Cloud-Based Master Data Platform for Smart Manufacturing Process

2020 ◽  
pp. 163-170
Author(s):  
Lei Ren ◽  
Ziqiao Zhang ◽  
Chun Zhao ◽  
Guojun Zhang
Keyword(s):  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Master Data ◽  
Data Platform

Implementation of MQTT protocol based network architecture for smart factory

2021 ◽  
pp. 095440542110144
Author(s):  
Chia-Shin Yeh ◽  
Shang-Liang Chen ◽  
I-Ching Li
Keyword(s):  
Machine Tool ◽  
Gesture Recognition ◽  
Manufacturing Process ◽  
Network Architecture ◽  
Information Management System ◽  
Edge Computing ◽  
Smart Factory ◽  
Smart Manufacturing ◽  
Industrial Iot ◽  
Computing Module

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

scholarly journals Smart manufacturing analytics application for semi-continuous manufacturing process – a use case

2018 ◽  
Vol 26 ◽  
pp. 1041-1052 ◽  
Author(s):  
Parikshit Mehta ◽  
Sergio Butkewitsch-Choze ◽  
Christopher Seaman
Keyword(s):  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Use Case ◽  
Continuous Manufacturing

scholarly journals New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021 ◽  
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blast Furnace ◽  
Conceptual Framework ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Steel Company ◽  
Steel Manufacturing ◽  
New Business

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

scholarly journals Smart Manufacturing: Quality Control Perspectives

2021 ◽  
Author(s):  
Joseph Evans Agolla
Keyword(s):  
Quality Control ◽  
Manufacturing Process ◽  
Quality Criteria ◽  
Smart Manufacturing ◽  
Physical Conditions ◽  
Manufacturing Operations ◽  
Accuracy And Precision ◽  
Manufactured Products ◽  
Data Analytic

Quality Control (QC) is a guideline or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. Smart manufacturing is where the work is interfaced work pieces and associated tools that include logistics operations, Cyber Physical Systems, Artificial Intelligence, and Big Data Analytic tools. These form the norm of manufacturing operations to generate large amounts of data, which are used for analysis and prediction. Therefore, help to optimise the quality of manufacturing operations and manufactured products. The change in technologies have, however, altered the traditional way of manufacturing process as well as QC systems. Therefore, to address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process often operating under adverse physical conditions need to provide adequate levels of data accuracy and precision. Methodologically, the Chapter followed critical literature review on QC concepts and Industry 4.0 revolution, thereby culminating into conceptual framework of QC in Smart Manufacturing, which is the main contribution of this Chapter.

scholarly journals Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO

2021 ◽  
Vol 15 ◽  
Author(s):  
Szu-Yin Lin ◽  
Hao-Yu Li
Keyword(s):  
Real Time ◽  
Image Recognition ◽  
Integrated Circuit ◽  
Manufacturing Process ◽  
Circuit Board ◽  
Smart Manufacturing ◽  
Recognition Model ◽  
Second Stage ◽  
Different Types ◽  
Smart Factories

Industry 4.0 has been a hot topic in recent years. The process of integrating Cyber-Physical Systems (CPS), Artificial Intelligence (AI), and Internet of Things (IoT) technology, will become the trend in future construction of smart factories. In the past, smart factories were developed around the concept of the Flexible Manufacturing System (FMS). Most parts of the quality management process still needed to be implemented by Automated Optical Inspection (AOI) methods which required human resources and time to perform second stage testing. Screening standards also resulted in the elimination of about 30% of the products. In this study, we sort and analyze several Region-based Convolutional Neural Network (R-CNN) and YOLO models that are currently more advanced and widely used, analyze the methods and development problems of the various models, and propose a suitable real-time image recognition model and architecture suitable for Integrated Circuit Board (ICB) in manufacturing process. The goal of the first stage of this study is to collect and use different types of ICBs as model training data sets, and establish a preliminary image recognition model that can classify and predict different types of ICBs based on different feature points. The second stage explores image augmentation fusion and optimization methods. The data augmentation method used in this study can reach an average accuracy of 96.53%. In the final stage, there is discussion of the applicability of the model to detect and recognize the ICB directionality in <1 s with a 98% accuracy rate to meet the real-time requirements of smart manufacturing. Accurate and instant object image recognition in the smart manufacturing process can save manpower required for testing, improve equipment effectiveness, and increase both the production capacity and the yield rate of the production line. The proposed model improves the overall manufacturing process.

