Enable Smart Manufacturing Research Articles

Industry 4.0 and Beyond: The Role of 5G, WiFi 7, and Time-Sensitive Networking (TSN) in Enabling Smart Manufacturing

This paper explores the role that 5G, WiFi 7, and Time-Sensitive Networking (TSN) play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. It provides an in-depth analysis of each technology’s application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, this paper includes a comparative study of these technologies, analyzing them based on several industrial use cases, supported secondary applications, industry adoption, and current market trends. This paper concludes by highlighting the challenges and future directions for adopting these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing.

Holistic process control framework for smart biomanufacturing: A deep learning driven approach

This article introduces a cyber-physical system comprising four interactive modules for enabling smart manufacturing in biotherapeutic production. These modules deal with process control, process prognosis, process diagnosis, and act as an external communication platform. An acoustic wave separator, a novel CHO cell clarification device, has been used as a case study with the objective of optimizing and regulating cell clarification process. A deep neural network (DNN)-based model has been proposed for implementing rapid control of bioprocess operations in case of detected anomalies or deviations in real-time. The use of the proposed holistic controller resulted in a 5% mean deviation from the setpoint in the first chamber, with more than 30% mean deviation resulting in the absence of the controller. The intricate layers of the automation framework facilitate real-time monitoring of the critical process parameters at 10-second intervals and ensure robust control action for deviations/anomalies, thus enabling smart manufacturing for biopharmaceutical production.

Direct Digital Manufacturing (DDM) Workflow for Printed Circuit Structures (PCS)

Digitally based manufacturing has been around for years and has been the basis of turnkey lights out manufacturing processes and systems. Moving forward is the concept of Direct Digital Manufacturing (DDM) which brings together conceptualizing the electronic device, developing a digital twin of the product, and digitally merging workflow with the manufacturing process. This would further extend through to physical product. This paper will illustrate DDM is the basis of developing the capability of conceptualizing, designing, and digitally manufacturing electronic devices and more specifically printed circuit structures or PCS and printed circuit cylinders, PCC. Unlike tradition PCB’s or multichip module technology, the goal is fully printed electronic devices that can merge structure with electronic functionality. This paper will explore the current state of practice in manufacturing workflow and extend to the emerging factory in a Box concept of multi-material printing of next generation electronic controller and packaging technology. This will also include exploring design tools that provide the capability of co-design mechanical function with electronic function and modeling the manufacturing process and electronic device using digital twin concepts and workflow enabling smart manufacturing of printed electronic devices and precision of DDM technology.

Cross-Domain Access Control Model in Industrial IoT Environment

The Industrial Internet of Things (IIoT) accelerates smart manufacturing and boosts production efficiency through heterogeneous industrial equipment, intelligent sensors, and actuators. The Industrial Internet of Things is transforming from a traditional factory model to a new manufacturing mode, which allows cross-domain data-sharing among multiple system departments to enable smart manufacturing. A complete industrial product comes from the combined efforts of many different departments. Therefore, secure and reliable cross-domain access control has become the key to ensuring the security of cross-domain communication and resource-sharing. Traditional centralized access control schemes are prone to single-point failure problems. Recently, many researchers have integrated blockchain technology into access control models. However, most blockchain-based approaches use a single-chain structure, which has weak data management capability and scalability, while ensuring system security, and low access control efficiency, making it difficult to meet the needs of multi-domain cooperation in IIoT scenarios. Therefore, this paper proposes a decentralized cross-domain access model based on a master–slave chain with high scalability. Moreover, the model ensures the security and reliability of the master chain through a reputation-based node selection mechanism. Access control efficiency is improved by a grouping strategy retrieval method in the access control process. The experimental benchmarks of the proposed scheme use various performance metrics to highlight its applicability in the IIoT environment. The results show an 82% improvement in the throughput for the master–slave chain structure over the single-chain structure. There is also an improvement in the throughput and latency compared to the results of other studies.

Automation technology as a key component of the Industry 4.0 production development path

Since 2011, the Industry 4.0 initiative is a key research and development direction towards flexible production systems in Germany. The objective of the initiative is to deal with the challenge of an increased production complexity caused by various factors such as increasing global competition between companies, product variety, and individualization to meet customer needs. For this, Industry 4.0 envisions an overarching connection of information technologies with the production process, enabling smart manufacturing. Bringing current production systems to this objective will be a long transformation process, which requires a coherent migration path. The aim of this paper is to represent an exemplary production development way towards Industry 4.0 using eminent formalization approaches and standardized automation technologies.

