation plans. Industrial engineering design emphasizes systematization and overall optimization, requiring balance among production efficiency, product quality, operating cost and personnel safety. Design optimal solutions using IE methods and tools.

• Apply SLP (Systematic Layout Planning) method or process procedure analysis for facility layout and process flow design
• Apply ergonomics principles for workstation design, considering anthropometric data, working posture and cognitive load
• Design quality management system or production control system, clarify operational processes, responsibility division and evaluation indicators
Deliverable: Design scheme (including layout diagram, process flow chart, work standards, staffing, indicator system) | Quality standard: Systematic scheme, scientific method, reasonable parameters, strong operability
Step 3
Process Capability Analysis
This step performs systematic analysis of industrial engineering problems, identifying the nature of problems and improvement opportunities. Industrial engineering focuses on system optimization, evaluating system status from efficiency, quality, cost dimensions through data analysis and process modeling. Diagnose root causes using value stream mapping, process analysis, statistical data and other methods.

• Conduct site investigation and data collection, obtain operation time data using stopwatch method, work sampling or video analysis
• Draw value stream map (VSM) or process flow chart, identify bottleneck processes, waste links and improvement points
• Apply statistical methods for data analysis, distinguish random fluctuations from abnormal factors, determine key influencing variables
Deliverable: Current state analysis report (including data collection table, process flow/VSM, problem diagnosis, improvement opportunity analysis) | Quality standard: Sufficient data, in-depth analysis, accurate problem定位, clear improvement opportunities
Step 4
FMEA Analysis
This step performs systematic analysis of industrial engineering problems, identifying the nature of problems and improvement opportunities. Industrial engineering focuses on system optimization, evaluating system status from efficiency, quality, cost dimensions through data analysis and process modeling. Diagnose root causes using value stream mapping, process analysis, statistical data and other methods.

• Conduct site investigation and data collection, obtain operation time data using stopwatch method, work sampling or video analysis
• Draw value stream map (VSM) or process flow chart, identify bottleneck processes, waste links and improvement points
• Apply statistical methods for data analysis, distinguish random fluctuations from abnormal factors, determine key influencing variables
Deliverable: Current state analysis report (including data collection table, process flow/VSM, problem diagnosis, improvement opportunity analysis) | Quality standard: Sufficient data, in-depth analysis, accurate problem定位, clear improvement opportunities
Step 5
Improvement Scheme and Report
This step performs optimization and improvement of industrial systems, seeking optimal solutions for system performance under constraint conditions. Industrial engineering optimization involves multi-objective decision-making, requiring balance among efficiency, quality, cost, delivery and other objectives. Continuously improve system performance using lean production, six sigma, operations research and other methods.

• Determine optimization objectives and constraints, establish optimization model, select appropriate optimization algorithms (genetic algorithm, particle swarm, simulated annealing, etc.)
• Apply lean production tools (5S, SMED, Kanban, TPM, etc.) to eliminate waste, improve production efficiency and quality
• Conduct multi-scheme comparison and sensitivity analysis, evaluate optimization effects and risks, select the optimal techno-economic scheme
Deliverable: Optimization scheme (including optimization model, improvement measures, effect prediction, cost-benefit analysis, implementation roadmap) | Quality standard: Clear objectives, appropriate methods, significant effects, strong implementability
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