Statistics for innovation : statistical design of "continuous" product innovation
著者
書誌事項
Statistics for innovation : statistical design of "continuous" product innovation
Springer Verlag, c2009
大学図書館所蔵 全6件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Design for Innovation.- Analysis of User Needs for the Redesign of a Postural Seat System.- Statistical Design for Innovation in Virtual Reality.- Robust Ergonomic Virtual Design.- Computer Simulations for the Optimization of Technological Processes.- Technological Process Innovation.- Design for Computer Experiments: Comparing and Generating Designs in Kriging Models.- New Sampling Procedures in Coordinate Metrology Based on Kriging-Based Adaptive Designs.- Product and Process Innovation by Integrating Physical and Simulation Experiments.- Continuous Innovation of the Quality Control of Remote Sensing Data for Territory Management.- An Innovative Online Diagnostic Tool for a Distributed Spatial Coordinate Measuring System.- Technological Process Innovation via Engineering and Statistical Knowledge Integration.- Innovation of Lifecycle Management.- Bayesian Reliability Inference on Innovated Automotive Components.- Stochastic Processes for Modeling the Wear of Marine Engine Cylinder Liners.- Research and Innovation Management.- A New Control Chart Achieved via Innovation Process Approach.- A Critical Review and Further Advances in Innovation Growth Models.
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