Intelligent Manufacturing Applications Research Articles

A Digital Twin Platform Integrating Process Parameter Simulation Solution for Intelligent Manufacturing

The present work aims to develop a digital twin system typical of intelligent manufacturing applications, which has integrated visualization technologies, as well as the process parameter simulation solution. The application under consideration is a typical machining process, with a gantry machine tool controlled by Siemens Programmable Logic Controller(PLC) S7-1200. With the establishment of dual-directional data communication between the physical machine tool and its virtual counterpart based on TCP/IP protocol, real-time visualization, monitoring, and control of the entire working process can be achieved. Furthermore, we integrated with the digital twin system as a solution for real-time process parameter simulation based on finite element modeling (FEM), which enables the real-time monitoring of necessary process parameters, e.g., surface deformation, during the machining process. A preliminary experiment was conducted to validate our proposed digital twin system, and the results demonstrated that our proposed method has satisfactory performance in terms of both control and monitoring of the traditional machining process, and synchronization between the physical and virtual models is also proven to be positive with minimal latency.

A machining error tracing method based on MEA-BP neural network for quality improvement of gear hubs

ABSTRACT The machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.

Post Weld Heat Treatment Optimization of Dissimilar Friction Stir Welded AA2024-T3 and AA7075-T651 Using Machine Learning and Metaheuristics.

Post weld heat treatment, or PWHT, is often used to improve the mechanical properties of materials that have been welded. Several publications have investigated the effects of the PWHT process using experimental designs. However, the modeling and optimization using the integration of machine learning (ML) and metaheuristics have yet to be reported, which are fundamental steps toward intelligent manufacturing applications. This research proposes a novel approach using ML techniques and metaheuristics to optimize PWHT process parameters. The goal is to determine the optimal PWHT parameters for both single and multiple objective perspectives. In this research, support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) were ML techniques employed to obtain a relationship model between PWHT parameters and mechanical properties: ultimate tensile strength (UTS) and elongation percentage (EL). The results show that the SVR demonstrated superior performance among ML techniques for both UTS and EL models. Then, SVR is used with metaheuristics such as differential evolution (DE), particle swarm optimization (PSO), and genetic algorithms (GA). SVR-PSO shows the fastest convergence among other combinations. The final solutions of single-objective and Pareto solutions were also suggested in this research.

Corporate digitalization, application modes, and green growth: Evidence from the innovation of Chinese listed companies

Digitalization is one of the main ways for enterprise growth in the digital economy era. However, the existing literature on digital technology application models and their impact on corporate green growth is rare. By using the green innovation data of Chinese A-share listed companies from 2008 to 2020, this paper empirically investigates the association between enterprise digitalization and green innovation. The empirical results show that digitalization has significantly improved enterprises’ substantive green innovation level, which is valid after conducting a series of endogenous and robustness tests. Further results show that digital technology application, intelligent manufacturing application, and modern information system application are the three main models of digitalization to promote green innovation of enterprises, while internet business model application cannot significantly promote corporate green innovation. In addition, the mechanism analysis results indicate that the increase in government subsidy and corporate own R&D investment contribute to the incentive effect mentioned above, while the loss of governance efficiency and fluctuation of the external environment offset this effect. This incentive effect is more obvious in non-state-owned, high-tech, and lower-polluted industry enterprises. Our paper reveals the mode and mechanisms for enterprises to realize innovative green growth by applying digital technology in the digital economy era, which is of great significance to relevant theoretical research and policy formulation.

Design and Research of Teaching and Training Platform Based on Industrial Internet and Intelligent Manufacturing

Industrial internet and intelligent manufacturing application training platform is a comprehensive teaching and training platform for industrial internet, which contains a complete industrial internet architecture, including: sensing equipment, actuator, wireless sensor network, wired sensor network, industrial master control organization, industrial Internet cloud platform, industrial big data analysis platform. The industrial internet application technology training platform uses emerging technologies such as industrial internet, embedded, big data, mobile application, artificial intelligence, intelligent manufacturing, and built-in industrial internet cloud platform to provide teaching, practical training and scientific research support for the upgrading and reform of relevant disciplines in vocational colleges. The industrial internet and intelligent manufacturing training platform provides hardware and software resources with three layers of architecture, including equipment layer, platform layer and application layer, providing supporting teaching and practical training resources for related teaching. Industrial internet application technology training platform can meet the needs of industrial Internet teaching and training courses, including: "Industrial Ethernet and Network Equipment", "Network Security Technology", "Industrial Internet of Things", "Industrial Automation Control", "Network System Software Design", "Machine Vision Technology", "Manufacturing Execution System (MES) Application Technology" and so on.

