Smart Manufacturing Environments Research Articles

The Big Role of Data Analytics in the Industrial Internet of Things

An IoT architecture is presented in the article which is a predictive way to maintain a group of buses using J1939 sensor data[1]. The authors adopted a minimally viable prototype (MVP) at the Socie´te´ de Transport de l’Outaouais (STO) garage in Gatineau, Canada, which was able to capture around 1 GB of uncompressed J1939 data per bus every day.[1] Data is a combination of the sensor reading and consists of data for the wheel speed, vehicle distance, driver pedal positions, engine information, oil information, coolant information, and transmission fluid information. The authors are trying to come up with a variant of Consensus Self-Organized Models (COSMO) named ICOSMO, which can self-fit the sensor selection for anomaly detection. The fourth industrial revolution has changed the face of ancient manufacturing by bringing in new technologies such as sensors, AI, IoT, and big data analytics. This equipment is used to convert factories into intelligent and interconnected systems that generate a huge amount of data. The data collected, when properly processed, can be utilized to improve decision making and reliability and safety. In addition, it makes predictive maintenance inevitable, thus minimizing the downtime and optimization of the performance[2]. Industrial big data is identified by the ”5V” elements: volume, velocity, variety, veracity, and value. Due to these characteristics, traditional data processing techniques become inadequate, so these circumstances require novel approaches to the treatment of the numerous and diverse data types that are produced in industry 4.0. The Proposed ICOSMO Infrastructure for IoT Predictive Maintenance consists of the following key activities: 1) finding out which sensor classes are the reasons for the faulty components, 2) adaptation of the sensor class (SC) and the sensor prediction capability (SPC) using fault detection abilities and 3) COSMO sensor configuration has to be rearranged after a certain period of time. ICOSMO has been verified with data from a minimally viable prototype (MVP) deployed on buses at the Socie´te´ de Transport de l’Outaouais in Canada. Furthermore, the interviewees said that the framework also includes techniques for the composition and the characterization of industrial big data, which is essential to make a practical predictive maintenance system that can work in smart manufacturing environments

Machine identity authentication via unobservable fingerprinting signature: A functional data analysis approach for MQTT 5.0 protocol

The Industrial Internet of Things (IIoT) and Smart Manufacturing environments face significant challenges in machine identity authentication, crucial for ensuring data integrity and cybersecurity. Traditional methods like One-Class Support Vector Machines (OCSVM) often fall short in sensitivity, specificity, and explainability. This research introduces a novel Functional Data Analysis (FDA)-based model that leverages unique temporal patterns in machine-generated data to authenticate machine identities, adapting to equipment wear and tear. Through a comprehensive comparative analysis, the FDA model demonstrates superior performance and robustness in industrial settings. Additionally, we propose integrating this FDA-based approach with the MQTT 5.0 protocol, enhancing data security in IIoT communications. This work not only advances IIoT cybersecurity but also supports digital twins and predictive maintenance, contributing to a more secure and efficient Smart Manufacturing ecosystem.

Machine Vision for Device Tracking in a Smart Manufacturing Environment Based on Augmented Reality

Machine Vision for Device Tracking in a Smart Manufacturing Environment Based on Augmented Reality

Cybersecurity Challenges for Manufacturing Systems 4.0: Assessment of the Business Impact Level

An ever-growing number of companies are moving toward the Industry 4.0 paradigm, adopting a range of advanced technologies (e.g., smart sensors, big data analytics, and cloud computing) and networking their manufacturing systems. This improves the efficiency and effectiveness of operations but also introduces new cybersecurity challenges. In this article, the impact assessment methodology is applied in the context of manufacturing systems 4.0 (also known as smart manufacturing systems, cyber manufacturing systems, or digital manufacturing systems), thus identifying the critical assets to be protected against cyber-attacks and assessing the business impacts in the case of subtractive and additive technologies. The research design of the single case study with multiple units of analysis is applied. In particular, a large company, a leader in the manufacturing of aeronautical components, is considered a representative case study, and its two main types of manufacturing cells that is, those based on networked computer numerical control machines and 3-D printers, are taken as applicative cases for the methodology. The application of the impact assessment methodology in the manufacturing context 4.0 of aeronautical components represents a useful guide for researchers in the field of cybersecurity and for companies intending to implement it in their smart manufacturing environments. In particular, based on this study, companies can define the critical manufacturing data to protect against cyber-attacks, isolate the business impacts in case of cybersecurity breaches, correlate the identified business impacts with the specific data category, and assess the level of business impacts.

