Components Of Manufacturing Systems Research Articles

The State of The Knowledge of Cloud Computing Technology for Manufacturing Automation Systems

The manufacturing sector is grappling with the need to adapt to rapid technological changes, and leveraging cloud computing is becoming crucial for staying competitive and resilient. The presentation of knowledge in this work focuses on highlighting the specific challenges and opportunities in integrating cloud technologies into manufacturing systems. It aims to answer critical questions such as how cloud computing can enhance manufacturing automation, solve problems, and benefit to the industry. The methodology employed in this research takes a comprehensive and top-down approach, aligning and exploring the practical aspects of implementing these X-as-a-Service (XaaS) model in manufacturing setups. The research also acknowledges the shift from legacy Distributed Numerical Control (DNC) systems to modern solutions like MTConnect and Open Platform Communication (OPC) for data exchange in automated manufacturing systems. Emphasizing the important of data collection and real-time monitoring, the study highlights the role of Industrial Internet of Things (IoT) sensors deployed at various points of manufacturing system components (machine tools, spindles, cutting tools, production units, etc.). These sensors capture real-time production and condition data, enabling informed decision-making in manufacturing systems. This research not only presents the latest knowledge but also offers insights into the challenges, strategies, and methodologies involved in the successful integration of cloud-based technology into manufacturing automation systems. It also aims to serve as a valuable resource for manufacturers, researchers, and industry professionals navigating the transformative journey toward cloud-powered manufacturing.

Decisioning-Based Approach for Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

Decisioning-Based Approach for Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

Neutrosophic MCDM Methodology to Select Best Industrial Arc Welding Robot

Industrial robots have made it possible for industrial companies to make goods of a good quality at lower costs. As a result, industrial robots are an integral component of sophisticated manufacturing systems. Industrial robots may be programmed to do a wide variety of tasks, including welding, painting, construction, and debugging. All of the elements are completed with an exceptional level of endurance, swiftness, and accuracy. The efficiency of industrial robots is governed by a number of different factors, some of which are in direct opposition to one another; for a strong choice approach, all of these criteria must be examined concurrently. For the purpose of selecting an industrial robot for the arc soldering process, a straightforward multi-criteria decision-making (MCDM) approach that is VIKOR method will be described in this research. The VIKOR method used to rank the robots. The results of the VIKOR methodology are provided here in the form of a priority of rating. The findings demonstrated that the MCDM strategies are highly helpful when selecting robots to utilize.

A Study of Components of Total Quality Management and Lean Manufacturing System Affecting Management Efficiency of Computer manufacturing industry in Thailand.

A Study of Components of Total Quality Management and Lean Manufacturing System Affecting Management Efficiency of Computer manufacturing industry in Thailand.

Capability-based remaining useful life prediction of machining tools considering non-geometry and tolerancing features with a hybrid model

ABSTRACT Machining tools are vital components of intelligent manufacturing systems whose state and remaining useful life (RUL) determine product quality. Specifically, the wearing of tool reduces its capability of production yield and diminishes product quality. Therefore, a capability-based RUL prediction approach is proposed in this paper to thoroughly evaluate the state and RUL of machining tools. First, the connotation of tool capability is discussed, and a framework for quality assurance capability-based RUL prediction is proposed. Product quality, which can be used to assess the capability of tool, is modelled and expanded to consider non-geometric dimensioning and tolerancing (non-GD&T) features based on the classic geometric dimensioning and tolerancing (GD&T) system. Second, a physics-based model of process is developed to estimate the non-GD&T features and calculate tool wear. Third, a hybrid data-driven and physics-based model is developed to quantitatively assess the capability of tool based on the comprehensive quality estimation. Finally, a case study of rolling machining tool is carried out to verify the effectiveness and proactiveness of the proposed framework, and the final result highlights its rationality and accuracy in estimating the RUL of machining tools with better interpretation.