In-situ manifestation of lapping mechanisms by rapid intelligent pattern recognition analysis (RIPRA) of acoustic emission via a point density fuzzy C-means (PD-FCM) method

Rapid pattern-recognition capabilities are very challenging tasks to fulfill while essential steps for the ultimate mission of achieving in-time security response, hazardous disaster prevention, structural failure prediction, and intelligent manufacturing applications. Mechanistic manifestation of a machining process through pattern recognition is even more daunting since one has to gain full understanding at the very fundamental level of the manufacturing processes, but it would be the most beneficial should it be accomplished. We designed and built an in-situ Acoustic Emission (AE) monitoring system, and successfully applied this newly developed AE system to manifest the lapping removal mechanisms of a novel fixed abrasive pad embedded with agglomerated diamond (AD) abrasives. To successfully extract valuable mechanistic information, the time-frequency spectrum of collected AE signals in a sapphire lapping process was decomposed by Wavelet Packet Transform (WPT) into three characteristically patterned bands, each representing rubbing, ploughing and cutting action in the lapping process correspondingly. We creatively applied Point Density Fuzzy C-Means (PD-FCM) algorithm for self-identifying clustering centers to classify contributions from rubbing, ploughing and cutting actions in the lapping process. By rapid intelligent pattern recognition analysis (RIPRA) of AE signals, the lapping removal mechanisms using fixed abrasive pads were clearly identified and quantitatively monitored in-situ throughout the whole lapping processes. Such a capable approach implicates a great potential in a wide range of ultra-precision manufacturing applications.

Relationship Extraction and Processing for Knowledge Graph of Welding Manufacturing

Acquiring welding domain relationships and forming a knowledge graph can positively impact complex engineering problem solving and intelligent manufacturing applications. However, relationships are lacking in the welding domain. The relationship extraction and processing solution are designed to handle data with different characteristics in welding fabrication. The BiLSTM+Attention and CR-CNN models are employed to extract relations in unstructured documents. The neighborhood rough set-based association rule model is proposed for project-specific documents to accomplish relationship acquisition, in which invalid attributes are removed via neighborhood rough sets and attribute values are related via association rules. In addition, the knowledge graph is built based on extracted relationships, and unique empirical relationships are handled by introducing relational nodes and databases. The results show that BiLSTM+Attention gets a good score with Macro-average metrics (0.788 for Precision, 0.846 for Recall, and 0.816 for F1-score). The relational rules obtained via the proposed model are consistent with the production experience. The constructed knowledge graph effectively handles empirical relationships while positively impacting knowledge retrieval, intelligent question and answer, and decision-making for complex engineering problems.

Analysis of the application of industrial robots in intelligent manufacturing

In the context of the new era, industrial robots play an increasingly important role in the field of intelligent manufacturing, which not only boosts the high-quality development of China's economy, but also helps promote the optimization and upgrading of industrial structure and enhance the momentum of economic development. At present, industrial robots are widely used in intelligent manufacturing in welding operation, automobile manufacturing, automatic assembly and other aspects, and have formed a certain industrial scale and economic effect. Therefore, this article mainly from the industrial robot in the field of intelligent manufacturing development present situation, analyzes the industrial robot in the field of intelligent manufacturing applications, and put forward from the stick to establish and improve the personnel training mechanism, vigorously strengthen the technological innovation capability and upgrading product application performance and so on three aspects to explore the optimum path of industrial robots in the field of intelligent manufacturing, In order to promote the long-term development of industrial robots from the perspective of intelligent manufacturing in the future.

Design and simulation of magnetic adsorption System for 3D curved surface mobile Robot

With the deepening of intelligent manufacturing applications, 3D curved surface mobile robots need to be developed to meet more and more practical engineering requirements. And the adsorption system is the key part of 3D curved surface mobile robot to overcome the gravity of the robot itself. Firstly, the relationship between adsorption force and inclination angle was calculated on the basis of mechanical characteristics. Secondly, we simulated the magnetic adsorption situation and acquired the relation curves of the adsorption force. Finally, the structure size and adjustment scheme of the magnetic adsorption system were determined according to the simulation results.

State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression.

Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re-examine the research of the equipment-state-prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.

The diffusion of intelligent manufacturing applications based SIR model

At present, manufacturing industry is obviously experiencing the rapid development of intelligent manufacturing on a global scale. To meet the needs in the new era, China has continuously been introducing new measures to develop smart manufacturing,

Data-driven ontology generation and evolution towards intelligent service in manufacturing systems

Abstract To support intelligent manufacturing, providing a unified production data view by integrating distributed data collected by different enterprise information systems is critical. Because various information systems are often heterogeneous, ontology is widely adopted to present a global reference view for data integration. However, construction and maintenance of these ontologies is difficult because of the heterogeneity and dynamism of these large-scale data. In this paper, with the objective of intelligent manufacturing application implementation, we propose a comprehensive ontology generation and evolution method that automatically abstracts ontology from raw production data and dynamically adjusts the ontology in accordance with changes in the manufacturing data environment. The proposed method comprises four phases: data extraction, ontology construction, ontology connection, and ontology evolution. In the first phase, data from different sources are mapped to data entities to form a unified data structure. In the second phase, an initial ontology is generated via instance-driven ontology construction. In the third phase, to support intelligent manufacturing, the initial ontologies are organised in terms of the dimensions of the various business elements, such as stuff, machine, product, process, and scenarios. In the fourth phase, rules regarding ontology restrictions are formulated to realise ontology evolution that respond to changes in the manufacturing environment. To verify the efficacy of the proposed method, a prototype was implemented with real data from a manufacturing factory, in which the constructed ontology was used as the metadata of product data in intelligent manufacturing.

