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008			220409s2022 sz | s |||| 0|eng d
020			_a9783030862671
024	7		_a10.1007/978-3-030-86267-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA279 _b2022 EB
100	1		_aShina, Sammy G. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683487
245	1	0	_aIndustrial Design of Experiments : _bA Case Study Approach for Design and Process Optimization _cby Sammy Shina
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXXI, 368 páginas) _b86 ilustraciones, 24 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9140162 _aDiseño industrial
650		7	_2embne _9147799 _aDiseño experimental
776	0	8	_iPrinted edition: _z9783030862664
776	0	8	_iPrinted edition: _z9783030862688
776	0	8	_iPrinted edition: _z9783030862695
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-86267-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _eIG _zSI