A Finite State Automaton for Green Data Validation in a Real-World Smart Manufacturing Environment with Special Regard to Time-Outs and Overtaking

Since data are the gold of modern business, companies put a huge effort into collecting internal and external information, such as process, supply chain, or customer data. To leverage the full potential of gathered information, data have to be free of errors and corruptions. Thus, the impacts of data quality and data validation approaches become more and more relevant. At the same time, the impact of information and communication technologies has been increasing for several years. This leads to increasing energy consumption and the associated emission of climate-damaging gases such as carbon dioxide (CO2). Since these gases cause serious problems (e.g., climate change) and lead to climate targets not being met, it is a major goal for companies to become climate neutral. Our work focuses on quality aspects in smart manufacturing lines and presents a finite automaton to validate an incoming stream of manufacturing data. Through this process, we aim to achieve a sustainable use of manufacturing resources. In the course of this work, we aim to investigate possibilities to implement data validation in resource-saving ways. Our automaton enables the detection of errors in a continuous data stream and reports discrepancies directly. By making inconsistencies visible and annotating affected data sets, we are able to increase the overall data quality. Further, we build up a fast feedback loop, allowing us to quickly intervene and remove sources of interference. Through this fast feedback, we expect a lower consumption of material resources on the one hand because we can intervene in case of error and optimize our processes. On the other hand, our automaton decreases the immaterial resources needed, such as the required energy consumption for data validation, due to more efficient validation steps. We achieve the more efficient validation steps by the already-mentioned automaton structure. Furthermore, we reduce the response time through additional recognition of overtaking data records. In addition, we implement an improved check for complex inconsistencies. Our experimental results show that we are able to significantly reduce memory usage and thus decrease the energy consumption for our data validation task.

Human–Robot Collaborations in Smart Manufacturing Environments: Review and Outlook

The successful implementation of Human–Robot Collaboration (HRC) has become a prominent feature of smart manufacturing environments. Key industrial requirements, such as flexibility, efficiency, collaboration, consistency, and sustainability, present pressing HRC needs in the manufacturing sector. This paper provides a systemic review and an in-depth discussion of the key technologies currently being employed in smart manufacturing with HRC systems. The work presented here focuses on the design of HRC systems, with particular attention given to the various levels of Human–Robot Interaction (HRI) observed in the industry. The paper also examines the key technologies being implemented in smart manufacturing, including Artificial Intelligence (AI), Collaborative Robots (Cobots), Augmented Reality (AR), and Digital Twin (DT), and discusses their applications in HRC systems. The benefits and practical instances of deploying these technologies are showcased, emphasizing the substantial prospects for growth and improvement in sectors such as automotive and food. However, the paper also addresses the limitations of HRC utilization and implementation and provides some insights into how the design of these systems should be approached in future work and research. Overall, this paper provides new insights into the current state of HRC in smart manufacturing and serves as a useful resource for those interested in the ongoing development of HRC systems in the industry.

MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing Contexts

In this research, we describe the MazeGen framework (as a maze generator), which generates navigation scenarios using Grammatical Evolution for robots or drones to navigate. The maze generator uses evolutionary algorithms to create robotic navigation scenarios with different semantic levels along a scenario profile. Grammatical Evolution is a Machine Learning technique from the Evolutionary Computing branch that uses a BNF grammar to describe the language of the possible scenario universe and a numerical encoding of individual scenarios along that grammar. Through a mapping process, it converts new numerical individuals obtained by operations on the parents’ encodings to a new solution by means of grammar. In this context, the grammar describes the scenario elements and some composition rules. We also analyze associated concepts of complexity, understanding complexity as the cost of production of the scenario and skill levels needed to move around the maze. Preliminary results and statistics evidence a low correlation between complexity and the number of obstacles placed, as configurations with more difficult obstacle dispositions were found in the early stages of the evolution process and also when analyzing mazes taking into account their semantic meaning, earlier versions of the experiment not only resulted as too simplistic for the Smart Manufacturing domain, but also lacked correlation with possible real-world scenarios, as was evidenced in our experiments, where the most semantic meaning results had the lowest fitness score. They also show the emerging technology status of this approach, as we still need to find out how to reliably find solvable scenarios and characterize those belonging to the same class of equivalence. Despite being an emerging technology, MazeGen allows users to simplify the process of building configurations for smart manufacturing environments, by making it faster, more efficient, and reproducible, and it also puts the non-expert programmer in the center of the development process, as little boilerplate code is needed.

Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment

Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product’s physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline’s output. An innovative framework for the early detection of a product’s physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice.

Securing Big Data Integrity for Industrial IoT in Smart Manufacturing Based on the Trusted Consortium Blockchain (TCB)

The smart manufacturing ecosystem enhances the end-to-end efficiency of the mine-to-market lifecycle to create the value chain using the big data generated rapidly by edge computing devices, third-party technologies, and various stakeholders connected via the industrial Internet of things. In this context, smart manufacturing faces two serious challenges to its industrial IoT big data integrity: real-time transaction monitoring and peer validation due to the volume and velocity dimensions of big data in industrial IoT infrastructures. Modern blockchain technologies as an embedded layer substantially address these challenges to empower the capabilities of the IIoT layer to meet the integrity requirements of the big data layer. This paper presents the trusted consortium blockchain (TCB) framework to provide an optimal solution for big data integrity through a secure and verifiable hyperledger fabric modular (HFM). The TCB leverages trustworthiness in heterogeneous IIoT networks of governing end-point peers to achieve strong integrity for big data and support high transaction throughput and low latency of HFM contents. Our proposed framework drives the fault-tolerant properties and consensus protocols to monitor malicious activities of tunable peers if compromised and validates the signed evidence of big data recorded in real-time HFM operated over different smart manufacturing environments. Experimentally, the TCB has been evaluated and reached tradeoff results of throughput and latency better than the comparative consortium blockchain frameworks.

Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning

Nowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the volume of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is no longer possible effectively. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of product type changes on the Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree technology, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice.

Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning

Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario.

A review of the applications of multi-agent reinforcement learning in smart factories.

The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories.

Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques

The German manufacturing industry has been carrying out new developments towards the next industrial revolution, focusing on smart manufacturing environments. Our work emphasizes human-centered control rooms in the context of production plants. Increased automation does not have to come with less human control. Therefore, we report on multimodal collaborative interaction techniques to augment industrial control rooms. In particular, we include mobile workers who use the control room while being in the production hall using tablets or specifically mixed reality glasses. Collaborative annotation dashboards support discussions and a shared understanding among analysts. Manufacturing-related data can be integrated into business analytics environments so that holistic analyses can be performed. Multimodal interaction techniques can support effective interaction with the control room based on the users’ preferences. Immersive experience through mixed reality-based three-dimensional visualizations and interaction possibilities support users in obtaining a clear understanding of the underlying data.

Long-term trials for improvement of autonomous area coverage with a Tetris inspired tiling self-reconfigurable system

