Advanced Manufacturing Methods: Smart Processes and Modeling for Optimization

211 Pages·2022·12.37 MB·English

by  Catalin I. Pruncu, Jamal Zbitou

Advanced Manufacturing Methods
Advanced Manufacturing Methods: Smart Processes and Modeling for Optimization
describes developments in advanced manufacturing processes and applications
considering typical and advanced materials. It helps readers implement manufactur-
ing 4.0 production techniques and highlights why a consolidated source and robust
platform are necessary for implementing machine learning processes in the manu-
facturing sector.
• Discusses the industrial impact of manufacturing process
• Provides novel fundamental manufacturing solutions
• Presents the various aspects of applications in advanced materials in cor-
relation of physical properties with macro-, micro- and nanostructures
• Reviews both classical and artificial manufacturing when applied with
typical and novel innovative materials
Aimed at those working in manufacturing, mechanical and optimization of manu-
facturing processes, this work provides readers with a comprehensive view of cur-
rent development in, and applications of, advanced manufacturing.

Advanced Manufacturing
Methods
Smart Processes and Modeling
for Optimization
Edited by
Catalin I. Pruncu and Jamal Zbitou
First edition published 2023
by CRC Press
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ISBN: 978-0-367-37089-3 (hbk)
ISBN: 978-1-032-32636-8 (pbk)
ISBN: 978-0-367-82238-5 (ebk)
DOI: 10.1201/9780367822385
Typeset in Times
by KnowledgeWorks Global Ltd.
Contents
Preface......................................................................................................................vii
Editor Biographies ....................................................................................................xi
List of Contributors ................................................................................................xiii
Chapter 1 Enabling Smart Manufacturing with Artificial Intelligence
and Big Data: A Survey and Perspective .............................................1
Huu Du Nguyen, Kim Phuc Tran, Philippe Castagliola,
and Fadel M. Megahed
Chapter 2 Green Applications with an Advanced Manufacturing
Method: Cold Spray Deposition Technology .....................................27
Koray Kılıçay, Salih Can Dayı, Esad Kaya,
and Selim Gürgen
Chapter 3 Multi-Criteria Decision-Making Applications in Conventional
and Unconventional Machining Techniques ......................................57
Şenol Bayraktar and Erhan Şentürk
Chapter 4 Taguchi-Based GRA Method for Multi-Response
Optimization of Spool Bore in EHSV Made Up
of Stainless Steel 440C ......................................................................83
Pranav R., Md. I. Equbal, Azhar Equbal, and Kishore K.
Chapter 5 Wearing Behaviour of Electrodes during EDM
of AISI 1035 Steel ............................................................................101
Azhar Equbal and Md. Asif Equbal
Chapter 6 Achieving Optimal Efficiency in Manufacturing
through Reinforced PA 3D Printed Parts
Generated by FDM Technology .......................................................113
Aissa Ouballouch, Rachid El Alaiji, Mohammed Sallaou,
Aboubakr Bouayad, Hamza Essoussi, Said Ettaqi,
and Larbi Lasri
v
vi Contents
Chapter 7 Characterization of Effect of Cellular Support Structures
in Selective Laser Melting Using Stainless Steel 316L ....................143
M. Abattouy, M. Ouardouz, and H. Azzouzi
Chapter 8 Using Carbon Nanotubes for Advanced Manufacturing
of Antibiofilm Materials ...................................................................159
M. Gomes, R. Teixeira-Santos, L. C. Gomes, and F. J. Mergulhão
Chapter 9 Development of a Novel Nanocomposite Coating
for Tribological Applications ...........................................................175
Arti Yadav, Muthukumar M, and M. S. Bobji
Index ......................................................................................................................195
Preface
Today, the application of artificial intelligence (AI) is very broad, and its increase
was noted especially in areas like understanding of natural language, visual recog-
nition, robotics, autonomous system, machine learning, design and manufacturing
and other critical fields. AI has evolved massively, thanks in particular to the emer-
gence of Cloud Computing and Big Data that are capable of storing and linking the
enormous amount of data from the critical sector for improving daily life such as
manufacturing. The present book will highlight the recent progress in fundamental
research in advanced manufacturing methods, integrating various aspects from syn-
thesis to applications of advanced materials and providing a correlation of physical
properties with macro, micro and nanostructures, which is a great interest for the
academic and industrial readers. Moreover, it will provide a cutting-edge research
from around the globe in this field. Current status, trends, future directions, oppor-
tunities, etc., will be discussed, making it friendly for beginners and young research-
ers. This book will present and discuss new studies that incorporate some modern
techniques such as AI, multi-criteria decision and novel advanced material from
macro to microscale representative of a society which main desire is to achieve net
zero emission by 2050. The state of art of each research field was presented briefly
and concisely in each chapter. It makes this book a desired tool for the university in
order to accommodate the new students with novel knowledge in advanced manu-
facturing that are based in AI and novel material. It can also be useful for training
courses, engineers, PhD students and other researchers.
