Modern Manufacturing Systems Research Articles

كلية الإدارة والاقتصاد/ جامعة كربلاء/ العراق

The research aims to clarify the concept of achievement accounting, the requirements for its application and its mechanism of action, and to assess the extent of its activation of cost systems by providing information to improve cost systems and evaluate performance and to identify the reality of the existing cost system in the company (General Dairy Products). The research concluded that the traditional cost systems do not show the real result of the activity, while the achievement accounting shows the reality of the losses incurred by the company during the period. (Abu Ghraib Factory) by applying and using various modern manufacturing systems in production, as well as keeping pace with the rapid technological developments witnessed by the modern manufacturing environment to face the intense competition that characterizes the markets by providing products that meet the needs of customers quickly, at the lowest cost and with high quality.

Power-efficient Strategies for Sensing in Autonomous Mobile Robots, a critical requirement of I4.0 standard

In a production environment, there are several challenges in meeting the Industry 4.0 (I4.0) standard requirements. Energy efficiency is an essential area of focus. In the production setup, the critical and real-time control systems need to be very efficient while implementing functions, namely, accurate sensing, fast processing and precise actuation. Automated Guided vehicles (AGVs) and Automated Guided Vehicles are an integral part of modern and intelligent manufacturing systems. Power consumption in such systems is directly proportional to the performance level achieved. However, there is a need to evolve strategies to reduce power consumption and attain optimal performance. Field Programmable Gate Array(FPGA) based controller solutions can provide competent performance at optimized power consumption. The proposed work discusses the requirements of I4.0 concerning energy efficiency infrastructures for the intelligent manufacturing setup. The need to develop efficient subsystems for sensing, decision-making and actuation based on FPGA is stressed. Thus the focus is on the FPGA based power-efficient models used for sensor fusion technique in Autonomous Mobile Robots. The fundamentals of sensor fusion technique and the need to fuse sensor data for improved decision making and actuation are emphasized.

A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0.

The Industry 4.0 paradigm is the focus of modern manufacturing system design. The integration of cutting-edge technologies such as the Internet of things, cyber–physical systems, big data analytics, and cloud computing requires a flexible platform supporting the effective optimization of manufacturing-related processes, e.g., predictive maintenance. Existing predictive maintenance studies generally focus on either a predictive model without considering the maintenance decisions or maintenance optimizations based on the degradation models of the known system. To address this, we propose PMMI 4.0, a Predictive Maintenance Model for Industry 4.0, which utilizes a newly proposed solution PMS4MMC for supporting an optimized maintenance schedule plan for multiple machine components driven by a data-driven LSTM model for RUL (remaining useful life) estimation. The effectiveness of the proposed solution is demonstrated using a real-world industrial case with related data. The results showed the validity and applicability of this work.

The role of modern manufacturing systems in process Design

The aim of the research to measure the correlation relationship between modern manufacturing systems and process design and measure the effect by adopting the regression; the research consists of two main variables, which are modern manufacturing systems and process design; it was applied in the production lines of the General Company for Construction Industries, There is a sample of managers, engineers, technicians, administrators, and some workers were selected to fill the special questionnaire with (70) forms which distributed and (65) were approved suitable for use, For data analysis the correlation coefficient was adopted to measure the relationship and regression analysis to find out the effect, Using (SPSS), So the first hypothesis for the research was rejected, that was stated (there is a correlation relationship between modern manufacturing systems and process design)

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

MODELING OF THE MANUFACTURING SYSTEMS STATE IN THE CONDITIONS OF THE LEAN PRODUCTION

This article investigates the hierarchy of the manufacturing system, which consists of a set of interrelated processes aimed at converting information, knowledge, energy, materials, and other resources into value for the consumer based on the principles of lean production. Modern manufacturing systems are becoming more and more complex to manage. The problems that need to be solved are associated with a significant number of time-varying parameters, large time delays, high non-linearity of processes, and a complex relationship between input and output parameters. Depending on the parameters of internal components and characteristics of external conditions, the state of manufacturing systems can change in an unpredictable manner. The paper considers many types of discrete states in which the system can be. The estimation of the probability of finding the manufacturing system in any of the given states was carried out using discrete Markov analysis. The article also presents the results of studies of possible transitions between states in which the production system is presented in the form of a transition matrix.

Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop

With the aggravation of the global greenhouse effect and environmental pollution, energy saving and emission reduction have already become the consensus of the manufacturing industry to enhance sustainability. A material handling system is an essential component of a modern manufacturing system, and its energy consumption (EC) is a non-negligible part when evaluating the total production EC. As typical transport equipment, automated guided vehicles (AGVs) have been widely applied in various types of manufacturing workshops. Correspondingly, AGV path planning is usually a multi-objective optimization problem, and closely related to the workshop logistics efficiency and the smoothness of the whole manufacturing process. However, the optimization objectives that current AGV path planning research mostly focuses on are transport distance, time, and cost, while EC or EC-related environmental impact indicators are seldom touched on. To address this, an investigation into the energy-saving oriented path planning is executed for a single-load AGV in a discrete manufacturing workshop environment. Based on the analysis of AGV EC characteristics from the perspective of motion state and vehicle structure, transport distance and EC are selected as two optimization objectives, and an energy-efficient AGV path planning (EAPP) model is formulated. Further, two solution methods, i.e., the two-stage solution method and the particle swarm optimization-based method, are put forward to solve the established model. Moreover, the experimental study verifies the effectiveness of the proposed model and its solution methods and indicates that transport task execution order has a significant impact on AGV transport EC.

Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots

• Methodology towards the enhancement of physics-based models with degradation curves. • Implementation and testing of the method for a robotic structure focusing on the friction of its axes’ bearings. • The utilization of Digital Twin in tandem with data extracted from degradation curves may be exploited to predict the future behaviour of the robot. Predictive maintenance has been proposed to maximize the overall plant availability of modern manufacturing systems. To this end, research has been conducted mainly on data-driven prognostic techniques for machinery equipment individual components. However, the lack of historical data together with the intricate design of industrial machines, e.g. robots, stimulate the use of advanced methods exploiting simulation capabilities. This paper aims to address this challenge by introducing a generic framework for the enhancement of advanced physics-based models with degradation curves. The creation of a robot's simulation model and its enrichment with data from the degradation curves of the robot's components is presented. Following, the extraction of information from degradation curves during the simulation of the robot's dynamic behaviour is addressed. The Digital Twin concept is employed to monitor the health status of the robot and ensure the convergence of the simulated to the actual robot behaviour. The output of the simulation can enable to estimate the future behaviour of the robot and make predictions for the quality of the products to be produced, as well as to estimate the robot's Remaining Useful Life. The proposed approach is applied in a case study coming from the white goods industry, where it is investigated whether the robot will experience some failure within the next 18 months.

Scheduling and Rescheduling Operations Using Decision Support Systems: Insights From Emotional Influences on Decision-Making.

For many years, manufacturers have focused on improving their productivity. Production scheduling operations are critical for this objective. However, in modern manufacturing systems, the original schedule must be regularly updated as it takes places in a dynamic and uncertain environment. The modern manufacturing environment is therefore very stressful for the managers in charge of the production process because they have to cope with many disruptions and uncertainties. To help them in their decision-making process, several decision support systems (DSSs) have been developed. A recent and enormous challenge is the implementation of DSSs to efficiently manage the aforementioned issues. Nowadays, these DSSs are assumed to reduce the users' stress and workload because they automatically (re)schedule the production by applying algorithms. However, to the best of our knowledge, the reciprocal influence of users' mental state (i.e., cognitive and affective states) and the use of these DSSs have received limited attention in the literature. Particularly, the influence of users' unrelated emotions has received even less attention. However, these influences are of particular interest because they can account for explaining the efficiency of DSSs, especially in modulating DSS feedback processing. As a result, we assumed that investigating the reciprocal influences of DSSs and users' mental states could provide useful avenues of investigation. The intention of this article is then to provide recommendations for future research on scheduling and rescheduling operations by suggesting the investigation of users' mental state and encouraging to conduct such research within the neuroergonomic approach.

Reliability analysis of aging control system via stability margins

Automatic control systems are critical sub-systems that ensure the reliable and efficient operation of a modern manufacturing system collaborating with other sub-systems. Critical to the performance of control systems are aging actuators that will ultimately result in an unstable control process and consequently failure of the supported manufacturing system. In this paper, by using the Nyquist stability criterion we prove the stability margin of a first-order control process with dead time, which is the fundamental failure threshold of the corresponding control system. The impact of aging actuators on the control system is modelled by errors in the dead time, which is the key parameter in the transfer function of a control process. By linking the stability margin with the physical degradation process of the actuator, we further derive explicitly the maximum allowable degradation level of the actuator and use the information to compute the failure probability of a control system. The proposed framework is demonstrated on a solar energy platform which can serve as a significant illustration for sustainable manufacturing. This platform is equipped with an aging valve controlling its temperature and the degradation process is modelled by an inverse Gaussian process with a curved hitting boundary to address the varying working conditions of the valve. Our case study shows that our method approximates the exact failure probability well with much lower computational cost compared to the traditional Monte Carlo simulation.

