Traditional Manufacturing Systems Research Articles

Changing Welding to Digital and Green - Actions and Communication

The implementation of Industry 4.0 is based, among other things, on the digitization and greening of manufacturing systems. Most traditional manufacturing systems are known for low productivity and efficiency. They are also known for the impact they have on the environment, with relatively high carbon footprints. The paper transfers a series of knowledge on the steps to be taken to transition a traditional manufacturing system to a digitized and green one. Customization of this discussion is done for manufacturing systems that have joining processes (welding and soldering). Each set of measures, whether aimed at the transition to digital or aimed at the transition to green, has two components: a technical component, directly related to the transformation of the hardware system (equipment, devices, measuring instruments) and a component auxiliary which is related to the transformation of the human resource, the modification of the working conditions and the continuous optimization of the newly created system. As no change is possible without removing employee misunderstandings and suspicions, the communication to be implemented must be clear, prompt, cover all levels of staff and continuously provide confidence by presenting successful cases and related beneficial figures.

Integrated optimization of production planning and electric trucks charging and discharging scheduling

The majority of supply chains in any manufacturing industry rely on heavy trucks for their flow of material from suppliers to end-users. Transportation by these heavy trucks causes greenhouse gas emissions detrimental to the environment. To cope with challenges caused by environmental issues and energy crises, replacing heavy trucks with electric trucks seems to be a viable solution. They can be considered as an energy resource that supports grid-to-vehicle and vehicle-to-grid, enabling efficient energy management for smart manufacturing systems and supply chains. Proper scheduling of charging and discharging activities of electric trucks also supports the penetration of renewable energy sources with volatile energy generation. This paper encourages sustainable practices and proposes a mixed integer programming model for the simultaneous optimization of production planning and transportation scheduling of electric trucks in a smart manufacturing system. The uncertainty sources considered in this system include power generation of renewable energy, transportation patterns, and customer demand for products. The results show that utilizing electric trucks in smart manufacturing with real-time pricing, save up to 11% of the total cost compared to traditional manufacturing systems without electric trucks and renewable energies. This integration also increases the efficient utilization of renewable energies such as wind and solar powers. The savings decrease to 4% and 8% under flat and time-of-use electricity pricing strategies. The results also highlight the complexity of setting utilization level of electric trucks and renewable energy sources into well-implemented sustainability standards.

From Nash Q-learning to nash-MADDPG: Advancements in multiagent control for multiproduct flexible manufacturing systems

The emergence of flexible manufacturing systems (FMS) capable of processing multiple product types is a result of the growing demand for product customization and personalization. Such multiproduct systems are characterized by a higher level of uncertainty and variability when compared to traditional manufacturing systems. This paper proposes a Nash integrated multiagent deep deterministic policy gradient method (Nash-MADDPG) to control the mobile robots’ assignment in a multiproduct FMS to enable intelligent decision-making, interaction, and dynamic learning capabilities. A mathematical model of a multiproduct FMS from a previous study is described, and system dynamic property is characterized by permanent production loss (PPL). Then, by observing PPL of the system and market demand for each product type, the multi-agent control scheme is developed to assign mobile robots to load/unload various product types at various machines. First, a Nash game is developed among the mobile robots and to improve the cooperation, a collaboration cost is defined. This collaboration cost is then used in the reward function of the multiagent deep deterministic policy gradient (MADDPG) algorithm. Second, the actions are jointly defined based on the action values of MADDPG, and the mobile robots’ strategies in the Nash game, which update the environment to a new state. The performance of the proposed method is verified by comparing it with conventional Nash Q-learning, vanilla MADDPG, Q-learning based single agent reinforcement learning (SARL) and a first-come-first-serve (FCFS) based control. The results demonstrate that the multi-agent control scheme under the proposed Nash-MADDPG is effective in dealing with cooperative FMS environment that involves complicated dynamics and uncertainties.