Automatic grouping of production data in Industry 4.0: The use case of internal logistics systems based on Automated Guided Vehicles

Automated Guided Vehicles (AGVs) have become an indispensable component of Flexible Manufacturing Systems. AGVs are also a huge source of information that can be utilised by the data mining algorithms that support the new generation of manufacturing. This paper focuses on data preprocessing, aggregation, and clustering in the new generation of manufacturing systems that use the agile manufacturing paradigm and utilise AGVs. This paper presents a method by which it is possible to automatically detect the types of work performed by an AGV based on streaming information. The paper presents a comparison of results obtained with two different classical clustering algorithms: K-Means and DBScan. As the results show, using at least one of the proposed algorithms, it is possible to automatically detect the types of work performed by AGVs and to detect new, previously unknown types. Additionally, to a limited extent, it is possible to identify anomalies using this method. The proposed methodology can be used as the initial step for production optimisation, predictive maintenance activities, production technology verification, or as a source of models for the simulation tools that are used in virtual factories. This is an extended version of the ‘Data preprocessing, aggregation and clustering for agile manufacturing based on Automated Guided Vehicles’ paper [43].

Weighted Voting in Physical Layer Authentication for Industrial Wireless Edge Networks

Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a pre-processing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and thus this paper presents a solution to enhance the access authentication accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information (CSI) based authentication using a single sample which include sample segmentation, grouping, and weighted voting, and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices' sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods.

Knowledge-based engineering approach for defining robotic manufacturing system architectures

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time. A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users’ unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution.

Industry 4.0 Perception Regarding to New Developments and New Trends of Industries

The purpose of this research is to assess the core principles of Industry 4.0, its advantages, literature review, and effective implementation techniques. Searching for published publications may demonstrate recent developments of industries with big data of production system and technology. Industrialization is a catch-all term for a wide range of modern automated systems, data exchanges, and manufacturing technologies. This revolution is a set of ideals comprised of the internet of things, online services, and cyber-physical systems. While the Industry 4.0 model represents a revolution in every aspect, it also delivers numerous breakthroughs, transformations, and adjustments. This research examines the evolution, technical advances, and benefits of Industrial 4.0, gives a brief background to this configuration and its use in industry implementations, and emphasizes the key components influencing successfully businesses. Furthermore, it is underlined those organizational innovations are required for the industry 4.0 revolution in terms of product, process, and business advancements. As a result, a significant deal of innovation is necessary in many sectors before the industry 4.0 model can be implemented. In product, process, business, and operational terms, innovation refers to variety that creates additional value. To that end, the ideas of Industry 4.0 and innovation are described briefly, followed by an examination of innovation through the lens of Industrial 4.0, with illustrations and concepts provided. It is abundantly clear through publications that are being searched those sectors and industries are making use of big data in the production environment and information. This research reflects on the developments of framework-structure in the enviroment of industry for the business-companies. The research also contains the aspect-application of the industry 4.0 technology depending on manufacturing system and improvement phases of manufacturing-production of the company. The application via Industry 4.0 technology of a white goods manufacturing company was evaluated on behalf of manufacturing performance and effectiveness of this manufacturing enterprise according to inputs and outputs components of manufacturing system for the analysis in terms of operation management and the business expectation.

A Generic Plug & Produce System Composed of Semantic OPC UA Skills

Typical industrial workcells are composed of a plenitude of devices from various manufacturers, which rely on their own specific control interfaces. To reduce setup and reconfiguration times, a hardware-agnostic Plug & Produce system is required. In this paper, we present a system architecture that uses generic and semantically augmented OPC UA skills for robots, tools, and other system components. Standardized skill interfaces and parameters facilitate flexible component interchange and automatic parametrization with a focus on reusability of skills across different platforms and domains. The hierarchical composition of such skills allows for additional abstraction through the grouping of functionalities. Through the extension of OPC UA discovery services, available skills are dynamically detected whenever a manufacturing system's component is updated. The introduced Plug & Produce system is evaluated in multiple industrial workcells composed of robots, tool changer, electric parallel gripper, and vacuum gripper-all controlled via the proposed OPC UA skill interface. The evaluation of our system architecture demonstrates the applicability of the Plug & Produce concept in the domain of robot-based industrial assembly. Although it is necessary to adapt existing hardware to comply with the semantic skill concept, the initial one-time effort yields reoccurring efficiency gains during system reconfiguration. In particular, small lot production benefits from reduced changeover times.

Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network

Deep learning has seen increased application in the data-driven fault diagnosis of manufacturing system components such as rolling bearing. However, deep learning methods often require a large amount of training data. This is a major barrier in particular for bearing datasets whose sizes are generally limited due to the high costs of data acquisition especially for fault scenarios. When small datasets are employed, over-fitting may occur for a deep learning network with many parameters. To tackle this challenge, in this research, we propose a new methodology of parallel convolutional neural network (P-CNN) for bearing fault identification that is capable of feature fusion. Raw vibration signals in the time domain are divided into non-overlapping training data slices, and two different convolutional neural network (CNN) branches are built in parallel to extract features in the time domain and in the time-frequency domain, respectively. Subsequently, in the merged layer, the time-frequency features extracted by continuous wavelet transform (CWT) are fused together with the time-domain features as inputs to the final classifier, thereby enriching feature information and improving network performance. By incorporating empirical feature extraction such as CWT, this proposed method can effectively enable deep learning even with dataset size limitation in practical bearing diagnosis. The algorithm is validated through case studies on publicly accessible experimental rolling bearing datasets. A wide range of dataset sizes is tested with cross-validation, and influencing factors on network performance are discussed. Compared with existing methods, the proposed approach not only possesses higher accuracy but also exhibits better stability and robustness as training dataset sizes and load conditions vary. The concept of feature fusion through P-CNN can be extended to other fault diagnosis applications in manufacturing systems.

Концепция поддержки лазерного аддитивного производства на основе онтологического подхода

A general concept of software and information support for laser-based additive manufacturing of metal parts from powder compositions is proposed. It is based on an ontological two-level approach to the formation of knowledge about the processes of laser additive manufacturing. Under this approach, the ontology is clearly separated from the knowledge base. So subject-matter specialists can create and develop knowledge in the representation that they understand. The conceptual architecture of the decision support software for laser-based additive manufacturing processes is presented. Its information and software components are described. Information components are ontologies, databases of laserbased additive manufacturing system components, databases of materials for additive manufacturing, knowledge base and case database. The knowledge base contains formalized information on the settings of laser-based additive manufacturing modes that ensure compliance of the obtained metal parts with the requirements of the current industry-specific guidelines (regulatory documents for this type of parts). The case database contains a structured description of the protocols for using laser technological equipment for additive manufacturing of metal parts from powder compositions. Software components are editors for creating and maintaining data and knowledge bases (controlled by the corresponding ontologies), decision support system based on both, knowledge and cases, and tool for cases structuring. There are also external tools for mathematical modelling of directed energy deposition physicochemical processes. When making decisions a hybrid approach that combines knowledge engineering methods and case-based search by analogy is proposed. The specialty of the approach is the continuous knowledge base update due to its improvement by experts and due to its verification in the process of accumulating cases.

A six-layer architecture for the digital twin: a manufacturing case study implementation

Industry 4.0, cyber-physical production systems (CPPS) and the Internet of Things (IoT) are current focusses in automation and data exchange in manufacturing, arising from the rapid increase in capabilities in information and communication technologies and the ubiquitous internet. A key enabler for the advances promised by CPPSs is the concept of a digital twin, which is the virtual representation of a real-world entity, or the physical twin. An important step towards the success of Industry 4.0 is the establishment of practical reference architectures. This paper presents an architecture for such a digital twin, which enables the exchange of data and information between a remote emulation or simulation and the physical twin. The architecture comprises different layers, including a local data layer, an IoT Gateway layer, cloud-based databases and a layer containing emulations and simulations. The architecture can be implemented in new and legacy production facilities, with a minimal disruption of current installations. This architecture provides a service-based and real-time enabled infrastructure for vertical and horizontal integration. To evaluate the architecture, it was implemented for a small, but typical, physical manufacturing system component.

