Intelligent Manufacturing Environment Research Articles

Applications and Challenges of Fieldbus Technology in Intelligent Manufacturing Systems

Abstract This study advances the field of intelligent manufacturing by integrating automation, information technology, and digitalization into a novel “five horizontal and three vertical” system architecture. We introduce an enhanced model-free adaptive control (MFAC) algorithm that utilizes a dynamic linearization method alongside pseudo-partial derivatives (PPD) for optimizing data transfer within intelligent manufacturing systems. Using CANoe software for simulation, our research demonstrates the improved MFAC algorithm’s superior bus message transmission efficiency by 0.0224 to 0.026 and a significant reduction in the network load factor by 0.2068. These advancements highlight the efficacy of fieldbus technology in reducing network congestion and enhancing real-time performance in intelligent manufacturing environments.

Non-contact online inspection method for the appearance diameter uniformity of chemical fiber filaments

As chemical fiber filaments diameter variation directly affects spinning quality, spinning condition monitoring becomes more and more important in an intelligent manufacturing environment. Firstly, based on the single-slit Fraunhofer diffraction model and the Babinet principle, the non-contact filaments diffraction model is developed to generate diffraction fringe patterns by irradiating chemical fiber filaments with He–Ne laser. Secondly, for the characteristics of diffraction fringe patterns, a segmentation mask window-based diffraction fringe center of gravity extraction method is proposed, and a zero-phase Butterworth low-pass filter is designed and applied to the diffraction light intensity curves to accurately extract the extreme points positions of the adjacent dark fringe and calculate the diameter information. Finally, the non-contact chemical fiber filaments appearance diameter online inspection experiment platform is built to carry out static and dynamic experiments of filaments. The experimental results show that the maximum absolute value of absolute error is less than 1.1 μm for static measurement and less than 10 μm for dynamic measurement.

Integrated optimization of process planning and scheduling problems based on complex networks

Industrial information integration can promote modern manufacturing enterprises to achieve efficient information processing and data integration in manufacturing systems, providing a good framework for solving process planning and workshop scheduling integration problems. To explore optimization issues in an intelligent manufacturing environment, find a better way to solve the integration of process planning and scheduling, and analyze basic features of manufacturing task-driven workshop scheduling network. The paper constructs a multi-process relational network model based on processes and machines in accordance with the complex network theory. Moreover, this paper proposes a multi-objective integrated model that minimizes maximum makespan, minimum machine load, and minimum machine energy consumption with the constraints of the workpiece, process, and machine. This paper uses the NSGA-II algorithm strategy to analyze the proposed model and issue. The feasibility of the model and algorithm proposed is demonstrated by solving various optimization objectives in actual workshop manufacturing tasks. Then, it analyses and evaluates the network structure characteristic index of the production and manufacturing scheduling process, which provides a decision basis for the task processing process design and scheduling planning in the intelligent manufacturing.

Dynamic Process Modeling and Real-Time Performance Evaluation of Rework Production System With Small-Lot Order

Rework production system is a typical structure in practical manufacturing, where the defective parts detected by the inspection machine are sent to rework rather than being directly scrapped. Due to the special structure, more complex coupling phenomena between machines and buffers are introduced, especially when machines are unreliable and buffers are finite. In addition, under the intelligent manufacturing environment, customized demands trigger small-lot orders become a common occurrence, which lead to traditional steady-state analysis inapplicable. Therefore in this paper, we study dynamic process modeling and real-time performance evaluation problems of the rework system. Practical issues, such as unreliable machines, finite buffers, and small-lot orders are taken into account. Specifically, a novel dynamic decomposition and aggregation algorithm is proposed. The proposed method is validated to have a precise performance measures prediction effect, as well as an efficient computational performance.

Distributed shop scheduling: A comprehensive review on classifications, models and algorithms.

In the intelligent manufacturing environment, modern industry is developing at a faster pace, and there is an urgent need for reasonable production scheduling to ensure an organized production order and a dependable production guarantee for enterprises. Additionally, production cooperation between enterprises and different branches of enterprises is increasingly common, and distributed manufacturing has become a prevalent production model. In light of these developments, this paper presents the research background and current state of distributed shop scheduling. It summarizes relevant research on issues that align with the new manufacturing model, explores hot topics and concerns and focuses on the classification of distributed parallel machine scheduling, distributed flow shop scheduling, distributed job shop scheduling and distributed assembly shop scheduling. The paper investigates these scheduling problems in terms of single-objective and multi-objective optimization, as well as processing constraints. It also summarizes the relevant optimization algorithms and their limitations. It also provides an overview of research methods and objects, highlighting the development of solution methods and research trends for new problems. Finally, the paper analyzes future research directions in this field.

Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.

Remote Diagnosis and Detection Technology for Electrical Control of Intelligent Manufacturing CNC Machine Tools

An intelligent manufacturing environment employs internet-based communication and monitoring technologies for fault detection, diagnosis, and monitoring of industrial machines. The monitoring and fault detection are performed remotely without human intervention that predicts faults and ensures specific operational control. This article introduces a rational fault diagnosis process (RFDP) best suited for remote fault detection and diagnosis of CNC machine tools. The proposed process monitors different operational segments of the machine and extracts related data to validate its performance. The interconnection between the segments and fault impact are identified using the transfer learning process. The previously identified faults are used in the state training process to improve detection and diagnosis accuracy. Depending on the operational control continuity, the performance is assessed post the fault diagnosis. The learning paradigm is trained using the machine’s efficiency and rational data processing to predict the transfer states’ faults. The transfer states are modulated based on the efficiency and minimum-maximum control recommended for the CNC machine. This process’s performance is validated using detection accuracy, diagnosis recommendation, downtime, data processing rate, and processing time. From the experimental analysis, it is seen that for the varying data extraction rates, the proposed process improves detection accuracy by 10.14%, diagnosis recommendation by 8.58% and data processing rate by 7.95%, reducing the downtime by 8.85%, and processing by 11.24%.

Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment

In the interest of developing an intelligent manufacturing environment with an agile, efficient, and optimally utilized transportation system, mobile robots need to achieve a certain level of autonomy as they play an important role in carrying out transportation tasks. Bearing this in mind, in the paper we propose a novel stereo visual servoing method for nonholonomic mobile robot control based on semantic segmentation. Semantic segmentation provides a rich body of information required for an adequate decision-making process in a clustered, dynamic, and ever-changing manufacturing environment. The innovative idea behind the new visual servoing system is to utilize semantic information of the scene for visual servoing, as well as for other mobile robot tasks, such as obstacle avoidance, scene understanding, and simultaneous localization and mapping. Semantic segmentation is carried out by exploiting fully convolutional neural networks. The new visual servoing algorithm utilizes an intensity-based image registration procedure, which results in the image transformation matrix. The transformation matrix encompasses the relations of images taken at the current and desired pose, and that information is directly used for visual servoing. The developed algorithm is deployed on our own developed wheeled differential drive mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental evaluation is carried out in the 3D simulation environment and in the laboratory model of the real manufacturing environment. The experimental results show that the accuracy of the proposed approach is improved when compared to the state-of-the-art approaches while being robust to the partial occlusions of the scene and illumination changes.

A study on the algorithm of ultrasonic detection and recognition based on DAG‐SVMs mixed HMM of teleoperation gestures for intelligent manufacturing devices

Remote control for the position and status of a machine or an equipment can often be teleoperated by gestures in an intelligent manufacturing environment. In order to solve the problems that gestures with two directions such as left and right cannot be detected by single ultrasonic frequency, double different ultrasonic frequencies are used to detect gestures by the Doppler shift, and an algorithm of the recognition gesture based on the DAG-SVMs mixed Hidden Markov Model (HMM) is proposed to identify and classify the extracted feature sequences. Thus, four more types of gestures are expanded other than that of reading display screen information, and the comparative experiments to classify and recognise gestures of teleoperation are made with DAG-SVMs, the HMM, the DAG-SVMs mixed HMM, and other improved HMM algorithms. The test results have shown that the mean rate of gesture recognition for the algorithm based on the DAG-SVMs mixed HMM is 94.917%, which is 9.497% higher than that of the unimproved HMM, and its recognition accuracy of complex teleoperation gestures is improved by 2.3% compared with other improved HMM algorithms. The experimental results show that the DAG-SVMs mixed HMM algorithm has a good effect on recognition for the gestures of teleoperation and it can perform gesture recognition accurately.

