Manufacturing Big Data Research Articles

An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops

With the development of sensing and communications technology, some new features have emerged in manufacturing processes, such as highly correlated, deeply integrated, dynamically integrated, and a huge volume of data. There is a strong need to deeply excavate information from manufacturing Big Data, especially the energy consumption data, for energy-efficient manufacturing operations management and analysis. However, relevant data reduction and association analysis to support energy-efficient manufacturing are still ineffective and error-prone, especially for discrete manufacturing workshops. In this paper, an energy-aware Cyber Physical System (E-CPS) is proposed for energy Big Data analysis and recessive production anomalies detection. Firstly, E-CPS is introduced to acquire manufacturing Big Data. Then, a Big Data analysis method, including data reduction and data association analysis, is proposed to analyse the manufacturing data in the E-CPS. Considering the complexity and dynamics of manufacturing processes, an energy Big Data-driven recessive production anomalies analysis method is proposed based on deep belief networks. The proposed method in this paper realises the integrated utilisation of production Big Data and energy Big Data in the E-CPS. Further, the efficiency evaluation and recessive anomalies detection methods can be used in existing production information systems.

Key technologies and applications of industrial big data in manufacturing of Chinese medicine

Along with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.

Manufacturing big data ecosystem: A systematic literature review

Abstract Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.

A method of NC machine tools intelligent monitoring system in smart factories

Abstract The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.

A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance

Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.

Feature space monitoring for smart manufacturing via statistics pattern analysis

Statistical process monitoring (SPM) is an important component in the long-term reliable operation of any system and its importance can only become greater in the era of smart manufacturing (SM). Previously we proposed statistics pattern analysis (SPA) based on the idea of using various statistics to quantify process characteristics, and monitoring these statistics instead of process variables themselves to perform process monitoring. In this work we provide a comprehensive review on recent progresses made in SPA framework, which underpins a roadmap of SPM we outlined recently. Both sample-wise feature extraction and variable-wise feature extraction are discussed, with new applications in both fault detection and diagnosis, and soft sensor development. Specifically, we provide the first systematic examination on the SPA's capability in handling process characteristics including dynamics, nonlinearity and data non-Gaussianity; and compare its performance to representative state-of-the-art SPM methods to highlight the enhanced capability of feature-based monitoring. In addition, the performance of SPA is tested using the benchmark industrial simulator TEP for fault detection and diagnosis, plus a wet lab and an industrial case studies for soft sensor development. Finally, the advantages and potential limitations of SPA in addressing the new challenges presented by smart manufacturing big data are discussed.

Cyber Physical Energy System for Saving Energy of the Dyeing Process with Industrial Internet of Things and Manufacturing Big Data

The manufacturing industry has recently been focusing on improving energy efficiency to reduce greenhouse gas emissions and achieve sustainable growth. The focus is on combining existing energy technologies with new information and communication technologies as the Fourth Industrial Revolution approaches. Dyeing and finishing shops use the most energy in the textile industry and have below-average energy efficiency because of low technology and facility investment. Research into increasing the energy efficiency of dyeing and finishing shops has concentrated on developing equipment; however, it is difficult for small- and medium-sized factories to benefit from these advances. Thus, research into means of improving energy efficiency by improving the process and system efficiency of dyeing and finishing companies, who have difficulties with facility investment and operation, is necessary. In this study, a cyber physical energy system that improves the energy efficiency of the dyeing process by collecting and analyzing manufacturing big data was developed. Further, the implemented system was applied to actual dyeing and finishing shops, and its effects were verified. This research contributes to improving the situation of the dyeing process using machine learning techniques and manufacturing big data by adjusting cyber physical energy systems without utilizing expensive equipment. Inaccurate process instruction from the laboratory are also replaced by the cyber physical energy system, and the invalid and inefficient steps in the traditional process scenario derived from operator’s experience are replaced with more valid and usable actions.

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Due to the huge consumption of materials and energy during machining processes, reduction of manufacturing carbon emission is an essential key to decrease the environmental burden of various manufacturing systems. To achieve this target, one critical step is to calculate and evaluate the carbon emissions of machining processes. However, this step is a little difficult for discrete manufacturing processes, because they are always complex and the data sources are diverse. Considering the complexity of discrete manufacturing workshops, a Big Data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops is proposed in an internet of things-enabled ubiquitous environment. Firstly, the deployment of data acquisition devices is introduced to create a ubiquitous manufacturing workshop, and data modeling of production state and carbon emission is described to realize data acquisition and storage. Then, a data-driven multi-level carbon efficiency evaluation of manufacturing workshop is established based on Big Data analysis approaches. Finally, an auto parts manufacturing workshop is studied to verify the feasibility and applicability of the proposed methods. This method realizes the combination of manufacturing Big Data and low-carbon production. Meanwhile, the evaluation method can be used in other production information systems and then assist the production decision-making.