Blockchain Technology in Overcoming Security Threats for Smart Manufacturing System - A Systematic Literature Review

Blockchain is a new emerging technology that is mainly used to add security layers to many applications. Studies showed that businesses and manufacturing industries suffer from cyber-attacks, especially after the emergence of Industry 4.0 and IoT devices connected to the cloud for data sharing purposes, these interactions between IoT devices and the information involved in the transactions between companies and customers produce a massive amount of data daily. This phenomenon becomes an easy target for attackers due to the security limitations, data corruption and compromised reliability of the traditional manufacturing systems during data transferring. Many have focused on the methods to address these security limitations of the manufacturing businesses. This paper shed the light on the major smart manufacturing sectors that can potentially benefit from blockchain technologies, including cloud manufacturing, collaborative manufacturing, digital twin manufacturing, robonomics based smart manufacturing and the supply chain industry. The blockchain can enhance the security by creating a decentralized environment for the data to be stored, which will make the data stored immutable for improved security. These immutable data are nearly impossible to manipulate, replace, or falsify the data stored on the blockchain secured network.

Probing an Easy-to-Deploy Multi-Agent Manufacturing System Based on Agent Computing Node: Architecture, Implementation, and Case Study

Abstract Due to the widespread adoption of personalized customization services, the application contexts within discrete manufacturing workshops have become increasingly intricate, necessitating the modern industry to evolve toward a more adaptable production trajectory. The pre-established production rules in a traditional centralized control manufacturing system present difficulties in accommodating dynamic situations. Although a multi-agent manufacturing system (MAMS) yields natural advantages in handling dynamic emergencies, the current research is limited to the computer simulation level and lacks integration with the underlying physical devices. In order to mitigate said challenges, the standardization modeling approach for the agent computing node (ACN) to facilitate the implementation of a readily deployable MAMS was proposed in the present study. Initially, adapters encompassing communication, decision, and control modules were developed within the industrial personnel computer-based computing node to accommodate the heterogeneous interface protocols of diverse machines. These adapters enable communication and interaction among machines while laying the computational foundation for the ACN. Accordingly, the models of the machine agent, the part agent, and the monitoring agent were constructed based on ACNs and could perceive the dynamic production information and support the enabling application. Subsequently, to guide ACNs in making scheduling decisions beneficial to global performance, an improved negotiation mechanism in MAMS was achieved in real-time. Finally, the proposed MAMS based on the ACN was deployed in an actual flexible machining workshop. Comparative experiments were implemented and, as exhibited from the experimental results, the proposed ACNs possessed the capabilities of achieving optimal global decision-making and facilitating straightforward deployment.

Research on real-time optimization technology of intelligent manufacturing based on learning algorithm

In the manufacturing industry, production efficiency is the core competitiveness of enterprises, but also an important goal of enterprise production planning management, and in recent years, the production efficiency of China's manufacturing industry does not meet the requirements, and there are still many problems to be solved. In the face of this situation, the relevant fields have explored intelligent manufacturing and proposed intelligent manufacturing technology, which mainly integrates information technology with traditional manufacturing system, can realize the important measures of intelligence, automation, digitalization and information in the manufacturing process, and can help solve the current key problems. Therefore, this paper takes ship manufacturing as the background. The real-time optimization technology of intelligent ship manufacturing based on learning algorithm is studied. This technology can effectively improve ship production efficiency and reduce production cost.

Трансформация оперативного управления эксплуатацией станков, станочных комплексов с помощью «Системы операционного менеджмента»

The article focuses on the potential integration of the SFM digital control system into production. To achieve a more accurate implementation of the dSFM system, the article identifies its strengths and weaknesses. It evaluates and outlines the factors contributing to the successful implementation of the dSFM system in production. The article also analyses the traditional Lean manufacturing system, the analogue SFM system and its digital version. The study scrutinised the manner in which workers interact with the «System of Operations Management» with the purpose of refining its assimilation into manufacturing processes and enhancing employee output.

