iMEC-APCOMS 2019
Proceedings of the 4th International Manufacturing Engineering Conference and The 5th Asia Pacific Conference on Manufacturing Systems
Published on 27. October 2020
Book
Paperback/Softback
XVIII, 619 pages
978-981-15-0952-0 (ISBN)
Description
This book presents the proceedings of the 4th International Manufacturing Engineering Conference and 5th Asia Pacific Conference on Manufacturing Systems (iMEC-APCOMS 2019), held in Putrajaya, Malaysia, on 21-22 August 2019. Covering scientific research in the field of manufacturing engineering, with focuses on industrial engineering, materials, processes, the book appeals to researchers, academics, scientists, students, engineers and practitioners who are interested in the latest developments and applications related to manufacturing engineering.
More details
Series
Edition
2020 ed.
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
55 s/w Abbildungen, 173 farbige Abbildungen
XVIII, 619 p. 228 illus., 173 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 35 mm
Weight
955 gr
ISBN-13
978-981-15-0952-0 (9789811509520)
DOI
10.1007/978-981-15-0950-6
Schweitzer Classification
Other editions
Additional editions
Muhammed Nafis Osman Zahid | Radhiyah Abd. Aziz | Ahmad Razlan Yusoff
iMEC-APCOMS 2019
Proceedings of the 4th International Manufacturing Engineering Conference and The 5th Asia Pacific Conference on Manufacturing Systems
Book
10/2019
Springer
€213.99
Shipment within 3-4 weeks
Content
Optimization of Woven Fabric Production Process on Picanol Omniplus Air Jet Machine Using Taguchi Multi-Responseand Grey Relational Analysis Methods.- Re-designing an Assembly Lines at an Automotive Manufacturing Company.- An assignment model to support the assembly line activities by considering the operator`s unique classification - the computational results.- An Optimization Model for Coal Procurement Networks with Coal Blending Facilities.- Competing Risk Models in Reliability Systems, an Exponential Distribution Model with Gamma Prior Distribution, a Bayesian Analysis Approach.