FDM Rapid Prototyping Technology of Complex-Shaped Mould Based on Big Data Management of Cloud Manufacturing

In order to solve the problem of high cost and long cycle in the process of traditional subtractive material manufacturing of a complex-shaped mould, the technology of FDM rapid prototyping is used in combination with the global service idea of cloud manufacturing, where the information of various kinds of heterogeneous-forming process data produced in the process of FDM rapid prototyping is analysed. Meanwhile, the transfer and transformation relation of each forming process data information in the rapid manufacturing process with the digital model as the core is clarified, so that the FDM rapid manufacturing process is integrated into one, thus forming a digital and intelligent manufacturing system for a complex-shaped mould based on the cloud manufacturing big data management. This paper takes the investment casting mould of a spur gear as an example. Through research on the forming mechanism of jet wire, the factors affecting forming quality and efficiency is analysed from three stages: the pretreatment of the 3D model, the rapid prototyping, and the postprocessing of the forming parts. The relationship between the forming parameters and the craft quality is thus established, and the optimization schemes at each stage of this process are put forward through the study on the forming mechanism of jet wire. Through a rapid prototyping test, it is shown that the spur face gear master mould based on this technology can be quickly manufactured with a critical surface accuracy within a range of 0.036 mm–0.181 mm and a surface roughness within the range of 0.007–0.01 μm by only 1/3 the processing cycle of traditional subtractive material manufacturing. It lays a solid foundation for rapid intelligent manufacturing of products with a complex-shaped structure.

A framework for shopfloor material delivery based on real-time manufacturing big data

Although auto-identification devices such as radio frequency identification and smart sensors have been widely used in shopfloor management and control, major challenges still hinder the vision of real-time and multi-source data-driven material delivery in a manufacturing big data environment. For instance, how to collect manufacturing big data in a timely and accurate manner, and how to discover the hidden pattern from the manufacturing big data rapidly to improve the efficiency of material delivery. To address these challenges, in this paper, a framework for shopfloor material delivery based on real-time manufacturing big data is proposed. Key technologies of the proposed framework are investigated. Firstly, a solution of data sensing and acquisition is designed. Secondly, the methods of manufacturing big data preprocessing and storage are developed to integrate and share the manufacturing data in a unified data format, and to ensure the reusability of the data. Thirdly, a graphic model for the manufacturing big data mining is presented. An improved Apriori-based association analysis model is exploited to identify the frequency trajectories of material delivery. In order to demonstrate the implementation of the proposed framework, a proof-of-concept scenario is designed. The key findings and insights from the experimental results are summarized as managerial implications, which can guide manufacturers to make more informed decisions for the shopfloor management.

State entropy–based fluctuation analysis mechanism for quality state stability in data-driven manufacturing process

Intelligent quality state analysis is a promising tool to deal with manufacturing big data due to its ability in efficiently processing state signals and providing accurate warning results. Inspired by the idea that uses the change of entropy flow to characterize the quality state change, this article proposes a fluctuation analysis mechanism for quality stability based on state entropy in data-driven manufacturing process. First, the multidimensional space cloud model with a three-tuple feature is constructed to describe quality state fluctuation in which the digital features of entropy and hyper-entropy represent the fluctuations’ uncertainty of quality state. Furthermore, in order to quantitatively analyze the fluctuation degree of process state, the entropy change mechanism is introduced into the manufacturing quality state to calculate the state fluctuation degree. The proposed method is validated by a fan blade machining process dataset, and the result shows that the approach could well monitor the quality state fluctuation and show good effect for process stability analysis, which will provide theoretical evidence for the real-time warning and evaluation for abnormal quality state in manufacturing process.

Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

Design of Intelligent Manufacturing Big Data Cloud Service Platform

With the coming of the intelligent manufacturing, the technology and application of industrial big data will be popular in the future. The productivity, competitiveness and innovation of the manufacturing industries will be improved through the integrated innovation of big data technology and industries. Besides, products, production process, management, services, new form and new models will be more intellectualized. They will support the transformation and upgrading of manufacturing industry and the construction of an open, shared and collaborative ecological environment for intelligent manufacturing industry.