Development of a Human Centric Cyber Physical Production System Framework for Enhanced Social Sustainability

Social sustainability focuses on building a sustainable workplace that prioritizes the occupational health, safety, and overall well-being of workers. It is considered one of the three fundamental pillars of sustainability. Human centric cyber physical production system (CPPS) emphasizes the central role of humans in the smart manufacturing process that has become an essential requirement to enhance social sustainability. This paper proposes a human centric CPPS framework for enhanced social sustainability. The social needs/requirements are identified and categorized into various types. Similarly, functional and design requirements are categorized into different elements and sub-elements of CPPS. The findings are used to create a QFD (Quality Function Deployment) matrix that integrates social requirements with the functional and design requirements of modular CPPS elements. The present work will be significant in enhancing the management capabilities and performance of traditional manufacturing systems, while also meeting diverse social needs and requirements.

Security of Cyber-Physical Systems of Chemical Manufacturing Industries Based on Blockchain

The traditional manufacturing systems are often enterprise-centric systems, whereas the modern chemical industry is oriented towards industrial chain integration. Enterprise entities present a loosely coupled state at the scale of the industrial chain, with decentralized characteristics. This poses greater challenges and requirements for the industrial safety system. Based on the characteristics of the chemical manufacturing industry and blockchain, the application of the information security of blockchain in the chemical manufacturing industry is studied herein and the cyber-physical systems security architecture model of dual blockchains is proposed. The first-layer blockchain is applied at the system’s core function level to solve security issues at the system level and provide security guarantees for communication, transactions, and billing between users and manufacturers. Meanwhile, the second layer involves the system resource layer, which not only solves the security problem of cross-level platform data interaction, but also enables the point-to-point security of the device-level cyber-physical system to ensure internal equipment communication information security. A domestic commercial concrete manufacturing company’s real production and operation data were used to simulate basic functions such as transaction requests, trade success, and blockchain queries. After multiple tests, results show that its basic blockchain, query response, transaction creation, and block creation functions are all finished within milliseconds, meeting the industrial requirements. Its safety verification can meet the requirements of safety, efficiency, and low latency for production control in chemical industry sites, proving the feasibility of applying the dual blockchain model in the chemical manufacturing industry. Based on data security, privacy, and integrity requirements, the blockchain technology proposed in this article provides a more efficient, transparent, and secure operation and management solution for the chemical industry.

Digital twin monitoring and simulation integrated platform for reconfigurable manufacturing systems

Recent advances in manufacturing technologies witness the digital transformation of traditional manufacturing systems. Digital Twin is one technology being studied and applied in various aspects of production. Reconfigurable Manufacturing System (RMS), as an advanced manufacturing paradigm, proposes new challenges for Digital Twin application. More flexibility is required for Digital Twin to adapt to the dynamic rearrangement of RMS, supporting the full lifecycle of the systems. But how this can be achieved is yet to be explored. Against the background, this paper aims to realise Digital Twin-driven RMS reconfiguration. By integrating and clarifying the prerequisites of Cyber–Physical System (CPS) and Manufacturing Execution System (MES), Digital Twin Monitoring and Simulation Integrated Platform (DTMSIP) is proposed for RMS. With transparent communication and decoupled structure, DTMSIP can perform real-time online supervision and high-fidelity offline simulation of the production of RMS. Accordingly, the method of implementing DTMSIP is introduced by building Digital Twin mapping based on Twinning Entity (TE). Moreover, how the CPS-MES-DTMSIP architecture serves the RMS evolution is discussed by proposing the procedure of DTMSIP-driven RMS reconfiguration. To validate the proposed procedure, DTMSIP software is realised for an existing RMS using Unreal Engine 4 (UE4). Through DTMSIP monitoring and simulation of planned RMS configurations, the performance of planned configurations is calculated, and the optimised configuration is obtained.

