ADVANCED TECHNOLOGIES FOR EFFICIENT PACKAGING PROCESSES

Abstract: This volume provides a comprehensive analysis of advanced robotics as a transformative force in modern manufacturing, focusing on its role in automating production and enhancing operational efficiency. The conceptual framework is grounded in the evolution from fixed automation to adaptive, intelligent systems, integrating cyber-physical architectures, edge–cloud computing, and Industry 4.0 principles. The central challenge addressed is the persistent gap between the technical potential of robotics and its scalable, secure, and interoperable implementation across diverse industrial contexts. The methodology combines theoretical synthesis with empirical examination, incorporating digital twin modeling, predictive maintenance protocols, machine vision–driven quality assurance, and multi-objective optimization across manufacturing hierarchies. Case studies from automotive, electronics, and precision engineering sectors illustrate the application of articulated robots, SCARA systems, autonomous mobile robots, and collaborative platforms in production environments. Comparative performance metrics and integration frameworks provide actionable guidance for deployment. Key results demonstrate that strategic adoption of robotics, when coupled with data-driven analytics, human-robot collaboration, and secure interoperability standards, delivers measurable gains in overall equipment effectiveness, defect reduction, and energy efficiency. The book’s findings have significant implications for both research and practice, offering a structured roadmap for scaling advanced automation while maintaining adaptability, safety, and sustainability in global manufacturing ecosystems. Keywords advanced robotics, manufacturing automation, Industry 4.0, cyber-physical systems, collaborative robots, autonomous mobile robots, SCARA robots, digital twins, predictive maintenance, machine vision, quality assurance, multi-objective optimization, human-robot collaboration, interoperability, sustainable manufacturing

A Novel Predictive Selective Maintenance Strategy Using Deep Learning and Mathematical Programming

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

The emerging Industrial Internet of Things (IIoT) provides industries with an opportunity to collect, aggregate, and analyze data from sensors, including motion control, machine‐to‐machine communication, predictive maintenance, smart energy grid, big data analysis, and other smart connected medical systems. The physical systems and the cyber systems are organically integrated, forming an interdependent IIoT. This system provides us with enormous advantages, but at the same time, it also introduces the main safety challenges in the design and operation phase. To exploit the security threats of IIoT systems, in this paper, we propose a novel security‐by‐design approach for interdependent IIoT environments across two different levels, namely, theory modeling and runtime simulation. Our method theoretically analyzes the cascading failure dynamics of the intentional attack network. Simultaneously, we verified the theoretical results through simulations and gave the risk factors that affect the system’s security to mitigate potential security attack threats. Besides, we prove its applicability through comparative simulation experiments to study application environments that rely on IIoT, which shows that our method helps identify risk factors and mitigate IIoT attacks’ mechanism.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

This article presents an idea of achieving reliability through Industrial Internet of Things (IIoT) for industrial power systems. It proposes hybrid approach for predictive and corrective maintenance. It discusses the self-corrective maintenance (SCM) paradigm as hybrid approach for industrial power systems along with condition-based maintenance approach utilizing IIoT to achieve it. As it is well known that industries pay huge penalty for the down time, and suffer to meet reliability demands for years. Study witnesses its cost in millions of dollars yearly for production disruptions. It can be prevented by proactively following the aggressive maintenance schedule. However, it often becomes expensive as part or service may not be utilized for its full life and failure may occur even in middle of maintenance cycle. On the other hand, condition-based maintenance (CBM) helps utilize the full life and prevents the downtime by predicting the failures ahead. This article reviews current maintenance practices followed by industry leaders and a proposal on self-corrective maintenance based on condition of restorable resources. It is about learning the condition of subsystems by itself and taking corrective action when subsystem is not active. This concept helps reduce manual intervention to correct the problem as well as the maintenance cost. This research also covers the self-uncorrectable issues to be handled by proactively following CBM process through IIoT. This hybrid proposal could be a significant gear shift in maintenance direction for general industry as well as power systems. It can be termed as industry's 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> revolution or Industry 5.0.

Industry 4.0 / Industrial Internet of Things - Related Technologies and Requirements for a Successful Digital Transformation: An Investigation of Manufacturing Businesses Worldwide