This book is composed of nine chapters:
Chapter 1 deals with manufacturing incorporating AI and Big Data, especially for
the development of Industry 4.0. This chapter is organized as follows: firstly, we find
an introduction about the use of internet of things (IoT) technologies in specific indus-
trial applications such as factories, manufacturing, facilities and warehouses. The IoT
(like RFID technology) can create new business models by improving productivity,
exploiting analytics for innovation, maximizing operational efficiency, optimizing
business operations and protecting systems. Secondly, were presented a part reserved
for Big Data from sensors and IoT devices. As known, a large number of special
sensors are used to collect data in smart manufacturing, in which devices are inde-
pendent of each other. The fourth part of this chapter is about the application of AI in
the industry, which makes the manufacturing sector more smart. The AI algorithms
are able to learn from data; enhance themselves by learning new heuristics. After
the presentation of the concepts, applications and current researches of the IoT, Big
Data, and AI for smart manufacturing in the Industry 4.0 era are revealed. We have
indicated opportunities and further research perspectives of these important factors
of industrial intelligent manufacturing. In the last section, we presented a case study
linked to the application of AI algorithms for the remaining useful lifetime (RUL)
prognostic of a product.
Chapter 2 discusses the use of cold spray (CS) technology in detail and its green
applications are examined. CS technology is a thermal spray coating method in
vii
viii Preface
which dense coating layers can be produced with a high deposition rate. Deposition
layers are produced by the mechanical locking mechanism, which is formed as a
result of the impact of the powder particles sprayed from the nozzle at supersonic
speeds on the substrate. It is defined as cold since the accelerated sprayed pow-
der particles are solid state due to the process temperature below powder melting
temperature. Thanks to the important advantages it provides, this method can also
be used in advanced manufacturing methods such as additive manufacturing and
innovative methods such as repairing damaged parts. For this reason, it is defined
in advanced manufacturing methods and can also be evaluated in the green applica-
tions class due to its environmental effects.
Chapter 3 is focused on the application of multi-criteria decision making
(MCDM) techniques when is applied to conventional and non-conventional machin-
ing techniques. The use of MCDM methods is based on modeling and analysis of
decision processes according to specific criteria. MCDM is preferred as an assist
tool in determining the most appropriate performance conditions by reducing cost
and time in manufacturing. This chapter discusses the different techniques VIKOR,
COPRAS, MULTIMOORA, WASPAS, EVAMIX, OCRA and MABAC, which are
still being developed and current studies on processing in the literature related to
these techniques are presented in detail and comparatively. The VIKOR method
is widely preferred in the literature in studies on machinability, COPRAS covers
qualitative and quantitative features and allows the selection of alternatives among
the results. MULTIMOORA is used for a ratio system in which the response of the
alternative on a target is compared with a denominator representing all the alter-
natives of the target. WASPAS is a multi-response appropriate decision-making
method. EVAMIX, which is among the decision-making analysis approach sys-
tems, reduces the selection time or decision-making process. OCRA is used to cal-
culate the performance of alternatives in performance and efficiency measurement
and analysis problems. And for the MABAC method, it is used to determine the
most suitable alternatives.