염색가공 산업의 에너지 효율화를 위한 제조현장 빅데이터 활용에 관한 연구

염색가공 산업의 에너지 효율화를 위한 제조현장 빅데이터 활용에 관한 연구

Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0

Big data analytics have been employed to extract useful information and derive effective manufacturing intelligence for yield management in semiconductor manufacturing that is one of the most complex manufacturing processes due to tightly constrained production processes, reentrant process flows, sophisticated equipment, volatile demands, and complicated product mix. Indeed, the increasing adoption of multimode sensors, intelligent equipment, and robotics have enabled the Internet of Things (IOT) and big data analytics for semiconductor manufacturing. Although the processing tool, chamber set, and recipe are selected according to product design and previous experiences, domain knowledge has become less efficient for defect diagnosis and fault detection. To fill the gaps, this study aims to develop a framework based on Bayesian inference and Gibbs sampling to investigate the intricate semiconductor manufacturing data for fault detection to empower intelligent manufacturing. In addition, Cohen’s kappa coefficient was used to eliminate the influence of extraneous variables. The proposed approach was validated through an empirical study and simulation. The results have shown the practical viability of the proposed approach.

Statistical process monitoring as a big data analytics tool for smart manufacturing

With ever-accelerating advancement of information, communication, sensing and characterization technologies, such as industrial Internet of Things (IoT) and high-throughput instruments, it is expected that the data generated from manufacturing will grow exponentially, generating so called ‘big data’. One of the focuses of smart manufacturing is to create manufacturing intelligence from real-time data to support accurate and timely decision-making. Therefore, big data analytics is expected to contribute significantly to the advancement of smart manufacturing. In this work, a roadmap of statistical process monitoring (SPM) is presented. Most recent developments in SPM are briefly reviewed and summarized. Specific challenges and potential solutions in handling manufacturing big data are discussed. We suggest that process characteristics or feature based SPM, instead of process variable based SPM, is a promising route for next generation SPM and could play a significant role in smart manufacturing. The advantages of feature based SPM are discussed to support the suggestion and future directions in SPM are discussed in the context of smart manufacturing.

A Manufacturing Big Data Solution for Active Preventive Maintenance

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

Research on real time feature extraction method for complex manufacturing big data

Big data related to manufacturing applications has the traits such as great quantity, multi-sources, low value density, high complexity, and dynamic state. Traditional feature extraction methods are incapable of meeting real-time demands. Therefore, a robust incremental on-line feature extraction method based on PCA (Principal Component Analysis), RIPCA (Robust Incremental Principal Component Analysis), is proposed. RIPCA adopts a sliding window to update new coming data stream and to filter outliers. The proposed method could ensure the accuracy of data analysis and meet real-time demands of big data processing for manufacturing applications. A test data set based on a semiconductor manufacturing process containing 1567 records with 590 features is used to demonstrate the availability of the proposed method. Experimental results show that the method can effectively extract features of the data stream in real time with high accuracy.

The Usage Needs and Adoption Intention of Manufacturing Big Data Technology in Small and Medium-sized Manufacturing Companies

The Usage Needs and Adoption Intention of Manufacturing Big Data Technology in Small and Medium-sized Manufacturing Companies

Mobile Services for Customization Manufacturing Systems: An Example of Industry 4.0

In the context of Industry 4.0, it is necessary to meet customization manufacturing demands on a timely basis. Based on the related concepts of Industry 4.0, this paper intends to introduce mobile services and cloud computing technology into the intelligent manufacturing environment. A customization manufacturing system is designed to meet the demands of personalization requests and flexible production mechanisms. This system consists of three layers, namely, a manufacturing device layer, cloud service system layer, and mobile service layer. The manufacturing device layer forms the production platform. This platform is composed of a number of physical devices, such as a flexible conveyor belt, industrial robots, and corresponding sensors. The physical devices are connected to the cloud via the support of a wireless module. In the cloud, the manufacturing big data are processed, and the optimization decision-making mechanism pertaining to customization manufacturing is formed. Then, mobile services running in a mobile terminal are used to receive orders from customers and to inquire the necessary production information. To verify the feasibility of the proposed customization manufacturing system, we also established a customizable candy production system.