A Systematic Literature Review: Industry 4.0 Based Monitoring and Control Systems in Additive Manufacturing

Three-dimensional printing, also referred to as additive manufacturing, offers a wide range of product diversity, design flexibility, and competitive product costs, making it a key technology in the Industry 4.0 era. With a growing demand for customer-oriented manufacturing strategies in the industry, 3D printing holds the potential to revolutionize traditional manufacturing systems by enabling the production of high-value-added complex products at reduced costs. This systematic literature review paper aims to analyze the ongoing research on Industry 4.0-based digital solutions in the field of monitoring and control to facilitate the adoption of 3D technologies. The study utilizes a systematic literature review method to provide detailed analyses. Specific keywords and a comprehensive database are employed for this study. Furthermore, the paper surveys the existing advancements in 3D printing machinery, focusing on process monitoring and control methods, as well as their impact on sustainability. The discussion section evaluates the literature review results for potential implementation in small and medium-sized enterprises.

REVIEW THE ASSESSMENT OF EFFECTS OF LOST TIME INJURIES IN AN INDUSTRIAL SYSTEM BY USING AN EXPLANATORY PROGRAM

Health and safety problem can be described by statistics it can only be understood by knowing and feeling the pain, suffering, and depression. Health and safety has a legal responsibility to protect it for everyone who can affect in the workplace. This includes manufacturers, suppliers, designers and controllers of work places and employees. Work injury is one of the major problems in manufacturing and production systems industries; it is reduced production efficiency and affects the cost. To gain flexibility from a traditional manufacturing system and production efficiency, this paper is about the application of estimating technology to preview and synthesis of Lost Time of Work Injuries in industry systems aims to provide a safe working environment for all employees to achieve safe workplaces, safe systems of work, and safety understanding within our workforce. Our industry oftenhas a poor record in dealing with modern and development techniques. Thus, as one of the targets, this leads to perform a helpful program plane to provide guidelines of management, employees to eliminate hazards, given the enormous cost of occupational personal damage in industry and to develop safe work methods work site. This work proposed a general methodology for constructing an explanatory software system to review and analysis workers injuries in a work site. The program language used is Axes, which suitable to shows categories of incidences and estimates costs to workers, employers and society of workplace injuries and work-related ill health. Data of the system was collected in the State Company for Woolen Industries in Al-Kadhumiaa in Iraq.The resulting is to provide a simple obvious outline system to evaluate lost time injury and the net-costs on safety interfering at the company level to reduce occupational morbidity and generating a helpful system to estimate of the total costs to employers and workers of workplace accidents and work-related ill health.

Machine Learning for Industry 4.0 [From the Guest Editors

The Fourth Industrial Revolution, also known as Industry 4.0, marks the technological shift from traditional manufacturing systems to smart cyberphysical systems. It leads to an improvement in overall productivity and a reduction in environmental impact and promotes sustainable economic development. Industry 4.0 has been driven by emerging technologies such as the Internet of Things (IoT), also called the Industrial Internet of Things; digital twins; artificial intelligence; cloud computing; and edge/fog computing <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> . It is a hot topic in both academia and industry. The implementation of IoT connects physical assets to cybernetworks and captures a significant amount of data. These data, often “big”, are then fed to AI-based mission-critical systems to perform production monitoring, quality inspection, fault root cause analysis, quality prediction, and process control. The proper adoption of relevant Industry 4.0 technologies should lead to significant efficiency improvements and cost reductions in various industrial sectors.