An adaptable framework to provide AR-based work instructions and assembly state tracking using an ISA-95 ontology

The high degree of digitalization in modern manufacturing systems and the increase in the systems that need to exchange data so that the production information reaches the target employee has pushed forward the need to apply standardized ontologies. This article presents how a modular framework to create and provide AR-based work instructions coupled with image-based state tracking can be modelled in an ontology based on the industrial ISA-95 standard to represent the data exchange among the different modules. The proposed modelling of the data exchanged in such a framework is validated in a use case from the agriculture machinery industry.

Integrated process-system modelling and control through graph neural network and reinforcement learning

Modern manufacturing systems are becoming increasingly complex, dynamic, and connected, and their performance is being affected by not only their constituent processes but also their system-level interactions. This paper presents an integrated modelling method based on a graph neural network (GNN) and multi-agent reinforcement learning (MARL) collaborative control for adjusting individual machining process parameters in response to system- and process-level conditions. The structural and operational dependencies among process machines are captured with a GNN. Iteratively trained with MARL, machines learn to adaptively control local process parameters, e.g., machining speed and depth of cut, while achieving the global goal of improving production yield.

A New Graph-Based Method for Class Imbalance in Surface Defect Recognition

Surface defect recognition is an important technology to guarantee the quality of products in modern manufacturing systems, but the class imbalance in real-world production greatly influences its performance. Recently, some deep learning-based methods have been developed to address the class imbalance. Because of the interclass similarities and the intraclass variations (ISIV) of surface defect, it is hard to apply them to solve the coupling between class imbalance and ISIV. Graph-based methods, including graph convolutional network (GCN), have the potential for ISIV. Therefore, this article proposes a new graph-based method, named anchor-based class-balanced GCN (ACB-GCN), to solve the class imbalance in surface defect recognition. First, the proposed method constructs a class-balanced graph to address the problem that excessive information from majority classes influence the performance of graph convolution. Second, the proposed method defines anchor vectors in each defect to reduce the influence of ISIV on graph construction. The experimental results on four famous data sets with class imbalance demonstrate that the proposed method can effectively address the coupling between class imbalance and ISIV, and thus extract the discriminative features. Meanwhile, the proposed method achieves better performance than traditional methods and the original GCN, especially on minority classes.

A Digital Twin architecture for monitoring and optimization of Fused Deposition Modeling processes

In the realm of the fourth Industrial Revolution, digital technologies are constantly integrated in Manufacturing processes in order to improve system reliability, monitor processes accurately and ultimately optimize processes. In order to tackle these issues, advanced simulation techniques such as Digital Twins (DT) have to be developed. Further to that, modern manufacturing systems are based on the integration of non-conventional processes, such as Additive Manufacturing (AM). Therefore, in this research work a Fused Deposition Modeling (FDM) DT is designed and developed, integrating quality assessment modules. Furthermore, a database has been modelled in an attempt to monitor all the experiment results. Additional modules have also been created and integrated, such as compensating the effects of printing, as well as immersive interfaces enabling the users to control and monitor the machine state remotely. Consequently, one of the key concepts of this paper is the design of the information flow between the above-mentioned modules for optimizing the FDM process parameters, and thus minimize time and resources caused by human error. The implementation of the proposed framework is based on the integration of suitable communication channels.

A Hybrid Genetic Algorithm for Flexible Job Shop Scheduling Problem With Sequence-Dependent Setup Times and Job Lag Times

This paper addresses the flexible job shop scheduling problem with sequence-dependent set-up times and job lag times (FJSP-SDST-LT), which characteristics are important in modern manufacturing systems. We first present a mathematical model with the objective to minimize the makespan. Then a hybrid algorithm (HGA-TS) which combines genetic algorithm (GA) and tabu search (TS) is proposed to solve the FJSP-SDST-LT. The GA performs powerful global search by genetic operators and serves as exploration, while based on the specific structure of SDST and LT, the TS is able to perform an effective local search by relocating operations and serves as exploitation. Therefore, the proposed HGA-TS integrates good searching ability with strong diversifying ability. In order to solve the FJSP-SDST-LT effectively, we adopt effective encoding and decoding methods, genetic operators in GA, and four neighborhood structures in TS. We conduct computational experiments on two classes of instances generated from two classic data sets and the results show the great performance of HGA-TS in solving FJSP-SDST-LT.