A systematic methodology for the modularization of manufacturing systems during early design

A methodology is proposed to provide a guided process for modularization of manufacturing system components within the broader design process. The application focus is on manufacturing systems, including flow processes. Component clusters are formed by simultaneously considering physical-functional and strategic drivers that are fundamental to a more holistic partitioning of the system. The clustering algorithm now includes the ability to compel component unification or separation. A broad review of modularization methods was conducted to improve upon the state-of-the-art. Current methodologies can be difficult to use, are disjoint in their goals, lack objectivity, and do not produce externally repeatable solutions. The proposed methodology aims to improve upon these limitations by providing a step-by-step framework and integrating strategic and technical perspectives when clustering. The methodology is structured to guide the user toward a deeper understanding of the system, whether or not one is an expert, whereby the designer can focus more on the technical and strategic issues that should inform the modularization. A realistic case study for the modularization of a transportable fast pyrolysis plant was performed to evaluate the meaningfulness of the methodology’s results. The methodology clustered components into modules consistent with the information provided to the model, reflecting what would be expected from a deep understanding of the system.

Understanding reconfigurability of manufacturing systems: An empirical analysis

The need for more responsive manufacturing systems to deal with high product variety and large fluctuations in market demand requires new approaches that enable the system to react to changes quickly and efficiently. Reconfigurability is an ability that allows the addition, removal or rearrangement of manufacturing system components and functions to better cope with high product variety and significant fluctuations in market demand in a cost effective way. This paper empirically investigates the understanding of reconfigurability in industrial manufacturing companies and tests and validates its core characteristics using a questionnaire survey, which was carried out with Portuguese companies. Findings show the existence of five core characteristics of reconfigurability. The implications of these characteristics, concerning the implementation of Reconfigurable Manufacturing Systems, are also analysed and discussed.

An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

There is no doubt that the rapid development in robotics technology has dramatically changed the interaction model between the Industrial Robot (IR) and the worker. As the current robotic technology has afforded very reliable means to guarantee the physical safety of the worker during a close proximity interaction with the IR. Therefore, new forms of cooperation between the robot and the worker can now be achieved. Collaborative/Cooperative robotics is the new branch of industrial robotics which empowers the idea of cooperative manufacturing. Cooperative manufacturing significantly depends on the existence of a collaborative/cooperative robot (cobot). A cobot is usually a Light-Weight Robot (LWR) which is capable of operating safely with the human co-worker in a shared work environment. This is in contrast with the conventional IR which can only operate in isolation from the worker workspace, due to the fact that the conventional IR can manipulate very heavy objects, which makes it so dangerous to operate in direct contact with the worker. There is a slight difference between the definition of collaboration and cooperation in robotics. In cooperative robotics, both the worker and the robot are performing tasks over the same product in the same shared workspace but not simultaneously. Collaborative robotics has a similar definition, except that the worker and the robot are performing a simultaneous task. Gathering the worker and the cobot in the same manufacturing workcell can provide an easy and cheap method to flexibly customize the production. Moreover, to adapt with the production demands in the real time of production, without the need to stop or to modify the production operations. There are many challenges and problems that can be addressed in the cooperative manufacturing field. However, one of the most important challenges in this field is the representation of the cooperative manufacturing environment and components. Thus, in order to accomplish the cooperative manufacturing concept, a proper approach is required to describe the shared environment between the worker and the cobot. The cooperative manufacturing shared environment includes the cobot, the co-worker, and other production components such as the product itself. Furthermore, the whole cooperative manufacturing system components need to communicate and share their knowledge, to reason and process the shared information, which eventually gives the control solution the capability of obtaining collective manufacturing decisions. Putting into consideration that the control solution should also provide a natural language which is human readable and in the same time can be understood by the machine (i.e., the cobot). Accordingly, a distributed control solution which combines an ontology-based Multi-Agent System (MAS) and a Business Rule Management System (BRMS) is proposed, in order to solve the mentioned challenges in the cooperative manufacturing, which are: manufacturing knowledge representation, sharing, and reasoning.