A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems

The potential benefits of the adoption of cyber-physical production systems (CPPSs) and their significant role in enabling smart manufacturing is well recognized today. However, it is less clear how such CPPS can be most effectively and consistently engineered and maintained throughout their lifecycle due to the existing divide in the information technology (IT) and operational technology (OT) landscape and ad hoc integration practices that result in inconsistent data and data models at various levels of manufacturing processes. The work presented in this article addresses this problem by envisioning a connective framework to support the engineering of CPPS through the use of a set of digital twins consistent with the real system throughout its lifecycle, not just used in the design and deployment phases. A review of the latest perspectives on using digital integration frameworks, methods, and solutions for lifecycle engineering of CPPS is provided in this article. This article demonstrates how a suitable framework, named SIMPLE, can be realized to effectively address the lack of consistent data models throughout the engineering lifecycle, including implementation details and example cases developed by the authors at the Warwick Manufacturing Group (WMG) in selected industrial sectors. Consideration is given to supporting cyber-to-physical systems' connectivity and extendable engineering toolsets, forming the basis for multidisciplinary digital engineering environments. Key discussion points include the role and importance of effective integration of IT and OT, suitable frameworks for integration and collaboration.

A Gap Study between Employers’ Expectations in Thailand and Current Competence of Master’s Degree Students in Industrial Engineering under Industry 4.0

Abstract Industry 4.0 is an era in which the manufacturing industry has adopted digital technologies and the Internet to enable smart manufacturing system, machines used in the production now can communicate with each other and exchange information between each other, and the machinery used in the manufacturing process is more modern and precise. Therefore, educational institutions should develop the curriculum to produce qualified graduates with the knowledge required for the Industry 4.0 era, especially Industrial Engineering graduates who are directly related to the industry sector. The purpose of this research is to collect the data for the Master of Industrial Engineering (MSIE) curriculum development. The Analytic Hierarchy Process (AHP) technique is used to rank the indicators of knowledge that is important to the employment of graduates with a master’s degree in Industrial Engineering, and study the gap between the expectations of employers and the ability of the current MSIE students of Khon Kaen University. The results of the study reveal that the first indicators that are most important to the employment of MSIE graduates is the knowledge of Industry 4.0 strategy and the knowledge that the students should have developed are the collaboration of humans and robots, big data analytics, real time data usage and databased decision making.

Recent Industrial Roadmaps to Enable Smart Manufacturing of Biopharmaceuticals

Biopharmaceutical manufacturing is an evolving industry with great potential to improve public health, but laden with technical and operational challenges. It has many features that are similar to other smart manufacturing industries, but it also has some unique features that are specific to the biological domain in which it operates. Both types of features have been carefully explored in recent industrial roadmaps to guide the research, development, and manufacturing of biopharmaceuticals. These roadmaps explicitly identify “fully automated facilities” and “knowledge management” as two of the critical enabling capabilities that will drive the biopharmaceutical industry in the next decade. This article describes these roadmaps to identify the emerging opportunities and future directions in this knowledge-intensive manufacturing industry. It also serves as a “call for action” for collaboration among key stakeholders, including the robotics and automation community. Note to Practitioners —The market for biopharmaceuticals has been rising rapidly in recent years. It is now a multibillion dollar industry with its own ecosystem of suppliers, medical professionals, drug delivery systems, and regulators. The healthcare benefits promised by biopharmaceuticals are impressive, but there are several research, development, and manufacturing challenges that must be overcome to deliver on these promises. The biopharmaceutical industry, in collaboration with academics and government laboratories, has published a series of roadmaps recently to address these challenges. “Fully automated facilities” and “knowledge management” have been explicitly identified in the roadmaps as two of the critical enablers to meet these challenges. This article summarizes the findings of these industrial roadmaps and provides a clear picture of the collaborative efforts needed to succeed in, and grow, this promising industry.

An intelligent patent recommender adopting machine learning approach for natural language processing: A case study for smart machinery technology mining

Recommendation systems are widely applied in many fields, such as online customized product searches and customer-centric advertisements. This research develops the methodology for a patent recommender to discover semantically relevant patents for further technology mining and trend analysis. The proposed recommender adopts machine learning (ML) algorithms for natural language processing (NLP) to represent patent documents in vector space and to enable semantic analyses of the patent documents. The ML approach of neural network (NN) language models, trained by domain patent documents (text) as a training set, convert patent documents into vectors and, thus, can identify semantically similar patents using document similarity measures. In particular, the proposed recommender is deployed to in-depth case studies for advanced patent recommendations. The case domain of smart machinery is used to better enable smart manufacturing by incorporating innovative technologies, such as intelligent sensors, intelligent controllers, and intelligent decision making. The research uses six sub-domains in smart machinery technologies as the case studies to verify the superior accuracy and efficacy of the recommender system and methodologies.

