Skip to main content
SpringerLink
Log in
Book cover

Statistics for Innovation

Statistical Design of "Continuous" Product Innovation

  • Book
  • © 2009

Overview

Editors:
  1. Pasquale Erto

    1. Department of Aerospace Engineering, University of Naples “Federico II”, Italy

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (14 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Design for Innovation

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Analysis of User Needs for the Redesign of a Postural Seat System

      • Stefano Barone, Alberto Lombardo, Pietro Tarantino
      Pages 3-25

    3. Statistical Design for Innovation in Virtual Reality

      • Antonio Lanzotti, Giovanna Matrone, Pietro Tarantino, Amalia Vanacore
      Pages 27-41

    4. Robust Ergonomic Virtual Design

      • Stefano Barone, Antonio Lanzotti
      Pages 43-64

    5. Computer Simulations for the Optimization of Technological Processes

      • Alessandro Baldi Antognini, Alessandra Giovagnoli, Daniele Romano, Maroussa Zagoraiou
      Pages 65-88

  3. Technological Process Innovation

    1. Front Matter

      Pages 89-89
      Download chapter PDF

    2. Design for Computer Experiments: Comparing and Generating Designs in Kriging Models

      • Giovanni Pistone, Grazia Vicario
      Pages 91-102

    3. New Sampling Procedures in Coordinate Metrology Based on Kriging-Based Adaptive Designs

      • Paola Pedone, Daniele Romano, Grazia Vicario
      Pages 103-121

    4. Product and Process Innovation by Integrating Physical and Simulation Experiments

      • Daniele Romano
      Pages 123-143

    5. Continuous Innovation of the Quality Control of Remote Sensing Data for Territory Management

      • Elisabetta Carfagna, Johnny Marzialetti
      Pages 145-160

    6. An Innovative Online Diagnostic Tool for a Distributed Spatial Coordinate Measuring System

      • Fiorenzo Franceschini, Maurizio Galetto, Domenico Maisano, Luca Mastrogiacomo
      Pages 161-176

    7. Technological Process Innovation via Engineering and Statistical Knowledge Integration

      • Biagio Palumbo, Gaetano De Chiara, Roberto Marrone
      Pages 177-190

  4. Innovation of Lifecycle Management

    1. Front Matter

      Pages 191-191
      Download chapter PDF

    2. Bayesian Reliability Inference on Innovated Automotive Components

      • Maurizio Guida, Gianpaolo Pulcini
      Pages 193-211

    3. Stochastic Processes for Modeling the Wear of Marine Engine Cylinder Liners

      • Massimiliano Giorgio, Maurizio Guida, Gianpaolo Pulcini
      Pages 213-230

  5. Research and Innovation Management

    1. Front Matter

      Pages 231-231
      Download chapter PDF

    2. A New Control Chart Achieved via Innovation Process Approach

      • Pasquale Erto, Giuliana Pallotta
      Pages 233-245

    3. A Critical Review and Further Advances in Innovation Growth Models

      • Pasquale Erto, Amalia Vanacore
      Pages 247-260

  6. Back Matter

    Pages 261-264
    Download chapter PDF
Back to top

Keywords

About this book

4. 1. 1 ImportanceofComputerSimulation The importance of experimenting for quality improvement and innovation of pr- ucts and processes is now very well known: “experimenting” means to implement signi?cant and intentional changes with the aim of obtaining useful information. In particular, the majority of industrial experiments have two goals: • To quantify the dependence of one or more observable response variables on a group of input factors in the design or the manufacturing of a product, in order to forecast the behavior of the system in a reliable way. • To identify the level settings for the inputs (design parameters) that are capable of optimizing the response. The set of rules that govern experiments for technological improvement in a ph- ical set-up are now comprehensively labeled “DoE. ” In recent years, the use of - perimentation in engineering design has received renewed momentum through the utilization of computer experiments (see Sacks et al. 1989, Santner et al. 2003), which has been steadily growing in the last two decades. These experimentsare run on a computer code implementing a simulation model of a physical system of int- est. This enables us to explore the complex relationships between input and output variables. Themain advantageofthis is that thesystem becomesmore“observable,” since computer runs are generally easier and cheaper than measurements taken in a physical set-up, and the exploration can be carried out more thoroughly. This is particularly attractive in industrial design applications where the goal is system - timization. 4. 1.

Editors and Affiliations

  • Department of Aerospace Engineering, University of Naples “Federico II”, Italy

    Pasquale Erto

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation