Integrated Systems (eBook)

Embedded, Signal Processing, and Communication

Abhishek Gudipalli, B. Jaganatha Pandian, N. Amutha Prabha, V. Indragandhi (Herausgeber)

eBook Download: EPUB

2025
872 Seiten
Wiley-Scrivener (Verlag)
978-1-394-31174-3 (ISBN)

Infos

Future-proof your technical expertise with this essential book, offering a comprehensive guide to the latest innovations, trends, and solutions at the critical intersection of embedded systems, signal processing, and communication systems.

Embedded systems play a pivotal role in our modern lives. These specialized devices are discreetly embedded within larger systems, performing specific tasks autonomously. From smartphones and wearables to industrial machinery and automotive control units, embedded systems are ubiquitous. Signal processing enables the extraction of meaningful information from raw data, making it essential for applications such as image and speech recognition, medical diagnostics, and wireless communication. This book focuses on the latest innovations, trends, and challenges encountered in the areas of embedded systems, signal processing, and communication systems. It highlights potential solutions and provides insights into emerging areas, such as signal processing algorithms and communication protocols, making it an invaluable resource for anyone working with integrated systems.

Abhishek Gudipalli, PhD is a Professor in the Instrumentation Department in the School of Electrical Engineering at the Vellore Institute of Technology with more than 15 years of experience. He has published more than 40 journal papers and 15 conference papers. His research interests include image processing, machine learning, IoT, and electric vehicles.

B. Jaganatha Pandian, PhD is a Professor and the Head of the Control and Automation Department in the School of Electrical Engineering at the Vellore Institute of Technology with more than 20 years of teaching experience. He has published more than 20 journal papers and 15 conference papers. His research interests include machine learning, process control, and intelligent systems.

N. Amutha Prabha, PhD is a Professor in the School of Electrical Engineering in the Department of Instrumentation at the Vellore Institute of Technology with more than 25 years of experience. She has published more than 50 papers in national and international journals and conferences. Her research includes wireless LAN networks and discrete time linear systems.

V. Indragandhi, PhD is an Associate Professor in the School of Electrical Engineering at the Vellore Institute of Technology with more than 12 years of research and teaching experience. She has authored more than 100 research articles in leading peer-reviewed international journals and filed three patents. Her research focuses on power generation, inverters, and photovoltaics.

Textauszug

1
Integration of Industrial Robots to Enhance Warehouse Efficiency in an Industry 4.0 Environment Using Digital Twin Technology

Manikandan Pounraj*, James Periyanayagam L., Jennifer Pearline Lobo and Ashwitha P.

Centre of Excellence in Automation, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India

Abstract

An automated warehouse serves as a facility designed for the storage of materials with a comprehensive inventory management system. This system meticulously monitors the loading and unloading of materials with minimal human intervention. The core operations within an automated warehouse are executed through the utilization of sophisticated software tools. This specific system falls within the purview of Manufacturing Execution Systems (MES). The integration of industrial robots is a pivotal aspect of warehouse automation, serving to meet the demands of efficiency while simultaneously reducing space requirements and overall operational costs within the warehouse management system. Industrial robots excel in executing processes swiftly and with precision, enhancing overall operational efficiency. This paper outlines the authors’ approach, which involves the programming of an industrial controller and the utilization of an Industry 4.0 Application Programming Interface (API) to facilitate communication with a 3D warehouse in Factory IO for loading materials. The programming of the industrial controller is carried out using the Sequential Function Chart (SFC) language, adhering to the IEC61131-3 standard.

Keywords: Industry 4.0, digital twin, warehouse automation, industrial robots, CODESYS, factory IO, sequential function chart, sustainable manufacturing

