A Simplified Systematic Method of Acquiring Design Specifications From Customer Requirements

2007 ◽  
Author(s):  
Nuogang Sun ◽  
Youyun Zhang ◽  
Xuesong Mei
Keyword(s):  
Design Process ◽  
Systematic Approach ◽  
Rough Set Theory ◽  
Fuzzy Rule ◽  
Concept Design ◽  
Customer Requirements ◽  
Design Specifications ◽  
Mapping Rules ◽  
Value Range ◽  
Clustering Approach

Faithfully obtaining design specifications from customer requirements is essential for successful designs. The natural lingual, inexact, incomplete and vague attributes of customer requirements make it very difficult to map customer requirements to design specifications. In general design process, the design specifications are determined by designers based on their experience and intuition, and often a certain target value is set for a specification. However, it is on one hand very difficult, on the other hand unreasonable, so a suitable limit range rather than a certain value is preferred at the beginning of design, especially at the concept design process. In this paper, a simplified systematic approach of transforming customer requirements to design specifications is proposed. First, a two-stepped clustering approach for grouping customer requirements and design specifications based on HOQ matrix is presented, by which the mapping is limited to within each group. To further simplify the inference mapping rules of customer requirements and design specifications, the minimal condition inference mapping rules for each design specification are extracted based on rough set theory. In the end, a suitable value range is determined for a specification by applying the fuzzy rule matrix.

A Simplified Systematic Method of Acquiring Design Specifications From Customer Requirements

2009 ◽  
Vol 9 (3) ◽  
Author(s):  
Nuogang Sun ◽  
Xuesong Mei ◽  
Youyun Zhang
Keyword(s):  
Design Process ◽  
Systematic Approach ◽  
Rough Set Theory ◽  
Fuzzy Rule ◽  
Concept Design ◽  
Customer Requirements ◽  
Design Specifications ◽  
Mapping Rules ◽  
Value Range ◽  
Clustering Approach

Faithfully obtaining design specifications from customer requirements is essential for successful designs. The natural lingual, inexact, incomplete, and vague attributes of customer requirements make it very difficult to map customer requirements to design specifications. In general design process, the design specifications are determined by designers based on their experience and intuition, and often a certain target value is set for a specification. However, it is on one hand very difficult, on the other hand unreasonable, so a suitable limit range rather than a certain value is preferred at the beginning of design, especially at the concept design process. In this paper, a simplified systematic approach of transforming customer requirements to design specifications is proposed. First, a two-stepped clustering approach for grouping customer requirements and design specifications based on the house of quality matrix is presented, by which the mapping is limited to within each group. To further simplify the inference mapping rules of customer requirements and design specifications, the minimal condition inference mapping rules for each design specification are extracted based on rough set theory. In the end, a suitable value range is determined for a specification by applying the fuzzy rule matrix.

The Datum Flow Chain: A Systematic Approach to Assembly Design and Modeling

1998 ◽  
Author(s):  
R. Mantripragada ◽  
D. E. Whitney
Keyword(s):  
Design Process ◽  
Systematic Approach ◽  
Assembly Planning ◽  
Assembly Process ◽  
Top Down ◽  
Assembly Design ◽  
Key Characteristics ◽  
Assembly Sequences

Abstract In order to be able to lay out, analyze, outsource, assemble, and debug complex assemblies, we need ways to capture their fundamental structure in a top-down design process, including the designer’s strategy for kinematically constraining and locating the parts accurately with respect to each other. We describe a concept called the “Datum Flow Chain” to capture this logic. The DFC relates the datum logic explicitly to the product’s key characteristics, assembly sequences, and choice of mating features, and provides the information needed for tolerance analyses. Two types of assemblies are addressed: Type-1 where the assembly process puts parts together at their prefabricated mating features, and Type-2 where the assembly process can incorporate in-process adjustments to redistribute variation. Two types of assembly joints are defined: mates that pass dimensional constraint from part to part, and contacts that merely provide support. The scope of DFC in assembly planning is presented using several examples. Analysis tools to evaluate different DFCs and select the ones of interest are also presented.

Design for Manufacturability

1995 ◽  
Author(s):  
Mohammed A. Azam ◽  
William P. Holmes
Keyword(s):  
Product Design ◽  
Engineering Design ◽  
Design Process ◽  
Integrated Design ◽  
Concept Development ◽  
Concept Design ◽  
Process Selection ◽  
Cad System ◽  
Product Design Process ◽  
Industrial Designers

Abstract Research has been carried out at Coventry University Centre for Integrated Design on the concept design process and it is funded by the Coventry University Research Fund. An experiment, simulating product design in industry, was conducted by concept designers which were, in turn, acted by student industrial designers and student engineering designers. In general the product design process is a sequential process. The first part of the process is the conceptual phase. This is followed by the engineering design phases which include all the manufacturing information. In this case the downstream engineering design focuses on designs for manufacture and process selection. Information on the requirements of conceptual designers in these areas was collected from these experiments. The information is ultimately to be incorporated into rules in a knowledge base which can be readily accessed by the industrial designer during concept development via a CAD system.

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

2017 ◽  
Vol 9 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Hemant Rana ◽  
Manohar Lal
Keyword(s):  
Learning Styles ◽  
Rough Set ◽  
Incomplete Data ◽  
Missing Values ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Data Mining Tool ◽  
Knowledge Analysis ◽  
Clustering Approach ◽  
Mining Tool

Handling of missing attribute values are a big challenge for data analysis. For handling this type of problems, there are some well known approaches, including Rough Set Theory (RST) and classification via clustering. In the work reported here, RSES (Rough Set Exploration System) one of the tools based on RST approach, and WEKA (Waikato Environment for Knowledge Analysis), a data mining tool—based on classification via clustering—are used for predicting learning styles from given data, which possibly has missing values. The results of the experiments using the tools show that the problem of missing attribute values is better handled by RST approach as compared to the classification via clustering approach. Further, in respect of missing values, RSES yields better decision rules, if the missing values are simply ignored than the rules obtained by assigning some values in place of missing attribute values.

System Design Analytics with Low-Fidelity Models

2021 ◽  
pp. 359-385
Author(s):  
Asko Ellman ◽  
Petter Krus
Keyword(s):  
Product Development ◽  
System Design ◽  
Design Process ◽  
Development Process ◽  
Product Development Process ◽  
Customer Requirements ◽  
Iterative Design

Establishing product requirements for the customer is usually the first step in the product development process. Indeed, identifying and fulfilling customer requirements is the key for successful product development. However, satisfying all the customer requirements is not always possible. Therefore, the best design is the design that fulfils a set of the most important customer requirements. Due to this, design process needs to be agile and iterative. Design and its requirements need to be effectively iterated.

Fuzzy Modeling by Occupancy Degree and Optimal Partition of Projection Using Rough Set Theory

1998 ◽  
Vol 2 (2) ◽  
pp. 54-61
Author(s):  
Youngwan Cho ◽  
◽  
Kichul Lee ◽  
Mignon Park
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy System ◽  
Rough Set Theory ◽  
Fuzzy Rule ◽  
Membership Functions ◽  
Linguistic Information ◽  
Input And Output ◽  
Decision Attributes ◽  
Modeling Data

The rough set theory suggested by Pawlak represents the degree of consistency between conditions and decision attributes of data pairs that have no linguistic information. In this paper, by using this representation feature, we define a measure called the occupancy degree that represents the consistency degree of a premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information. We examine the validity of the proposed method by modeling data pairs randomly generated by a fuzzy system.

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