Model of multidimensional resource configuration in production scheduling: proactive and reactive approach

Abstract Modern manufacturing systems are characterized by a high degree of automation and robotization as well as saturation with IT technologies such as IoT, Cloud Computing, RFID, etc. This results in shortening both production cycles and supply chains. It also means that the classic approach to resources as well as planning and scheduling processes has to be changed. The multidimensionality of resources, their high configurability and a proactive and reactive approach to planning and scheduling are the main elements of these changes. The paper considers the problem of schedule robustness in relation to the availability of multidimensional resources. A model of multidimensional resource configuration was proposed, which is the basis of a proactive-reactive robust approach to planning/scheduling in the context of manufacturing problems. There is also presented implementation of the model using mathematical programming and a proprietary hybrid approach.

UAVs for Industrial Applications: Identifying Challenges and Opportunities from the Implementation Point of View

New technologies such as Unmanned Aerial Vehicles (UAV) are constantly being introduced under the Industry 4.0 framework. Although UAVs are mainly used in civil and military applications, the opportunities for Industrial integration must be examined, namely real-time remote monitoring, wireless coverage, and remote sensing. Therefore, UAVs can be considered as proactive solvers, simultaneously contributing to enhanced decision making as they are considered to be Internet of Things (IoT) platforms for efficient cost-effective data collection and monitoring. However, the use of UAVs in confined and crowded Industrial environments, e.g. machine shops, need further research. Therefore, this paper focuses on highlighting the limitations regarding the integration of UAVs in modern manufacturing systems as well as on presenting an intelligent framework based on Industrial Internet of Things (IIoT) for real-time machine shop monitoring. The applicability of the developed framework is tested in-vitro, in a laboratory-based machine shop.

Machine Fault Diagnosis of Fused Filament Fabrication Process with Physics-Constrained Dictionary Learning

Sensors have been widely applied in modern manufacturing systems to monitor the processes and machine health conditions in order to control product quality. Processing a large amount of sensor data becomes a new challenge on the efficiency of diagnosis. In this paper, a novel physics-constrained dictionary learning approach is proposed to simultaneously improve the efficiency of data collection with compressed sensing (CS) and perform diagnosis with the classification of sensor data. Two-stage optimization is performed. At the first stage, measurement matrix is optimized to determine the time stamps of collected data points with a fixed basis matrix. This is solved based on a constrained FrameSense algorithm. At the second stage, the basis and classification matrices are optimized with the fixed measurement matrix based on the K-SVD algorithm. The above two optimization steps are repeated until the optimal measurement, classification, and basis matrices converge without further improvement. The recovered signals can be classified more accurately based on the learned classification matrix for specific data. The proposed approach for machine fault diagnosis is demonstrated with acoustic emission signals collected in the fused filament fabrication process.

Surface Response to Mechanics of Hardness and Wear Characteristics of Nanoscale Hydrophobic Film

There is an increase in demand for durable and efficient organic nanoscale coatings for modern manufacturing systems and/or components for various applications. In wake of this demand, a new superhydrophobic silane compound was deposited on mild steel, stainless steel and titanium substrates which were pre-treated with alumina via the atomic layer deposition (ALD) method. Three substrates were considered for evolving properties comparison, also, determination of the influence of substrates’ response to films mechanical properties. The mechanical properties and the failure mechanics were investigated through nanoindentation and nanoscratch while the morphologies were examined through a field emission scanning electron microscope. The morphological evolution and mechanical viability indicated that stainless steel showed the most robust properties when compared with other substrates. Therefore, a nanoscale coating’s mechanical strength can be influenced by the substrate’s material compositions.

MOSAIK: A Formal Model for Self-Organizing Manufacturing Systems

In this article, we review past and current system architectures displaying self-organization in the domain of manufacturing. Based on a corpus of 84 reference papers, we find that multiagent systems (MAS) play a significant role in self-organization, especially MAS featuring bio-inspired algorithms for agent coordination. The emergence of new classes of cyber-physical systems further strengthens the prevalence of MAS on the subject. As outcome of our review, we devise the MOSAIK model, a generic model synthesizing all system architectures found in our corpus. The MOSAIK model can be used as a reference for formally comparing distinct architectures. We also use it to identify gaps for future research on self-organizing manufacturing systems. The model includes the central concepts of Agent and Artifact, which suggest that the Web is an adequate communication infrastructure for modern manufacturing systems: Agents become (autonomous) Web Agents and Artifacts become resources exposed by Web servers.