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999			_c200458013 _d76225
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008			220103s2022 sz | s |||| 0|eng d
020			_a9783030862671
024	7		_a10.1007/978-3-030-86267-1 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aTA160
072		7	_aTBD _2bicssc
072		7	_aTEC016020 _2bisacsh
072		7	_aTBD _2thema
090			_aTA160EBK
100	1		_aShina, Sammy. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aIndustrial Design of Experiments _h[electronic resource] : _bA Case Study Approach for Design and Process Optimization / _cby Sammy Shina.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPresentations, Statistical Distributions, Quality Tools and Relationship to DoE -- Samples and Populations: Statistical Tests for Significance of Mean and Variability -- Regression, Treatments, DoE Design and Modelling Tools -- Two-Level Factorial Design and Analysis Techniques -- Three-Level Factorial Design and Analysis Techniques -- DoE Error Handling, Significance and Goal Setting -- DoE Reduction Using Confounding and Professional Experience -- Multiple Level Factorial Design and DoE Sequencing Techniques -- Variability Reduction Techniques and Combining with Mean Analysis -- Strategies for Multiple Outcome Analysis and Summary of DoE Case Studies and Techniques. .
520			_aThis textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis. Industrial Design of Experiments: A Case Study Approach for Design and Process Optimization is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE. Presents classical versus Taguchi DoE methodologies as well as techniques developed by the author for successful DoE; Offers a step-wise approach to DoE optimization and interpretation of results; Includes industrial case studies, worked examples and detailed solutions to problems.
650		0	_aEngineering design.
650		0	_aExperimental design.
650		0	_aIndustrial design.
650		0	_aIndustrial engineering.
650		0	_aProduction engineering.
650		0	_aTechnological innovations.
650	1	4	_aEngineering Design.
650	2	4	_aDesign of Experiments.
650	2	4	_aIndustrial Design.
650	2	4	_aIndustrial and Production Engineering.
650	2	4	_aInnovation and Technology Management.
653		0	_aEngineering -- Experiments
653		0	_aExperimental design
653		0	_aManufacturing processes -- Experiments
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-86267-1 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK