IoT-enabled Manufacturing Research Articles

Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach.

Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization. Within the network-based manufacturing system (NBMS), mobile industrial robots (MiRs) are vital in increasing operational efficiency, adaptability, and productivity. However, with the advent of IoT-enabled manufacturing systems, security has become a serious challenge because of the communication of various devices acting as mobile nodes. This paper proposes the framework for a newly personalized customization factory, considering all the advanced technologies and tools used throughout the production process. To encounter the security concern, an IoT-enabled NBMS is selected as the system model to tackle a black hole attack (BHA) using the NTRUEncrypt cryptography and the ad hoc on-demand distance-vector (AODV) routing protocol. NTRUEncrypt performs encryption and decryption while sending and receiving messages. The proposed technique is simulated by network simulator NS-2.35, and its performance is evaluated for different network environments, such as a healthy network, a malicious network, and an NTRUEncrypt-secured network based on different evaluation metrics, including throughput, goodput, end-to-end delay, and packet delivery ratio. The results show that the proposed scheme performs safely in the presence of a malicious node. The implications of this study are beneficial for manufacturing industries looking to embrace IoT-enabled subtractive and additive manufacturing facilitated by mobile industrial robots. Implementation of the proposed scheme ensures operational efficiency, enables personalized customization, and protects confidential data and communication in the manufacturing ecosystem.

An MBD-driven order remaining completion time prediction method based on SSA-BiLSTM in the IoT-enabled manufacturing workshop

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency.

Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing

• We solved the fault prediction problem with the combination of CNN and LSTM . • We solved the maintenance decision problem with the deep reinforcement learning. • The enhanced visual guidance of the maintenance process was realized by AR , which could integrate the invisible maintenance information into the machine tools. • The proposed predictive maintenance approach outperforms others in terms of accuracy and effectiveness. In the Industry 4.0 era, the number and complexity of machine tools are both increased, which is prone to cause malfunctions and downtime in the manufacturing process. Predictive Maintenance (PdM), as a pivotal part of Prognostics and Health Management (PHM), plays a vital role in enhancing the reliability of machine tools in the Internet of Things (IoT)-enabled manufacturing. In order to realize a highly reliable maintenance plan integrated with the fault prediction, the maintenance decision-making, and the Augmented Reality (AR)-enabled auxiliary maintenance, an intelligent predictive maintenance approach for machine tools is proposed in this paper via multiple services cooperating within a single framework. The fault prediction service is supported by the combination of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). Specified features from massive production data acquired by IoT can be comprehensively extracted using CNN, and their nonlinear relationship can be fitted by LSTM. Based on the fault prediction result, deep reinforcement learning is adopted to achieve the production control and schedule maintenance personnel if a fault code appears. On top of this, the guidance information from the maintenance experience database can be integrated into the faulty machine tools in the form of visibility through AR, which can guide the maintenance personnel to complete maintenance tasks more efficiently. Moreover, the remote expert service is also integrated in the AR-supported auxiliary maintenance, which is activated to solve unexpected faults that are not stored in the maintenance experience database. Comparative experiments are conducted in the IoT-enabled manufacturing workshop with real-world case studies, and the results demonstrate that the proposed predictive maintenance approach is both effective and practical.

An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing.

With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.

Process modeling with multi-level categorical inputs via variable selection and level aggregation

An Industrial IoT-enabled manufacturing system often involves multiple categorical variables, denoting the process configurations and product customizations. These categorical variables lead to a flexible relationship between the input process variables and output quality measurements, as there are many potential configurations of the manufacturing process. This causes significant challenges for data-driven process modeling and root cause diagnosis. This article proposes a data-driven additive model to address the effects of different categorical variables on the relationship between process variables and quality measurements. The estimation algorithm automatically identifies the variables that have significant effects on the product quality, aggregates the levels of each categorical variable based on a priori knowledge of level similarity, and provides an accurate model that describes the relationship between the process variables and quality measurements. The simulation study validates the accuracy and effectiveness of the proposed method, and a case study on a hot rolling process shows that the method provides useful guidance on the understanding of the production system.

A Dynamic Context-Aware Workflow Management Scheme for Cyber-Physical Systems Based on Multi-Agent System Architecture

Although Cyber-Physical Systems (CPS) provides a paradigm to accommodate frequent changes in manufacturing sector, modeling and managing operations of CPS are challenging issues due to the complex interactions between entities in the system. Development of an effective context-aware workflow management system to guide the entities in the system is a critical factor to attain the potential benefits of CPS. In this paper, we will address the issue on the design of context-aware workflow management systems for CPS in IoT-enabled manufacturing environment. A CPS consists two parts, the Physical World and the Cyber World. To achieve the goal to design a context-aware information system for CPS, the Cyber World models of the entities in the system are constructed based on discrete timed Petri nets (DTPN) and a multi-agent system architecture in which each entity in the system is modeled as an agent to capture the interactions of entities in CPS. To develop context-aware workflow management systems for CPS, a Configuration/Scheduling Feasibility Problem and a Context Generation Problem in CPS are formulated. A condition for configuration/scheduling feasibility based on transformation of the Cyber World Models is established to develop an algorithm to generate contextual information to guide the operation of CPS. The proposed method is illustrated by examples. A series of experiments have been conducted to demonstrate the practicality of the proposed method in terms of computation time and response time. The results indicate that the computation time and total response time increase polynomially with respect to problem size parameters and show that the proposed method is effective in solving real problems.

A big data approach for worker’s performance evaluation in IoT-enabled manufacturing shopfloors

Internet of things (IoT) and Radio Frequency Identification (RFID) technologies are gradually adopted in manufacturing recently. With the aid of them, numerous data is generated from daily manufacturing operations. Big data analytics is used in locating deficiencies and thus improving the productivity of a manufacturing shopfloor. Many studies have also examined the effect of “Blue Monday” and “post-lunch slump” on worker’s performance. This paper provides a big data approach on analyzing worker’s performance with the data collected from a manufacturing shopfloor. By evaluating the worker’s performance at different time periods, a better decision can be arranged for improving overall productivity.

A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process

Accurate anomaly detection is the premise of production process control and normal execution of production plan. The implementation of Internet of Things (IoT) provides data foundation and guarantee for real-time perception and detection of production state. Taking abundant IoT data as support, a density peak (DP)-weighted fuzzy C-means (WFCM) based clustering method is proposed to detect abnormal situations in production process. Firstly, a features correlation and redundancy measure method based on mutual information (MI) and conditional MI is proposed, unsupervised feature reduction is completed based on the principle of maximum correlation-minimum redundancy. Secondly, a DP-WFCM based clustering model is established to identify clusters with fewer samples to detect production anomalies. DP is used to obtain the initial clustering centers to solve the problem that FCM is sensitive to the initial centers and the clusters number needs to be determined manually in advance. MI-based similarities are introduced as weight coefficients to guide the clustering process, which improves convergence speed and clustering quality. Finally, a real case from an IoT enabled machining workshop is carried out to verify the accuracy and effectiveness of the proposed method in anomaly detection of manufacturing process.

Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning

As IoT-enabled manufacturing is still in its infancy, there are several key research gaps that need to be addressed. These gaps include the understanding of the characteristics of the big data generated from industrial IoT sensors, the challenges they present to process data analytics, as well as the specific opportunities that the IoT big data could bring to advance manufacturing. In this paper, we use an inhouse-developed IoT-enabled manufacturing testbed to study the characteristics of the big data generated from the testbed. Since the quality of the data usually has the most impact on process modeling, data veracity is often the most challenging characteristic of big data. To address that, we explore the role of feature engineering in developing effective machine learning models for predicting key process variables. We compare complex deep learning approaches to a simple statistical learning approach, with different level or extent of feature engineering, to explore their pros and cons for potential industrial IoT-enabled manufacturing applications.

A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing

In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. The implementation of Internet of Things (IoT) makes it possible to take real-time production state as an important factor affecting PP. In the IoT-enabled workshop, a two-stage transfer learning-based prediction method using both historical production data and real-time state data is proposed to solve the problem of low-prediction accuracy and poor generalization performance caused by insufficient data of target order. The deep autoencoder (DAE) model with transfer learning is designed to extract the generalized features of target order in the first stage, which uses bootstrap sampling to avoid over fitting. The deep belief network (DBN) model with transfer learning is constructed to fit the nonlinear relation for PP prediction in the second stage. A real case from an IoT enabled machining workshop is taken to validate the performance of the proposed method over the other methods such as DBN, deep neural network.

Internet of things enabled manufacturing: a review

Internet of things (IoT) plays an important role in the manufacturing sector, allowing objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and therefore resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. With the world-wide spread of Industry 4.0, IoT-enabled manufacturing is now one of the key supports to smart factory, intelligent automation, and real-time adaptive decision-makings. This paper comprehensively reviews related technologies and world-wide movements so that insights and lessons could be useful for academia and practitioners when contemplating IoT technologies for upgrading and transforming traditional manufacturing into an new future in the context of Industry 4.0.

Big Data Analytics for Processing Time Analysis in an IoT-enabled manufacturing Shop Floor

Internet of things (IoT) technology has been widely used in manufacturing where great myriad of data have been generated. This paper introduces a big data analytics for Internet of Things (IoT)-enabled manufacturing shop floor which uses radio frequency identification (RFID) technology for capturing the real-time production data. This involves two machine learning techniques: k-means clustering and gradient descent optimization for the data from the manufacturing sites. In particular, this research deals with the real RFID data related to various processes for individual batches and processing time. This could produce valid predictions about expected overall manufacturing time for a given number of manufacturing batch inputs.

Production events graphical deduction model enabled real-time production control system for smart job shop

Under industry 4.0, the production control system of smart job shop should be able to real-time respond to various production events and effectively coordinate different kinds of manufacturing resources in good order according to their material flows. Production events enabled real-time production control system is good at responsibility and flexibility. Internet of things (IoT) can provide enormous real-time production events, which represent the change of material flows. However, some of production events seriously interfere with production control procedures. They sharply restrict the real-time capability of production control system. Thus, it is imperative that an efficient realization method of production control system, which is enabled by useful production events. Additionally, the control system should satisfy production control procedures visibility. For solving the problems, a production events graphical deduction model enabled real-time production control system for smart job shop is proposed in this article. Firstly, the manufacturing resources are divided into work in process related, operator-related, cutting tool-related, fixture tool-related, and measuring tool-related. And the material flows of different manufacturing resources in IoT-enabled smart job shop are described in detail. Secondly, the graphical deduction model of production event is put forward. Based on the model, the material flows of manufacturing resources in a process are segmented into several stages according to different production events. And then, the cooperation model of manufacturing resources is established by using the time and logical relationships between production events. Thirdly, the control model in a process is drawn from the cooperation model. Next, the entire control procedure of work in process production in IoT-enabled smart job shop is proposed. Finally, a small-scale IoT-enabled manufacturing system is used to verify the feasibility of the proposed model and methods.

Statistical Process Monitoring for IoT-Enabled Cybermanufacturing: Opportunities and Challenges

Initiated from services and consumer products industries, there is a growing interest in using Internet of Things (IoT) technologies in various industries. In particular, IoT-enabled cybermanufacturing starts to draw increasing attention. Because IoT devices such as IoT sensors are usually much cheaper and smaller than the traditional sensors, there is a potential for instrumenting manufacturing systems with massive number of sensors. The premise is that the big data subsequently collected from IoT sensors can be utilized to advance manufacturing. Therefore, data-driven statistical process monitoring (SPM) is expected to contribute significantly to the advancement of cybermanufacturing. In this work, the state-of-the-art in cybermanufacturing is reviewed; an IoT-enabled manufacturing technology testbed (MTT) was built to explore the potential of IoT sensors for manufacturing, as well as to understand the characteristics of data produced by the IoT sensors; finally, the potentials and challenges associated with big data analytics presented by cybermanufacturing systems is discussed; and we propose statistics pattern analysis (SPA) as a promising SPM tool for cybermanufacturing.

A MPN-based scheduling model for IoT-enabled hybrid flow shop manufacturing

As the wide use of Internet of Things (IoT) technology in manufacturing, production decision-makings are revitalized due to the real-time activities and behaviors happened in flow shops. This paper introduces a manufacturing Petri Net-based scheduling model for IoT-enabled hybrid flow shop manufacturing. The proposed model switches the schedule of Token sequences in MPN to the trigger sequence of transition. Thus the real-time production behaviors in IoT-enabled manufacturing flow shops could be modelled. Several contributions are significant. Firstly, the proposed model is able to reflect the real-time behaviors of various smart manufacturing objects enabled by IoT devices so that optimal schedules could be achieved. Secondly, an ant colony optimization (ACO)-based solution is proposed with the confinement of the minimum and maximum pheromone value to avoid the stagnation of the search caused by the pheromone concentration. Thirdly, the comparison of the proposed model and classic ant system presents the outperformance of average computational cost. The outperformance attributes to the limitation of the maximum and minimum of pheromone as well as their ratio so that it has better efficiency. Key findings and observations are summarized as managerial implications which could be used for guiding practitioners to implement the decision-making in IoT-enabled manufacturing flow shops.