Abbreviations

BL

Blue Lid

BB

Blue Base

FTAPPZ

Fourth robot moving in Z-axis

FTAPPZM

Fourth robot moving 180-degree Z-axis

GB	Green Base

GL

Green Lid

OTAPPX

First robot moving in X–axis

BBWRRS

Retroreflective sensor giving signal for Blue Base

BLPRRS

Retroreflective sensor giving signal for Blue Lid in pick and place area

BWPRRS

Retroreflective sensor giving signal for the completed job

OTAPP

The first two axes pick and place the robot

OTAPPZM

The first robot moving 180-degree Z-axis

TWTAPPX

The second robot moving in X-axis

TWTAPPXM

Second robot rotating 180-degree axes

TWTAPPZ

The second robot moving in Z-axis

TWTAPPZM

Second robot moving 180-degree Z-axis

THTAPPX

The third robot moving in X-axis

THTAPPXM

Third robot rotating 180-degree axes

THTAPPZ

The third robot moving in the Z-axis

THTAPPZM

Third robot moving 180-degree Z-axis

FTAPPX

Fourth robot moving in X–axis

FTAPPXM

Fourth robot rotating 180-degree axes

1.1 Introduction

In the age of Industry 4.0, industrial automation is undergoing a transformative evolution, reshaping the landscape of manufacturing processes globally. Industry 4.0, characterized by the integration of digital technologies, data exchange, and smart manufacturing, holds the potential to not only enhance efficiency and productivity but also contribute significantly to Sustainable Development Goals (SDGs) [1]. One of the core principles of Industry 4.0 is the seamless connectivity between machines, systems, and humans that fosters real-time data exchange, enabling businesses to make informed decisions promptly. Bastas [2] explained the context of SDG-9 that talks about industries focused toward innovation and development of infrastructure. Industrial automation, a key element of Industry 4.0, incorporates advanced technologies like the Internet of Things (IoT) and artificial intelligence that facilitate predictive maintenance, minimizing downtime and optimizing energy consumption. This technology aims to achieve SDG-7 that focuses on affordable and clean energy that becomes more feasible as industries embrace automation to enhance energy efficiency and reduce their environmental footprint. Additionally, it aligns with SDG-8 that focuses on economic growth by fostering innovation and skill development and creating new employment opportunities in the digital economy. Contrary to the misconception that automation leads to job loss, Industry 4.0 promotes a shift in job roles toward more skilled and knowledge-based tasks. This perspective is supported by Beier et al. [3] from their investigation that Industry 4.0 emphasizes resource efficiency through intelligent manufacturing processes and supply chain management. The integration of automation technologies allows for precise control over production, reducing waste and contributing to SDG-12 by promoting sustainable practices within industries.

1.2 Industry Internet of Things and Robot Applications in Warehouse

In Industry 4.0, robots use advanced sensors like retroreflective and vision sensors, enhanced by AI, creating a dashboard for real-time monitoring and diagnostics. The Industry Internet of Things (IIoT) offers highly flexible solutions that enable digitization of factories and their operations. Lodgaard et al. [5] reviewed that many manufacturing and production companies transitioned to the digital era in order to transform the fundamental functioning of their business operations over a span of 5 years. An intelligent and interactive framework for communication with field devices like sensors and programmable logic controllers is provided by the Human Machine Interface (HMI), which can be secured using a secure login and password to stop data interchange with untrusted clients, while analysis can be conducted by extracting data from HMI devices typically positioned near machinery.

1.3 Programming Using CODESYS V3.5 SP19

Akilesh et al. [12] used CODESYS V3.5 SP19, an open-source software, to program an industrial controller to control the bottling unit. SFC adopts a graphical approach, breaking down control tasks into easily understandable steps and transitions that provides a visual representation of the control logic through a series of steps, representing a specific action or state, and transitions that define the conditions for moving between steps. This representation allows for a more straightforward depiction of sequential processes, making it accessible even to those less experienced in programming.

1.4 Creation of Warehouse in Factory IO

In the proposed manufacturing workflow shown in Figure 1.1, the production process begins with the Computerized Numerical Control (CNC) machining of base and lid components, involving various operations to achieve the desired shapes and dimensions followed by a rigorous quality control and inspection phase that ensures that the components meet specified tolerances and quality standards. The assembly process takes place at a pick and place station, utilizing automated systems for the precise placement of lids onto bases verifying alignment checks and subsequent fixing or attachment processes, involving adhesive application and clamping to secure the lid to the base. The assembled units then undergo transfer operations facilitated by conveyor systems or material handling equipment, reaching a stacking station where automated systems stack the products for efficient storage and retrieval. The orchestrated sequence of these operations ensures the streamlined fabrication, assembly, and logistical handling of base and lid components within the warehouse setting.

Figure 1.1 Warehouse model in Factory IO.

The proposed process consists of three zones that involve two fabrication units and one raw material collection unit. Zone 1 and Zone 2 are having fabrication units, while Zone 3 is meant for collection of raw materials that are then transferred to the stacker crane. The machining center serves as a pivotal manufacturing hub, orchestrating the streamlined production of lids and bases from raw materials. The operational sequence commences with the poised articulated robot, anticipating the placement of raw material at the entry bay. Upon the detection of new material, a fluid process ensues, guiding the loading of the raw material into the Computer Numerical Control (CNC) machine, thereby instigating the precise manufacturing of the designated item. It is noteworthy that each item type commands a distinct temporal interval for production, with lids requiring a meticulous 6 seconds and bases exhibiting efficiency at a mere 3 seconds. After completing the operation, the articulated robot executes a precise placement of the finished item onto the exit bay.

In Figure 1.2, the top view of the warehouse is shown. L-shaped conveyors are placed for blue and green base and lid. Four machining centers are optimally placed, with four pairs of two-axis PPM kept for...

Produkt-Details

• Erscheint lt. Verlag: 26.11.2025
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Communication Networks • computer vision • Digital Audio Processing • Embedded Systems • Image Processing • internet of things • Neural networks • Signal Processing • Speech Enhancement • Telecommunication • Video compression • VLSI (Very Large Scale Integration) • Wavelet Multitone Modulation • Wavelets • wireless communication
• ISBN-10: 1-394-31174-5 / 1394311745
• ISBN-13: 978-1-394-31174-3 / 9781394311743