Toward Digital twin for sustainable manufacturing: A data-driven approach for energy consumption behavior model generation

Traditional manufacturing systems face a major transition challenge toward intelligent and sustainable manufacturing systems. Energy-efficient manufacturing has become one of the main focuses of this transition because of the high and critical consumption of energy for the production of many industrial sectors. To this aim, modeling Energy Consumption (EC) behavior is a primary step to monitor and reduce consumption. However, it requires a deep understanding of the system’s kinematic and dynamic behaviors. Hence, creating a model from scratch can be challenging, which may result in a model that does not correctly represent the real system. With the advancement of digital technologies, it is now possible to collect and analyze data from manufacturing systems in real-time. This opens the door to the possibility of modeling the energy consumption behavior of different machine states based on a data-driven approach and keeping the current energy consumption under control and monitored using real-time data from the equipment. Prior research has been conducted in the literature incorporating EC modeling into a Digital Twin (DT). However, the addressed issue remains an open challenge due to its complexity. The proposed methodology solves the lack of literature by proposing a methodology that makes the EC modeling within the reach of any researcher or practitioner in the field. The current paper proposes a data-driven methodology for integrating the EC model into Digital Twins. The methodology is based on measurements to identify different segments and sub-states of EC of production equipment, using techniques such as segmentation and regression. It relies on power absorption measurement of industrial equipment to generate EC related-parameters to be fed into the DT model to monitor the current operating condition of the physical system. This work contributes to the DT-based sustainable transition by allowing to monitor and quantitatively measure those parameters which could be controlled to reduce EC. A case study on an industrial robot is used to validate and assess the performance of the approach in a laboratory environment.

Quantifying and exhibiting the congruence of process choice criteria with traditional and additive manufacturing systems

A well-determined manufacturing system helps organisations achieve a desired competitive advantage. So, the manufacturing system selection concerns the critical decision of manufacturing strategy deployment. In the present era of industry 4.0, there exist four traditional manufacturing systems (TMS) (i.e. job-shop, batch-shop, mass, and continuous) and additive manufacturing system (AMS). Different process choice criteria (PCC) are responsible for selecting the best-suited system from five or a hybrid (AMS + TMS) configuration. This research formulates a two-stage framework comprising Delphi and Voting analytical hierarchy process (VAHP) techniques for quantifying the congruence between PCC and manufacturing systems. Initially, extant literature is reviewed to identify the critical PCC and further validated by industry experts through Delphi. This results in retaining thirty-six PCC. The relative importance of an individual criterion concerning a particular manufacturing system is computed using VAHP in the second stage to understand the alignment of PCC in different manufacturing systems for exhibiting the congruence between PCC and manufacturing system (TPS and AMS). The findings offer critical insights about the different PCC and their level-of-fit in TMS and AMS, which can assist researchers and practitioners in evaluating a suitable manufacturing system for an organisation using identified PCC.

Zero Defect Manufacturing ontology: A preliminary version based on standardized terms

The global transition from traditional manufacturing systems to Industry 4.0 compatible systems has already begun. Therefore, the digitization of the manufacturing systems across the globe is increasing with exponential growth which implies a significant increase in the volume and variety of the generated data. Industry 4.0 technologies are mostly data driven and therefore, manufacturers need to be equipped with the appropriate tools and skill sets to extract useful knowledge and insights from the plethora of data continually collected form shop floors. Furthermore, quality assurance is a key domain in manufacturing that uses almost all the industry 4.0 technologies and has great impact on the sustainability of a manufacturing systems. The latest approach to higher quality and manufacturing sustainability is named Zero Defect Manufacturing (ZDM). ZDM interest has spiked the last three years illustrating the need for an alternative quality assurance approach from the traditional such as Six Sigma and Lean manufacturing. Therefore, the goal of this paper is to create a ZDM ontology that can semantically align multiple software systems that interact in a ZDM ecosystem. The development of the proposed ZDM ontology was performed using the principles introduced by Industrial Ontology Foundry (IOF) and with the use of Basic formal ontology (BFO) as an upper level ontology. The proposed ontology was utilized in the Prediction Optimization Designer tool developed, to assist developers to create new projects reusing existing resources, or to respond to a specific challenge. The use case validation results show that the combination of Natural Language Processing (NLP) using Sentence-BERT and ontology-based search methods rooted in the ZDM ontology is a promising strategy to implement effective search engines for applications in the ZDM domain.

Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing

Fog manufacturing can greatly enhance traditional manufacturing systems through distributed Fog computation units, which are governed by predictive computational workload offloading methods under different Industrial Internet architectures. It is known that the predictive offloading methods highly depend on accurate prediction and uncertainty quantification of runtime performance metrics, containing multivariate mixed-type responses (i.e., continuous, counting, binary). In this work, we propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics and to enable the statistical inferences. The proposed method considers both group and individual variable selection to jointly model the mixed types of runtime performance metrics. The conditional dependency among multiple responses is described by a graphical model using the precision matrix, where a spike-and-slab prior is used to enable the sparse estimation of the graph. The proposed method not only achieves accurate prediction, but also makes the predictive model more interpretable with statistical inferences on model parameters and prediction in the Fog manufacturing. A simulation study and a real case example in a Fog manufacturing are conducted to demonstrate the merits of the proposed model.

Industry 4.0-Oriented Turnkey Project: Rapid Configuration and Intelligent Operation of Manufacturing Systems

More extensive personalized product requirements and shorter product life cycles have put forward higher requirements for the rapid establishment, commissioning, and operation of corresponding manufacturing systems. However, the traditional manufacturing system development process is complicated, resulting in a longer delivery time. Many manufacturing enterprises, especially small and micro enterprises, may not have the necessary manufacturing knowledge or capabilities to meet these requirements. Therefore, it is essential to promote the construction of turnkey projects under the paradigm of Industry 4.0, parallelizing and integrating the existing manufacturing system development process based on mass manufacturing equipment to quickly provide turnkey solutions for manufacturing systems’ configuration and implementation for these enterprises. This paper aims to extract and refine the configuration and operation key views of the Industry 4.0-oriented Turnkey Project (I4TP) from Reference Architecture Model Industrie 4.0 (RAMI4.0) and use it to guide the development of key functional processes of turnkey projects to achieve rapid configuration and efficient operation management of manufacturing systems. The turnkey project platform in the Advanced Manufacturing Technology Center (AMTC) is taken as a demonstration case to provide a reference idea for the rapid configuration and intelligent operation of the turnkey manufacturing system.

Smart and flexible manufacturing systems using Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT)

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.

Machine-Vision-Based Intelligent Manufacturing by Fine-Grained Point Cloud Identification.

Traditional manufacturing system inspections are primarily conducted through microtools. Nowadays, the size of consciously manufactured parts that is getting smaller and smaller, wherein unwritten methods are subject to subjectivity, often resulting in reckless deviations. It is necessary to develop a highly efficient and correct discovery technique. Given the leaned flexibility of traditional manufacturability analysis methods based on cognitive notoriety and empire foundation, the reality that existing manufacturability analysis methods based on full scientific support cannot give a specific purpose for capability manufacturability. A deep cognition support framework grants the manufacturability analysis example. Manufacturing separative methods are presented as imitate. First, a large enumeration of 3D CAD plan with specific manufacturability are made by digital modeling technology, wherein the tier genealogy is realized to cause the data adjustments required for thorough academics. Then, concavity-oriented designs are established on the PointNet entangle form manufacturability. Dense learned grids were analyzed, and network parameter tuning and management were done; then comparisons with voxel-representation-enabled three-dimensional convolutional neural networks (3D-CNN) and existing methods revealed detailed literature for fabricating networks with better robustness and lower algorithm cycle complexity; finally, the actual completion of the network is verified through the example section, and the manufacturability of the cave shape is analyzed to identify the unmanufacturable overall form and explain its considerations. The experimental results have shown that the rule can determine the specific reasons for the unmanufacturable shape under the condition of ensuring the complete notification accuracy ratio, and has a great reproducibility value.