[EMBARGOED UNTIL 8/1/2024] Order picking, which involves retrieving items from storage locations for an internal or external customer, is a core function in warehouses and accounts for up to 65 percent of the total operating cost. It is considered as a crucial driver for supply chain performance as improper planning of picking operations leads to inefficient asset utilization and delayed deliveries, which, in turn, adversely affects customer satisfaction, operating cost, and competitiveness. A majority of warehouses adopt a picker-to-parts system (workers travel through a warehouse to retrieve and transport items from storage to packing station), and the fulfillment speed of such order picking system (OPS) depends on the following key decisions - (i) set of orders to be picked in a tour (order batching decision), (ii) assignment of a batch to a picker and order in which their a processed (batch assignment and sequencing decisions), and (iii) route followed x by the picker to collect the orders in each batch (picker routing decision). However, with the growing customer demand and global labor shortage, warehouses are seeking efficient and less labor-intensive order picking systems. Autonomous mobile robots (AMRs) or collaborative robots (cobots) have the potential to alleviate the strain on human workers and expedite order picking operations. However, there are several key operational challenges to address for ensuring an effective collaborative human-robot order-picking system (CHR-OPS), where humans perform item retrieval tasks, and AMRs handle item transportation to the depot. This research aims to improve the fulfillment efficiency of a picker-to-parts CHROPS by optimizing key decisions associated with two warehouse picking strategies, namely, static picking and dynamic picking. In the case of static picking, the items to be retrieved from storage for a given day are known as apriority. On the other hand, the dynamic picking strategy allows for orders to arrive over time (e.g., e-commerce warehouses), and the pick cycle (or picking plan) can be updated in real time. First, we address the problem of optimizing the following key subproblems of a CHR-OPS with a static picking strategy: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts CHR-OPS system employing multiple pickers and AMRs. A mixed integer linear programming model is developed to jointly optimize the three subproblems with the objective of minimizing the total tardiness of all orders. The MILP model is validated and solved to optimality for small instances. However, since the problem under consideration is NP-hard, it is computationally intractable for larger instances. To efficiently handle large instances, we proposed deterministic and stochastic local search algorithms, namely variable neighborhood descent and a new variant of simulated annealing. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human{robot team composition, AMR speed, AMR capacity and warehouse layout. Subsequently, we address the CHR-OPS with dynamic picking and developing interventionist picking algorithms for a multi-robot multi-human setting. Specifically, we propose two interventionist policies for dynamic collaborative order picking, namely, the collaborative human-robot interventionist picking algorithm (CHRIPA) and the collaborative human-robot rule-based interventionist picking algorithm (CHR-RIPA). The evaluation of the policies demonstrates that the proposed rules can improve the overall performance of the system compared to benchmark approaches (human-only dynamic picking and collaborative picking with no intervention). In addition, results indicate CHR-IPA outperforms CHR-RIPA in terms of average tar diness and order completion time, albeit with a slightly higher travel distance for human workers. The results have led to several managerial implications for a collaborative order picking system. Further, the proposed models and algorithms are modular and can be adapted to any warehouse setting by accounting for the relevant parameters such as warehouse layout, AMR capacity and human-robot composition. Finally, the directions for future research have been identified and summarized.

Industrial Internet of Things (IIoT) systems play a pivotal role in enabling real-time monitoring and analysis of equipment to prevent breakdowns and optimize performance. Predictive maintenance stands as a critical component of IIoT systems, allowing industries to anticipate equipment failures, minimize downtime, and enhance overall equipment effectiveness. This paper investigates the potential of Long Range (LoRa) technology in predictive maintenance applications within IIoT systems. Specifically, the study delves into the utilization of LoRa technology to bolster predictive maintenance capabilities, with a focus on a live prediction analysis demonstration conducted on an industrial robotic arm. The research presents a detailed test bed setup for conducting predictive maintenance, which involves capturing data from the robot, transmitting it to a LoRaWAN gateway, enabling prediction of normal or abnormal behavior in the robotic arm. This study provides insights into the efficacy of LoRa technology in enhancing predictive maintenance capabilities within IIoT systems.

Industry 4.0 has become a well-known production paradigm over the last decade. It represents innovation and creativity in research and industry but also aims at including digital innovation into the manufacturing area. Robotics, automation and handling of large data sets are key to fully exploit the potential of Industry 4.0. Within this work, a novel production approach combining collaborative robotics, additive manufacturing and a state-of-the-art control and data acquisition approach is conceptualized and implemented. At Karlsruhe Institute of Technology (KIT), Germany, an exemplary setup is implemented based on the use of four identical robotic systems, fused filament fabrication and databases to capture data from sensors and machinery in real-time. The setup will show the capabilities of state-of-the art flexible, scalable manufacturing concept in order to release the full potential of Industry 4.0 and enhance the flexibility and sustainability of current and future value chains.KeywordsSustainable productionIndustry 4.0Collaborative roboticsAdditive manufacturing

Big data-driven predictive maintenance is becoming a fundamental component of IIoT systems to enable failure predication proactively and streamline the scheduling process. This work examines the intersection of machine learning, digital twin technology, and optimization techniques in the context of increasing predictive maintenance efficiency and effectiveness. Four algorithms were evaluated via live IIoT sensor reading inputs: Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). The performance outcome indicates that XGBoost achieved the highest in fault detection accuracy at 96.4%, followed by CNN at 94.8%, LSTM at 92.3%, and then Random Forest at 90.1%. A blockchain-based federated learning framework was also utilized to facilitate secure and decentralized predictive maintenance and minimize false alarms by 28% compared to conventional methods. Optimization methods such as Koopman observables and Dynamic Mode Decomposition with Control (DMDc) also enhanced system efficiency, reducing computing cost by 35%. Scalability issues with predictive maintenance in large-scale industries are confirmed as part of this study, as well as edge AI integration and reinforcement learning as probable future trends. These results form the basis of the significance of data-driven predictive maintenance in minimizing downtime, optimizing resource utilization, and facilitating cost-efficient industrial processes.