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

Shop floor to cloud connect for live monitoring the production data of CNC machines

ABSTRACT Manufacturing industry is at its most exciting times with digital revolution. Man, machine, material and tools have to co-ordinate with each other seamlessly to maximize efficiency in a mass production environment. Various theoretical models are in practice to measure efficiency of equipment, gauging, services, maintenance, etc. While all the theoretical models are quite accurate in their approach to measuring the activities, they lagging behind in the process of data collection. The data collection methods are manual and not accurate. In this paper, a model is arrived at to collect live data of production, rejection and idle time in a machine tool with the help of electronic sensors. Effort is made to enhance operator engagement by compelling the operator to feed data so that the data collection becomes closed loop. As all data are digital in nature, meaningful information is sent to the management through an Internet of Things platform. This paper focuses on the digital data flow from the shop floor to management through the Cyber Physical System, enabling smart manufacturing in a mass production environment. The proposed model has been implemented and validated in a mass production set-up, engaged in manufacturing plug shell components.

IoT based Predictive Maintenance in Manufacturing Sector

Industry 4.0 enables technological trends like Big Data Analytics and Machine Learning techniques to converge into and merge with traditional manufacturing processes, resulting in smart manufacturing. Smart manufacturing techniques leverage the use of Industrial Internet of things (IIoT) technology using IoT sensors that are fitted on physical assets to enhance manufacturing processes. IoT Sensors enable smart manufacturing facilities capable of autonomously exchanging information, which can be used to drive business decisions more accurately. Businesses that adopt Smart manufacturing techniques lead to a competitive advantage for these firms as they can bring in higher profit margins, reduced maintenance costs, energy savings, and better-quality products. This study proposes an architecture for IIoT based predictive maintenance. A case study from ancillary automobile industry is presented to demonstrate a predictive model for predicting sudden breakdown in industrial machines, thereby enabling the production and maintenance cycle to be ‘smart.’

A collaborative IoT-gateway architecture for reliable and cost effective measurements

Root-cause analysis and gaining insight of the operations of manufacturing machines requires periodic measurements. Today, most such measurements are taken manually and are therefore not fine-grained, accurate, or fast enough for the smart solutions required by Industry 4.0. Such smart solutions are expected to use measurements from IoT sensors to make optimal decisions via data analytics, for instance, to perform continuous structural health monitoring [1], to enable smart manufacturing systems [2], to facilitate smart transportation [3], and many more. All of these systems need continuous feed of measurements from sensors, and very often the collected measurements are transmitted and stored in the cloud for data analysis [4]. Thus, not only the communication between the devices must be reliable, but also connection reliability to the cloud is equally important. In addition to that, the trend of data collection has become such that today, data is being collected before the precise needs of the data are known, so naturally, the best strategy is to store all of the data for possible future use [5]. This leads to a large amount of data, and considering that over the course of time more and more sensors will be deployed in an IoT environment, and more measurements will be generated, we can see that conventional networking approaches will not be able to cope with this huge demand. Smart systems, which need to be scalable, agile and fluid, are already forcing changes to conventional web technology to accommodate the volume, velocity and variety of measurements data generated from IoT [6].

Using Graphs to Link Data Across the Product Lifecycle for Enabling Smart Manufacturing Digital Threads

Smart manufacturing promises to provide significant increases in productivity and effectiveness of manufacturing systems by better connecting the data from people, processes, and things. However, there is no uniform, generalized method for deploying linked-data concepts to the manufacturing domain. The literature describes and commercial vendors offer centralized data repository solutions, but these types of approaches quickly breakdown under the intense burden of managing and reconciling all the data flowing in and out of the various repositories across the product lifecycle. In this paper, we introduce a method for linking and tracing data throughout the product lifecycle using graphs to form digital threads. We describe a prototype implementation of the method and a case study to demonstrate an information round-trip for a product assembly between the design, manufacturing, and quality domains of the product lifecycle. The expected impact from this novel, standards-based, linked-data method is the ability to use digital threads to provide data, system, and viewpoint interoperability in the deployment of smart manufacturing to realize industry's $30 Billion annual opportunity.