In the past two decades, robotics have invested tremendous research effort to deploy long-term autonomous robots to ensure a smart hygienic manufacturing environment in the food and drink industry and translated such attempts into successful robotic products. However, most such systems were developed with fixed morphological design, which highly constrains their navigation and access in constraint spaces. To this end, we have developed hTetro, Tetris-inspired self-reconfigurable floor-sweeping system capable of performing autonomously in a constrained environment for an extended period to maximize the cleaning area. This paper presents the hTetro robot’s improved system design, control system, and autonomous coverage strategy that supports achieving long-term deployment. We validated the performance of the proposed system by conducting trials in a complex food court environment as our case study. Our trials at SUTD Lab, SUTD Singapore canteen, and Tampines Singapore food center are divided into three phases from Jan 2020 until the end of Sep 2020. We tracked the robot’s movement every day during each trial while cleaning to generate the coverage map, which helps compute the total area covered. Also, we systematically benchmarked the robot’s coverage performance with a commercially available fixed morphology robot cleaning robot in the same environment. To our best knowledge, this is the first time the reconfigurable robot has been deployed to validate the feasibility in terms of mechanical designs, electronics, and software for long-term autonomy in the real environment. The result exhibits that after fixing the issues during the platform development state, our hTetro robot could achieve maximum area coverage performance every day during the long-term trial in a real environment than the commercial robot due to its shape-shifting ability. With the successful deployment of the reconfigurable cleaning robot, a further important step towards service robotics for daily use has been taken.

Modelling of thermocouple geometry variations for improved heat transfer monitoring in smart electronic manufacturing environment

In this paper, the effect of different thermocouple constructions on the hot spot temperature are investigated with a modelling approach to achieve more precise soldering profile acquisition and failure analysis at different reflow soldering heat transfer methods. The paper focuses on convection-based reflow and condensation based vapour phase soldering processes and related heat transfer coefficients. Different thermocouple constructions were investigated. The parameters include the ASTM-type (K, T, J), the length of uninsulated wire length, the insulation material (PFA, PVC, PTFE, woven glass-fibre), the insulation thickness and the diameter of the hot-spot. The physical dimensions were obtained with optical microscopy for validation. Finite element analysis was performed. After validation, the results show that most parameter and geometry changes – most significantly, the uninsulated wire end – can result in non-negligible timely differences in 100 ms range between different sensors, pointing to the requirement of sorting and pairing in a symmetric measurement environment. The results can be applied in a modern manufacturing environment and in future process modelling and evaluation tasks.

An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments.

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition.

The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

Developing Taiichi Ohno’s Mental Model for Waste Identification in Nontraditional Applications

AbstractThe growth of technology in the manufacturing domain is compelling industry to digitally transform with little to no guidance on what constitutes value-added and nonvalue-added data and information. However, the Toyota production system (TPS) approach, which has proven successful for decades in identifying wastes in physical manufacturing processes, can provide some insights. Extensive research has been conducted on the history of Toyota and the concepts and tools of the TPS, but there is no documentation of how Taiichi Ohno approached problems and developed the classification of wastes (the 7 Wastes) which led to the concepts and tools for continuous improvement that are collectively called the TPS. This article deconstructs literature on Ohno and the Toyota story to reconstruct the mental model that Ohno used to identify and categorize physical production waste in Toyota’s manufacturing operations. The mental model attributed to Ohno proposed in this work is then generalized into a framework for identifying and eliminating both physical and nonphysical wastes in systems. Manufacturing companies and researchers can utilize the framework to foster the same thinking that Ohno used to identify nonvalue-added activities in production processes. Applying the described framework to data and information flows will allow for the discovery of wastes that were once hidden and will lead to the development of tools for improving the data and information needed to support manufacturing in a Smart Manufacturing environment.

A Criticality-Aware Dynamic Task Scheduling Mechanism for Efficient Resource Load Balancing in Constrained Smart Manufacturing Environment

A Criticality-Aware Dynamic Task Scheduling Mechanism for Efficient Resource Load Balancing in Constrained Smart Manufacturing Environment

Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

Smart manufacturing in the “Industry 4.0” strategy promotes the deep integration of manufacturing and information technologies, which makes the manufacturing system a ubiquitous environment. However, the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers. To deal with this challenge, this study focuses on the real-time hybrid flow shop scheduling problem (HFSP). First, the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed, and its scheduling problem is described. Second, a real-time scheduling approach for the HFSP is proposed. The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the realtime status in the hybrid flow shop. With the scheduling rule, the priorities of the waiting job are calculated, and the job with the highest priority will be scheduled at this decision time point. A group of experiments are performed to prove the performance of the proposed approach. The numerical experiments show that the realtime scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances. Therefore, the contribution of this study is the proposal of a real-time scheduling approach, which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.