Smart and collaborative industrial IoT: A federated learning and data space approach

Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission.

Maintenance management is an important function under Facilities Management (FM). Industries moved to preventive maintenance models to counteract the inefficiencies of reactive maintenance and further evolved into predictive maintenance (PdM) models. The demand for Industry 4.0 enabled PdM for FM has risen as a result of the industrial revolution and the dynamic nature of the FM functions. Thus, the study aimed to investigate the applicability, benefits, and challenges of applying Industry 4.0 concept for effective PdM in FM. The qualitative research approach was undertaken to accomplish the aim. A comprehensive literature review followed by 15 semi-structured interviews was carried out with experts in the maintenance sector who have Industry 4.0 knowledge. The data was collected from experts in Australia, Qatar, Dubai, Singapore, and Sri Lanka, and analysed through code-based content analysis using NVivo 12. The results demonstrate that there is a huge potential for using Industry 4.0 smart technologies such as big data analytics, Cyber-Physical Systems (CPS), autonomous robots, Cloud Computing, Industrial Internet of Things (IIoT), cybersecurity, Machine Learning (ML), Augmented Reality (AR), Data Mining (DM), system integration, and simulation for PdM under FM. Applying Industry 4.0 concept for effective PdM under FM provides significant benefits such as the deployment of a zero-failure strategy, establishment of machine-to-machine communication and interaction, detection of early anomalies and extended equipment lifetime. Lack of technological knowledge, capital, data management, employees’ interest, integration between systems, standardized procedures, and internet access are identified key challenges.

ADNOC Onshore X Field is in the southwest of Abu Dhabi city. This field has currently 01 Centralized Processing Facility, 3 Remote Degassing Stations, 15 oil gathering stations and two satellite pumping stations and more than 1000 oil producing wells. The current sustainable oil production from this Field is around 570,000 STBOPD. Most of the remote gathering stations are manned during daytime and operator is present at site to support operational and maintenance activities. The objective of this case study was to adopt lean maintenance practices and take advantage of the digitalization projects completed in ADNOC Onshore and optimization the resources in both operations and maintenance Disciplines. In oil field, the assets are remotely located which possess a great challenge related to logistics and resource mobilization and lot of time is consumed in front end troubleshooting during any operational disturbance or equipment downtime. The objective of this paper is to share the outcome of a pilot project executed at one of the giant oil fields in ADNOC Onshore for the de-manning/resource optimization at one of remote facility by applying advance maintenance approaches and Industry 4.0 solutions. The methods and solutions utilized to achieve this objective are TPM (Total Productive Maintenance), Autonomous Maintenance, Operator Driven Reliability, Connected Worker, Deployment of IIoT (Industrial Internet of Things) for remote monitoring and AI based predictive maintenance model for operator support and training. These satellite facilities are located at 45 minutes of driving distance from parent facility and are designed to be manned during day times. The objective was to lead this operation to unmanned and minimize the mobilization towards this facility by optimizing the operation and maintenance activities being conducted for this site. Following methodologies were adopted to during the formulation of execution strategy. All operational and low critical activities performed maintenance team was analyzed and a solution was developed to adopt Autonomous Maintenance / TPM (Total Productive Maintenance) Philosophy. ORS was achieved with Connected Worker Solution. Cost Effective IIoT solution were deployed for remote monitoring at parent facility. AI based decision model based on FMEA and RCM outcomes was deployed over the existing Process Information Solution to facilitate the predictive maintenance and ease in operator and maintenance technician decision process. After conducting the analysis and assessing the initial functionality of ODR and IIoT solution, following are the benefits from this approach. 10% of total annualized manhours were optimized by shifting low critical activities from maintenance to operations. 30% reduction in movement by operations and maintenance staff towards that remote facility leading to 8000KG/Month reduction of CO2 emission. Low critical maintenance and operations activities plan designed in a way that justifies the requirement of an operator during the daytime performing operational and maintenance activities. Operator takes greater ownership of the equipment and enhancement equipment functionality, failure modes and corrective actions. Maintenance Team only responsible for low frequency and high critical activities. This paper will describe the optimized operation of remote facilities with the application of advance maintenance strategies e.g., TPM, ODR and industrial solutions offered by advancement in digitalization such as IIoT, connected worker, remote data monitoring and AI techniques based on plant data. Lesson learnt and challenges faced in adopting this change will also be showcased.