Chapter 4 discusses and presents the optimal WEDM process for machining of
the spool bore, which is a critical component in EHSV. The dimension of spool bore
must be precisely controlled in order to maintain the smooth functioning of the servo
system. Therefore, there is required to have an optimized process input to the WEDM
process for precise and controlled machining of spool bore. The study contains a
detailed experimental investigation that was performed to obtain an optimal combi-
nation of process parameters for the WEDM of a spool bore for a type II EHSV. The
spool bore is made by machining of stainless steel of grade 440C. Experiments were
designed in accordance with Taguchi array. The significance of parameters affecting
the quality characteristics is established using ANOVA. Grey-based Taguchi method
is employed for multi-response optimization method.
Chapter 5 is concentrated on the study of electrical discharge machining
(EDM), which is a precise machining technique where machining is done by a
series of repetitive sparks between electrode and workpiece. Since the perfor-
mance of electrodes is important in EDM, it is necessary to understand the com-
plex wearing behavior of electrodes obtained during machining. In this study, it
Preface ix
was concluded that the wear of electrode is affected by a number of factors. By
consequent, it is recommended to impose an appropriate control for the machin-
ing factors used in EDM. To understand that wear of copper electrode (tool wear
ratio, TWR), the machining of AISI 1035 steel were used as case study. After the
determination of significant EDM factors, affecting the TWR, an optimal set of
factors that yield lower TWR was determined using the main effect plot and desir-
ability function approach.
Chapter 6 deals with additive manufacturing (AM) technologies such as fused
deposition modeling (FDM). In this chapter, we have provided a study on the effect
of PPs when using the FDM technique on mechanical properties, dimensional
accuracy, surface roughness and total cost is investigated. Hence, an experimen-
tal method for AM of chopped glass reinforced polyamide (GRPA) and chopped
Kevlar reinforced polyamide (KRPA) is presented. These PPs include extrusion
temperature (ET), layer thickness (LT) and print speed (PS). In the conducted study,
a detailed investigation of performance and quality of 3D printed polyamide com-
posites (with chopped glass fiber and Kevlar fiber reinforcement) was presented. The
comparison of these composites with fabricated additively neat polyamide and ABS
parts and those processed by injection molding was carried out. By consequent,
the dimensional accuracy of both PAs was assessed and found to be influenced by
extrusion temperature and layer thickness more than print speed and reinforcement.
KRPA surface roughness was largely affected by process parameters more than
GRPA. Total cost was found to be notably influenced by print speed, layer thickness
and nature or reinforcement.
Chapter 7 discusses a full factorial design of experiment (DOE) for cone sup-
port, tree support and different cellular support structures manufactured from
stainless steel 316L using selective laser melting for selected geometric control
factors. Then digital microscopy is used, which enables to study of upper surface
quality. The morphology of surface was further examined through cross section-
ing and revealing the deformation mechanisms too. Afterward, a removability
evaluation of every sample from the platform was investigated. The purpose of
this study was to compare the features of two distinct types of support structures
(tree and cellular supports). Each support type was manufactured from 316L stain-
less steel as control material.
Chapter 8 is focused on the field of surface engineering and advanced manufac-
turing, carbon nanotubes (CNTs) which have been drawing industrial attention not
only due to their unique structural, mechanical and electrical properties but also to
their antimicrobial activity. The high antimicrobial activity of CNT-nanocomposites
was reported against a broad spectrum of microorganisms and their potential for
medical and water treatment applications was demonstrated. Also, the significant
fouling resistance of these nanocomposites was proven at distinct levels, including
in the development of marine AF or FR coatings, water treatment, and industrial
processes such as filtration.
Chapter 9 focuses on the manufacturing of nanocomposite coating by introduc-
ing the development of a novel nanocomposite coating with ordered porous alumina
(NPA) as a matrix embedded with aligned metal (Cu) nanorods. This was achieved