Adoption of digital technologies of smart manufacturing in SMEs

Abstract The Fourth Industrial Revolution-commonly referred to as Industry 4.0-with smart manufacturing currently on its forefront-has arrived. The manufacturing industry is evolving and manufacturers of all sizes, worldwide, need to evolve too. In order not to be left behind from early adopters, Small and Medium-sized Enterprises (SMEs) integrate modern Smart Manufacturing-related Information and Digital Technologies (SMIDT) such as artificial intelligence with their business operations to enable smart manufacturing. The present study is concerned with identifying the determinants of SMIDT adoption within manufacturing SMEs. The study benefits from a cross-sectional survey to capture the opinions of Malaysian and Iranian participating SMEs. Results indicate that a collection of technological, organizational, and environmental factors determine SMEs decision for SMIDT adoption. The study further explores how various combinations of identified determinants have influenced the implementation of 13 individual SMIDT among SMEs. Theoretical contribution and managerial implications of this research are discussed which are believed to offer valuable insights to academicians, executives, and policymakers.

Analysis of Smart Manufacturing Technologies for Industry Using AI Methods

Smart manufacturing technologies have gained significant attention in the industrial sector due to their potential to revolutionize traditional manufacturing processes. Among these technologies, artificial intelligence (AI) methods have emerged as powerful tools for enhancing efficiency, productivity, and decision-making in manufacturing operations. This paper presents an analysis of smart manufacturing technologies for industry using AI methods. The analysis focuses on the application of AI techniques such as machine learning, deep learning, and data analytics in various aspects of smart manufacturing, including predictive maintenance, process optimization, quality control, and supply chain management. The paper provides an overview of the key AI methods employed in smart manufacturing and discusses their benefits and challenges. It also highlights case studies and real-world implementations of AI-based smart manufacturing systems. The findings of this analysis demonstrate the significant contributions of AI methods in enabling intelligent and autonomous manufacturing systems. The paper concludes with insights into the future directions and potential impact of AI-driven smart manufacturing technologies in industry, emphasizing the importance of continued research and development in this field to unlock the full potential of smart manufacturing in the industry. Smart manufacturing technologies have revolutionized the industrial sector by enhancing productivity, efficiency, and flexibility. Artificial intelligence (AI) methods, such as machine learning and data analytics, play a crucial role in enabling smart manufacturing systems to optimize processes and make informed decisions. This research paper aims to analyse the application of AI methods in smart manufacturing technologies. The study explores various AI-based approaches used in different stages of smart manufacturing, including data acquisition, data analysis, process optimization, and predictive maintenance. The research provides insights into the benefits, challenges, and potential future developments of AI in smart manufacturing, offering valuable guidance for industries aiming to implement these technologies.

Methods and Materials for Smart Manufacturing: Additive Manufacturing, Internet of Things, Flexible Sensors and Soft Robotics

With the advent of the Fourth Industrial Revolution, the realization of smart manufacturing and Industry 4.0, rapid advancements in technologies will change manufacturing of goods and services, apart from how we live. Innovation in digital technologies will have to be accompanied by advances in manufacturing processes and materials, to keep up with the transition. This paper will review and discuss the methods and material technologies present now, along with challenges to overcome, which will be critical for enabling smart manufacturing in the form of the Internet of Things (IoT), cyber-physical systems (CPS), human-robot interaction, augmented and virtual realities.

Editorial to the Special Issue Information Integration for Innovative Design and Smart Manufacturing

Information integration is one of the key features to foster innovative design approaches and to enable smart manufacturing in modern companies. Future manufacturing will be more automated, more digitalized, but also more flexible, distributed, networked and collaborative. As a consequence, information integration approaches are necessary to support companies to be innovative and profitable. The issue collects valuable examples of how collaborative approaches and interdisciplinary actions can benefit companies, from new idea co-creation to smart enterprise management, from intelligent data analysis to flexible decision-making, from smart management of business relationships to reconfigurable manufacturing automation.

ENABLING SMART MANUFACTURING TECHNOLOGIES FOR DECISION-MAKING SUPPORT.

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

On architecting and composing engineering information services to enable smart manufacturing.

Engineering information systems play an important role in the current era of digitization of manufacturing, which is a key component to enable smart manufacturing. Traditionally, these engineering information systems spanned the lifecycle of a product by providing interoperability of software subsystems through a combination of open and proprietary exchange of data. But research and development efforts are underway to replace this paradigm with engineering information services that can be composed dynamically to meet changing needs in the operation of smart manufacturing systems. This paper describes the opportunities and challenges in architecting such engineering information services and composing them to enable smarter manufacturing.