A Modified Approach for Six Sigma Quality Assessment of Product with Multiple Characteristics in Intelligent Manufacturing Environments

Abstract Process quality and yield are always crucial determinants of a firm’s competitiveness; therefore, monitoring and assessment for process quality are vital aspects of their sustainable development. The Six Sigma quality control is based on defect rate and nonconforming units to eliminate factors that cause process defects. Thus, it has become a powerful instrument for manufacturers to achieve the quality goals of only 3.4 defects per million opportunities. However, the originated Six Sigma program is based on the 1.5σ shift in the process mean to recognize the tendency of processes to shift over the long term and achieve this goal. With the production environment of intelligent manufacturing today, manufacturers keep up to date with production performance and then monitor the distance between the process mean shift and the target value at all times. This makes it possible to adopt a more rigorous standard by decreasing in allowable shift to define the quality level in Six Sigma. For this reason, this paper proposes the redefined Six Sigma quality indexes as an assessment tool and has developed a modified approach for Six Sigma quality assessment of products with multiple characteristics. The proposed method uses more rigorous standards to analyze process quality, thereby allowing manufacturers to provide industry partners or consumers with products of higher quality, meet their needs, and even enhance their own industry competitiveness to continue progressing toward sustainability. We present a real-world example to demonstrate the practical applicability of the proposed approach.

Cloud manufacturing architecture: a critical analysis of its development, characteristics and future agenda to support its adoption

PurposeIn the last decade, cloud manufacturing (CMfg) has attracted considerable attention from academia and industry worldwide. It is widely accepted that the design and analysis of cloud manufacturing architecture (CMfg-A) are the basis for developing and applying CMfg systems. However, in existing studies, analysis of the status, development process and internal characteristics of CMfg-A is lacking, hindering an understanding of the research hotspots and development trends of CMfg-A. Meanwhile, effective guidance is lacking on the construction of superior CMfg-As. The purpose of this paper is to review the relevant research on CMfg-A via identification of the main layers, elements, relationships, structure and functions of CMfg-A to provide valuable information to scholars and practitioners for further research on key CMfg-A technologies and the construction of CMfg systems with superior performance.Design/methodology/approachThis study systematically reviews the relevant research on CMfg-A across transformation process to internal characteristics by integrating quantitative and qualitative methods. First, the split and reorganization method is used to recognize the main layers of CMfg-A. Then, the transformation process of six main layers is analysed through retrospective analysis, and the similarities and differences in CMfg-A are obtained. Subsequently, based on systematic theory, the elements, relationships, structure and functions of CMfg-A are inductively studied. A 3D printing architecture design case is conducted to discuss the weakness of the previous architecture and demonstrate how to improve it. Finally, the primary current trends and future opportunities are presented.FindingsBy analyzing the transformation process of CMfg-A, this study finds that CMfg-A resources are developing from tangible resources into intangible resources and intelligent resources. CMfg-A technology is developing from traditional cloud computing-based technology towards advanced manufacturing technology, and CMfg-A application scope is gradually expanding from traditional manufacturing industry to emerging manufacturing industry. In addition, by analyzing the elements, relationships, structure and functions of CMfg-A, this study finds that CMfg-A is undergoing a new generation of transformation, with trends of integrated development, intelligent development, innovative development and green development. Case study shows that the analysis of the development trend and internal characteristics of the architecture facilitates the design of a more effective architecture.Research limitations/implicationsThis paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. The reason for considering Chinese articles is that CMfg was proposed by the Chinese and a lot of Chinese CMfg-A articles have been published in recent years. CMfg is suitable for the development of China’s manufacturing industry because of China’s intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg-A.Originality/valuePrior studies ignore the identification and analysis of development process and internal characteristics for the current development of CMfg-A, including the main layers identification of different CMfg-As and the transformation process analysis of these main layers, and in-depth analysis of the inner essence of CMfg-A (such as its elements, relationships, structure and functions). This study addresses these limitations and provides a comprehensive literature review.

Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

To realize intelligent manufacturing, a controllable factory must be built, and manufacturing competitiveness must be achieved through the improvement of product quality and yield. The yield in the micromanufacturing process is gaining importance as a management factor used in deciding the production cost and product quality as product functions becomes more sophisticated. Because the micromanufacturing process involves manufacturing products through multiple steps, it is difficult to determine the process or equipment that has encountered failure, which can lead to difficulty in securing high yields. This study presents a structural model for building a factory integration system to analyze big data at manufacturing sites and a hierarchical factor analysis methodology to increase product yield and quality in an intelligent manufacturing environment. To improve the product yield, it is necessary to analyze the fault factors that cause low yields and locate and manage the critical processes and equipment factors that affect these fault factors. However, yield management is a difficult problem because there exists a correlation between equipment, and in the sequence of process equipment that the lot passed through, the downstream and the upstream cause complex faults. This study used data-mining techniques to identify suspected processes and equipment that affect the yield of products in the manufacturing process and to analyze the key factors of the equipment. Ultimately, we propose a methodology to find the key factors of the suspected process and equipment that directly affect the implementation of the intelligent manufacturing scheme and the yield of the product. To verify the effect of key parameters of critical processes and equipment on the yield, the proposed methodology was applied to actual manufacturing sites.

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

l The resource scheduling model of intelligent manufacturing workshop is established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. l The NSGA-Ⅱ algorithm is improved, and the evaluation function is established based on crowding ranking and ranking level. l the competition mechanism is introduced, the elitist retention strategy is improved, the probability is determined by variable proportion method, and the optimal solution is determined by AHP. With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

Application of Fuzzy Analytic Hierarchy Process in Intelligent Manufacturing Resources Allocation Evaluation System of Alliance Enterprises

With the increasing global competition, more and more enterprises form the industrial alliance. In order to solve the problem of resource allocation and scheduling optimization of industrial alliance enterprises in intelligent manufacturing environment, an efficient resource allocation method and evaluation index of industrial alliance service platform are established. A set of systematic intelligent manufacturing resources evaluation scheme is established by using FAHP, which can be used to evaluate and analyze several manufacturing resources conveniently. According to different needs of customers, the weight assignment is also changing, and the most suitable manufacturing resources can be obtained.

Research on Evaluation System of Alliance Enterprises in Intelligent Manufacturing Environment Based on BP Neural Network

In an intelligent manufacturing environment, fierce global market competition has made more and more companies form industrial alliances to gain an advantage. The basic goal of enterprise alliance is achieving high efficiency and a win-win situation. This paper mainly studies the necessity of partner selection, determines the principle of partner selection, and establishes an evaluation index system for dynamic alliance enterprise cooperation. At the same time, multi-attribute decision analysis and BP neural network are used to identify the ultimate partner. Finally, an example is analyzed with MATLAB to verify its feasibility.

Theory, supporting technology and application analysis of cloud manufacturing: a systematic and comprehensive literature review

PurposeCloud manufacturing (CMfg) is a networked manufacturing mode that promotes the agile, service-oriented, green and intelligent development of the manufacturing industry. Although some scholars have reviewed related studies of CMfg from multiple perspectives, these reviews are not fully systematic or well justified and fail to fully reveal the key characteristics in the development process of CMfg. The purpose of this paper is to systematically review the relevant research on CMfg via identification of key characteristics of definition, architecture, supporting technology and application of CMfg to provide critical information in decision support for the innovation and development of CMfg.Design/methodology/approachThis study systematically reviews the relevant research on CMfg across theoretical methods to technical applications by integrating quantitative and qualitative methods. Word cloud method is used to quantitatively analyse the structure and feature of different definitions of CMfg. The principle of System Science is used to explore the basic components and functions of various CMfg architectures and their common and differing characteristics. A multi-level technology framework is developed to explore the development status of CMfg supporting technologies. A multi-stage application classification is proposed to reveal the application status of CMfg.FindingsThrough literature review, this study found that CMfg architecture is currently dominated by general architectures and lacks architectures that fit the actual enterprise characteristics; CMfg supporting technology is mature in the traditional cloud computing-based technology, but it is still weak in the development of virtualization and servitization technology, service scheduling technology; CMfg application is still in the initial stage and still lacks a relatively complete system application. By analysing the development status of CMfg, this study also identified potential research directions of CMfg in information management, service composition and evaluation, system application and sustainable development and other aspects.Research limitations/implicationsThis paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. Chinese articles account for more than half of the total. The reason is that CMfg was proposed by the Chinese and CMfg is suitable for the development of China's manufacturing industry because of China's intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg.Originality/valuePrior literature reviews ignore the identification and analysis of key feature identification for the current development of CMfg, including common and unique feature identification of different CMfg architectures and functions, multi-layer analysis and interpretation of CMfg technology and different stage analysis of CMfg applications. This study addresses these limitations and provides a comprehensive literature review.

