Design of experiments
Of attendees in this course in 2021 recommend it.
Attendees are satisfied with this trainings.
(Average rating on all sessions in 2021)
- Know how to use Designs of Experiments in order to precisely choose nominal values and tolerances of input factors so that the variations of the output factor varies within predefined limits.
Learning operational objectives
- Know how to build, analyze a design of experiments and leverage its capabilities:
- simple effects,
- effects of order 2 or above,
- search of optimum condition,
- response surface,
- % of contribution.
- Depending on the context and on the applications of the trainees, know how to choose the most appropriate resolution approach:
- matrix calculation-based resolution method,
- traditional resolution method.
- Become proficient with the theory and the practical implementation of full factorial DOEs and of Taguchi’s fractional factorial DOEs (diminution of the number of trials).
- Know how to discover and take into account the relevant input factors having an influence on the output signal.
- Know how to create response surfaces based on a design of experiment.
- Know how to prepare, realize and analyze a DOE that has mixed levels and non-linearities.
- Know how to evaluate and take into account the error present in the results of a DOE.
- Know how to optimize a product/process in order to minimize the impact of noise factors.
The "Pluses" of the training session
- Training tailored on examples of internal products and manufacturing processes of the organizing company (Case study used if necessary). Workshops made on these examples.
- The examples and methods treated in this training cover at the same time usage of DOE for optimization of products (design office, laboratories, project quality…) and for optimization of manufacturing processes (industrial department, production, quality, etc.).
- This training implements a « matrix-based » method to solve DOEs (traditionally not treated in this type of training sessions). It allows more calculation power, more flexibility and ease of update of the DOE after changes.
- Theoretical parts organized in a dynamic and interactive way (workshops in subgroups, quizzes, questions, etc.).
- This training includes a 6-months period of post training support (email exchanges, webinars, phone, response time depending on the trainer’s availability).
Organization and resources
- Each attendee receives a training manual in English as well as exercise and workshop files used during the session.
- For face-to-face training, the room must have a video projector as well as a flipchart and a whiteboard with coloured markers in working order.
- The attendees must dispose of a laptop computer, connected to the internet, with MS Excel 2007 or more installed.
- Level assessment quizzes for each attendee at the beginning and the end of the training session.
- Hot satisfaction questionnaire completed by each attendee at the end of the training.
- Cold evaluation questionnaire, to evaluate achievements realized after the training completed 3 months after training, by each attendee, with their hierarchy.
- Anyone involved in the realization of Designs of Experiments for product and/or process optimization.
- Attendees have successfully completed, at minimum, a “bachelor” academic curriculum in engineering, mathematics, sciences, industrial engineering.
- Participants should have an experience of the products and manufacturing processes of the company organizing the training.
- At least 1 year professional experience in either production, project management, industrial engineering, quality, product design and engineering.
- Holds master’s diploma from top tier engineering school (Centrale, INSA, Arts et Métiers, UTC). Equivalent to Master's degree in industrial engineering.
- 20 years experience in automotive industry in industrial statistics (MSA, SPC, capability studies, design of experiments, tolerancing) and in methodologies of product / process design and engineering (functional analysis, DFMEA, PFMEA, CAD).
- Shared training
Next shared training session
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