~~NOTOC~~ ======== Design For Experiments ======== ==== General Information ==== |Name|Design For Experiments| |Abbreviation|DFE| |Learning Cost|160| |Playing Cost|500| |Suggested Phases|2,3,4| == Engineers == ^ Mechanical Engineer ^ Industrial Design ^ System Engineer ^ Electrical Engineer ^ Production Engineer ^ Software Engineer ^ |✔|✔|✔|✔|✔|✔| == Technique and Issue Views == ^ BusinessNeeds ^ Stakeholder ^ Stakeholder Needs ^ System Requirements ^ System Structure Architecture ^ |✗|✗|✗|✔|✗| ^System Functional Architecture ^Detail Hardware Design ^Detail Service Design ^Detail Software Design ^Manufacturing Operations ^ |✗|✔|✔|✔|✔| == Technique Traits == ^Identify Stakeholders ^Elicit Needs ^Remove Ambiguity ^Layman's Terms ^Technical Terms ^Teamworkings^ | 0 | 1 | 2 | 0 | 3 | 1 | ^Traceability ^Prioritizing ^Exploring Breadth ^Inside the Box ^Outside the box ^ V&V^ | 3 | 1 | 4 | 1 | 1 | 4 | == Verification and Validation == ^Analysis ^Calculus ^Inspection ^Demonstration ^Test ^ | ✗ | ✔ | ✗ | ✗ | ✗ | ==== Description ==== Design of experiments, referred to as DOE, is a systematic approach to understanding how process and product parameters affect response variables such as processability, physical properties, or product performance. Unlike quality, mechanical, or process tools, DOE is a purely mathematical tool used to define the importance of specific processing and/or product variables, and how to control them to optimize the system performance while maximizing properties [1][2]. Invented by Ronald A. Fisher in 1935 in his book “The design of experiments” [3], DOE uses statistical methodology to analyze data and predict product property performance under all possible conditions within the limits selected for the experimental design. Next to understanding how a particular variable affects product performance, interactions between different process and product variables are identified. DOE is a technique or procedure to generate the required information with the minimum amount of experimentation [1]. A DoE approach can greatly improve the efficiency in screening for suitable experimental conditions, for example, for cell culture, nutrient complement, factor level, optimization of a process, or robustness testing [4]. Design of experiments is best used to answer questions such as: “What factor contributes most to the problem?” and “what is the most excellent arrangement of aspect values to reduce dissimilarity in response?” [5]. {{:fDesign For Experiments.png|}}