User Behavior Analysis by Cross-Domain Log Data Fusion

Modern mass customization production allows user interaction activities to be distributed in the full product life cycle via multiple information systems. Investigating user behaviors across the boundaries of different domains helps to deeply integrate isolated fragmental profiles into a comprehensive one, and therefore can provide multi-dimensional, high-quality and valuable services. However, traditional user behavior analysis models are based on individual user profile information derived from separate domains, such as requirement analysis, design, supply chain, logistics, marketing, etc., which have not considered the whole complexity of mass customization manufacturing. In this paper, we introduce the concept of multi-dimensional semantic activity space, where user behavior features are merged and represented as combined vectors. User behavior patterns are discovered by mining action data extracted from log files in different subsystems in the corresponding domains. We also identify distinct categories of user behaviors in various modules and subsystems in the context of an intelligent manufacturing environment. Experiment results show a strong indication that the proposed approach can be applied to reveal variations in typical behavioral aspects of cross-domain participants, in terms of patterns in resource access, operation tasks, performance assessment, etc.

Remote laboratory design and implementation as a measurement and automation experiential learning opportunity

Designing and implementing a remote laboratory has long been a professional job, carried out by experts working in synergy with teachers. Nowadays, thanks to the availability of advanced tools, the implementation can be assigned as a student project related to advanced measurement and automation classes. The development of the project happens to have an important educational validity, since it allows the student to get familiar with principles and technologies that characterize intelligent manufacturing and smart environments, as central elements of Industry 4.0. In particular, the student has the opportunity to increase knowledge of Internet technologies that more and more often are substituting for the proprietary solutions, previously adopted in the industry for process monitoring and automation.

Quality of service optimization in wireless transmission of industrial Internet of Things for intelligent manufacturing

Based on the requirements of industrial Internet of Things in intelligent manufacturing environment, the layered architecture, cross-layer optimization protocol, and key technologies of universal converter for heterogeneous networks of industrial Internet of Things are analyzed. A new architecture of two-layer distributed hybrid industrial Internet of Things is proposed, which combines the advantages of star network and mesh network. In the orthogonal frequency division multiple access (OFDMA)–based relay system, in order to satisfy the quality of service (QoS) requirement and ensure the maximum throughput of the system while ensuring user fairness, a resource allocation algorithm oriented to quality of service (QoS) is proposed. Firstly, according to the waiting delay of the user in the queue and the requirement of the user for the rate, the delay priority factor and the rate priority factor are introduced to calculate the priority of the user, and then, the dynamic resource scheduling and allocation are carried out on the backhaul link and the access link respectively. The simulation results show that the new algorithm has low packet loss rate, better meets GBR requirements, and can achieve high system throughput and fairness.

INTERNET OF THINGS IN THE CONTEXT OF INDUSTRY 4.0: AN OVERVIEW

Due to successive technological advancements, developments and innovations, the global industrial landscape has drastically transformed over the last years. The fourth industrial revolution (Industry 4.0) aims at transforming traditional industries into intelligent ones by incorporating innovative technologies. Industry 4.0 enables physical assets to be integrated into intertwined digital and physical processes thus creating smart factories and intelligent manufacturing environments. Internet of Things (IoT) is a rapidly growing technology that has drastically contributed to the Industry 4.0 realization. IoT pursues to pervade our everyday environment and its objects, linking the physical to the digital world and allowing people and “things” to be connected anytime, anywhere, with anything and anyone ideally using any network and service. IoT is regarded as a dynamic and global network of interconnected “things” uniquely addressable, based on standard and interoperable communication protocols and with self-configuring capabilities. Despite still being at an early development, adoption and implementation stage, Industry 4.0 and IoT can provide a multitude of contemporary solutions, applications and services. Hence, they can improve life quality and yield significant personal, professional and economic opportunities and benefits in the near future. This study scrutinizes IoT in the Industry 4.0 context. More specifically, it presents related studies, describes the IoT concept and explores some of the numerous IoT application domains. Moreover, it presents and analyzes the concept of Industry 4.0 and the benefits it offers as well as the relevant key technologies (e.g. industrial internet of things (IIoT), cyber-physical systems (CPSs), cloud computing, big data and advanced data analytics). Furthermore, it describes the concept of intelligent manufacturing and highlights the main IoT and Industry 4.0 challenges and open research issues. Finally, the need for innovation in the industrial domain and the impact and benefits that IoT and Industry 4.0 provide to everyday life and industries is described.