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.

A Tripropylene Glycol Diacrylate-based Polymeric Support Ink for Material Jetting

Support structures and materials are indispensable components in many Additive Manufacturing (AM) systems in order to fabricate complex 3D structures. For inkjet-based AM techniques (known as Material Jetting), there is a paucity of studies on specific inks for fabricating such support structures. This limits the potential of fabricating complex 3D objects containing overhanging structures. In this paper, we investigate the use of Tripropylene Glycol Diacrylated (TPGDA) to prepare a thermally stable ink with reliable printability to produce removable support structures in an experimental Material Jetting system. The addition of TGME to the TPGDA was found to considerably reduce the modulus of the photocured structure from 575MPa down to 27MPa by forming micro-pores in the cured structure. The cured support structure was shown to be easily removed following the fabrication process. During TG-IR tests the T5% temperature of the support structure was above 150°C whilst the majority of decomposition happened around 400°C. Specimens containing overhanging structures (gate-like structure, propeller structure) were successfully manufactured to highlight the viability of the ink as a support material.

The Relationship Between Flexibility of Manufacturing System Components, Competitiveness of SMEs and Business Performance: A Study of Manufacturing SMEs in Northern India

The rapid changes in the manufacturing environment have become the greatest challenge for a manufacturing unit. This leads to the demand for change from the traditional manufacturing strategies to more flexible strategies. To sustain in the hyper-competitive market, a flexible system strategy is required. For designing such strategies and policies, it is important to know the factors that influence the performance of the system. The aim of this paper is to establish the relationship between the flexibility of components of a manufacturing system (machine, material handling and worker), competitiveness and business performance of small and medium scale enterprises (SMEs). A survey was conducted in SMEs of northern India. Three sectors, namely automotive, machine tool and light engineering (mechanical components and equipment), were analysed under manufacturing SMEs. The study contributes towards investigating the effect of the machine, material handling and worker flexibility on competitiveness and further competitiveness on business performance of manufacturing firms. This paper presents a structural equation model displaying the relationship between the flexibility of manufacturing system components, competitiveness of SMEs, and business performance.

A scheduling procedure for a flow shop–like knowledgeable manufacturing cell with self-evolutionary features

As a component of knowledgeable manufacturing systems, the structure of flow shop–like knowledgeable manufacturing cells is similar to that of a flow shop, thus representing an NP-hard issue. Here, we propose a self-evolutionary algorithm that exhibits learning ability and is composed of learning and scheduling modules. Unlike traditional scheduling algorithms, whose performances remain unchanged when the procedure is coded, the performance of the algorithm proposed in this study gradually improves as the learning process continues. The self-evolutionary ability is realized through the training of a hybrid kernel support vector machine. The hybrid kernel support vector machine was designed to approximate the value of the Q-function to select the appropriate action for the scheduling module and thus to obtain the optimal solution. An iterative process of value based on the Q-learning was adopted to train the hybrid kernel support vector machine to gradually enhance the algorithm’s efficiency and accuracy. The extracted state features of the flow shop–like knowledgeable manufacturing cells serve as inputs to hybrid kernel support vector machine for easy generalization of the learning results. The action exerted on a feasible solution is also defined as the input of the hybrid kernel support vector machine. The computational results show that the performance of the proposed procedure improves as the learning process progresses. Data from the computation and comparisons with other algorithms verify the validity and efficiency of the proposed algorithm.