Performance analysis and evaluation of direct phase measuring deflectometry

Three-dimensional (3D) shape measurement of specular objects plays an important role in intelligent manufacturing applications. Phase measuring deflectometry (PMD)-based methods are widely used to obtain the 3D shapes of specular surfaces because they offer the advantages of a large dynamic range, high measurement accuracy, full-field and noncontact operation, and automatic data processing. To enable measurement of specular objects with discontinuous and/or isolated surfaces, a direct PMD (DPMD) method has been developed to build a direct relationship between phase and depth. In this paper, a new virtual measurement system is presented and is used to optimize the system parameters and evaluate the system's performance in DPMD applications. Four system parameters are analyzed to obtain accurate measurement results. Experiments are performed using simulated and actual data and the results confirm the effects of these four parameters on the measurement results. Researchers can therefore select suitable system parameters for actual DPMD (including PMD) measurement systems to obtain the 3D shapes of specular objects with high accuracy.

Applications of artificial intelligence in intelligent manufacturing: a review

Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent manufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.

Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks

Information extracting method from numerous measured signals is a critical technique for intelligent manufacturing application to further reduce the manpower cost and improve the productivity and workpiece quality. Manually defining signal features,as the common way, unfortunately will lose most of the information and the performance can’t be guaranteed. In the past few years, machine learning method with deep structure has been the most promising automatic feature extracting method which has made great breakthrough in computer vision and automatic speech recognition. In this paper, deep belief networksareemployed using vibration signal obtained from endmilling to build feature space for cutting states monitoring. Greedy layer-wise strategy is adopted to pre-train the network and standard samples are used for fine-tuning by applying back-propagation method. Comparisons are made with several manually defined features both in time and frequency domain, like MFCC and wavelet method. Different modeling methods are also employed in the research forcomparisons. Resultsshow that the deep learning method has similar ability to characterize the signal for cutting states monitoring compared to those manually defined features. And the modeling accuracy ismuch better thanother traditionalmodeling methods.Furthermore, benefitting fromthe potentialcapability in information fusion, deep learning method would be a promising solution for more complex applications, like tool wear monitoring, machining surface prediction et al.

Meta knowledge of intelligent manufacturing: An overview of state-of-the-art

This paper tries to stimulate empirical research into the overall impacts of intelligent systems in manufacturing aspects. To reach this goal, a schema of intelligent applications is provided for each aspect as frame base structure, meaning the knowledge of intelligent applications in that specific aspect. Then, a semantic network is developed for intelligent manufacturing based on hierarchical structure of manufacturing systems to provide Meta knowledge of intelligent manufacturing applications. The analysis of semantic network indicates an increasing growth in application of soft computing in manufacturing aspects. Specially, fuzzy logic and its derivates have found more applications in recent years.

Multi-agent Mediator architecture for distributed manufacturing

A generic Mediator architecture for distributed task planning and coordination has been developed using multi-agent paradigms. In this approach, agents function autonomously as independent computing processes, and dynamic virtual clusters coordinate the agent's activities and decision making. This coordination involves dynamically created coordination agents and resource agents concurrently. The Mediator architecture contains three levels of these coordination agents: the template mediator, the data-agent manager, and the active mediator. The template mediator is the top-level global coordinator. This agent contains both the templates and the cloning mechanism to create the successively lower-level agents. Task plans are decomposed successively into subtasks, which are allocated to dynamically created agent clusters coordinated through data-agent managers and active mediators. Coordination of agent activity takes place both among the clusters and within each cluster. The system dynamically adapts to evolving manufacturing tasks, with virtual agent clusters being created as needed, and destroyed when their tasks are completed. The mediator architecture and related mechanisms are demonstrated using an intelligent manufacturing scheduling application. Both the machines and the parts involved in this production system are considered as intelligent agents. These agents use a common language protocol based on the Knowledge Query Manipulation Language (KQML). The generic Mediator approach can be used for other distributed organizational systems beyond the intelligent manufacturing application it was originally developed for.

Combined Structure and Parameter Learning for Intelligent Manufacturing Applications

Integrated use of the neural and fuzzy techniques in cutting tool monitoring is illustrated in the paper. The results are compared with ANN and previous neuro-fuzzy (NF) approaches. The paper shows that the NF technique can comply with the fundamental requirements of intelligent manufacturing, i.e. real-time nature, uncertainty handling and learning abilities, with the additional benefits of managing both symbolic and numeric information, hybrid learning, and a kind of explanation facility. Finally, the integration of such a hybrid system in an intelligent manufacturing environment is investigated.