Experimentation, Modelling, and Analysis of Machining of Hard Material

Manjunath Patel G. C; Ganesh R. Chate; Mahesh B. Parappagoudar; Kapil Gupta

doi:10.1007/978-3-030-40102-3_3

Experimentation, Modelling, and Analysis of Machining of Hard Material

Manjunath Patel G. C
, Ganesh R. Chate
, Mahesh B. Parappagoudar
, Kapil Gupta

Mechanical and Industrial Engineering Technology

Peoples Education Society, Bangalore
KLS Gogte Institute of Technology
Goa University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Citations (Scopus)

Abstract

Planning and conducting experiments is the key in effective monitoring of system, which leads to success in manufacturing. The traditional approach of experimental study (i.e. one factor at a time, OFAT) requires more number of experiments and consequently consumes more resources. Moreover, the interpretations and analysis that can be made from the experimental data are also limited. Design of experiments (DOE) is a statistical tool, which uses well-planned set of experiments to collect the input–output data. Further, DOE can be used to analyse the experimental data, establish input–output relations, and optimize the process. Figure 3.1 shows the general steps followed in designing a statistical-based experiment.

Original language	English
Title of host publication	SpringerBriefs in Applied Sciences and Technology
Publisher	Springer
Pages	53-71
Number of pages	19
DOIs	https://doi.org/10.1007/978-3-030-40102-3_3
Publication status	Published - 2020

Publication series

Name	SpringerBriefs in Applied Sciences and Technology
ISSN (Print)	2191-530X
ISSN (Electronic)	2191-5318

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

ASJC Scopus subject areas

Biotechnology
General Chemical Engineering
General Mathematics
General Materials Science
Energy Engineering and Power Technology
General Engineering

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10.1007/978-3-030-40102-3_3

Cite this

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abstract = "Planning and conducting experiments is the key in effective monitoring of system, which leads to success in manufacturing. The traditional approach of experimental study (i.e. one factor at a time, OFAT) requires more number of experiments and consequently consumes more resources. Moreover, the interpretations and analysis that can be made from the experimental data are also limited. Design of experiments (DOE) is a statistical tool, which uses well-planned set of experiments to collect the input–output data. Further, DOE can be used to analyse the experimental data, establish input–output relations, and optimize the process. Figure 3.1 shows the general steps followed in designing a statistical-based experiment.",

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AU - Chate, Ganesh R.

AU - Parappagoudar, Mahesh B.

AU - Gupta, Kapil

PY - 2020

Y1 - 2020

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AB - Planning and conducting experiments is the key in effective monitoring of system, which leads to success in manufacturing. The traditional approach of experimental study (i.e. one factor at a time, OFAT) requires more number of experiments and consequently consumes more resources. Moreover, the interpretations and analysis that can be made from the experimental data are also limited. Design of experiments (DOE) is a statistical tool, which uses well-planned set of experiments to collect the input–output data. Further, DOE can be used to analyse the experimental data, establish input–output relations, and optimize the process. Figure 3.1 shows the general steps followed in designing a statistical-based experiment.

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BT - SpringerBriefs in Applied Sciences and Technology

PB - Springer

ER -

Experimentation, Modelling, and Analysis of Machining of Hard Material

Abstract

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UN SDGs

ASJC Scopus subject areas

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