IoT and Smart Manufacturing

2018 ◽  
pp. 1-38 ◽  
Author(s):  
Aqeel ur Rehman ◽  
Iqbal Uddin Khan ◽  
Ahmar Murtaza ◽  
Uzma Naz
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
The Internet ◽  
Huge Amount

Internet of things (IoT) is a concept of providing uniquely identifiable objects connectivity to the internet. Under the roof of IoT, it is predicted that above 28 billion devices will be connected to the internet by the year 2020. When billions of things connect, it will be difficult to manage and analyze huge amount of data as each object will send and retrieve data. Smart manufacturing is an emerging concept where the manufacturing process is supported by technology and the required information is made available during the manufacturing process to get the flexibility and the product as per customer changing needs. Internet of things (IoT) may provide a good platform to enhance the manufacturing process into smart manufacturing. The advantages of smart manufacturing include the higher quality of a product, improved productivity, increased energy efficiency, enhanced scalability in manufacturing process, etc. This chapter presents in depth the IoT and smart manufacturing concepts, their requirements, relevance, and available solutions.

An Open Web-Based Repository for Capturing Manufacturing Process Information

2016 ◽  
Author(s):  
William Z. Bernstein ◽  
Mahesh Mani ◽  
Kevin W. Lyons ◽  
K. C. Morris ◽  
Björn Johansson
Keyword(s):  
Manufacturing Process ◽  
Recent Progress ◽  
Performance Assessments ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Process Information ◽  
Web Based ◽  
Model Consistency ◽  
Effective Models

With recent progress in developing more effective models for representing manufacturing processes, this paper presents an approach towards an open web-based repository for storing manufacturing process information. The repository is envisioned to include several new use cases in the context of information use in smart manufacturing. This paper examines several key benefits through usage scenarios engaging existing engineering activities. Based on the scenarios, the desired characteristics of an open web-based repository are presented, namely that it will be (1) complementary to existing practices, (2) open and net-centric, (3) able to enforce model consistency, (4) modular (5) extensible, and (5) able to govern contributions. A repository will support and motivate the ubiquitous and extended use of standardized representations of unit manufacturing processes in order to promote consistency of performance assessments across industries and provide a tangible, data-driven perspective for analysis-related activities. Furthermore, the paper presents additional benefits and possible applications that could result from a shared manufacturing repository.

scholarly journals Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process

2021 ◽  
Vol 11 (15) ◽  
pp. 6832
Author(s):  
Yu-Hsin Hung
Keyword(s):  
Quality Management ◽  
Ensemble Learning ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
Data Communication ◽  
Predictive Maintenance ◽  
Learning Performance ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Total Quality

Industrial Internet of Things (IIoT) technologies comprise sensors, devices, networks, and applications from the edge to the cloud. Recent advances in data communication and application using IIoT have streamlined predictive maintenance (PdM) for equipment maintenance and quality management in manufacturing processes. PdM is useful in fields such as device, facility, and total quality management. PdM based on cloud or edge computing has revolutionized smart manufacturing processes. To address quality management problems, herein, we develop a new calculation method that improves ensemble-learning algorithms with adaptive learning to make a boosted decision tree more intelligent. The algorithm predicts main PdM issues, such as product failure or unqualified manufacturing equipment, in advance, thus improving the machine-learning performance. Herein, semiconductor and blister packing machine data are used separately in manufacturing data analytics. The former data help in predicting yield failure in a semiconductor manufacturing process. The blister packing machine data are used to predict the product packaging quality. Experimental results indicate that the proposed method is accurate, with an area under a receiver operating characteristic curve exceeding 96%. Thus, the proposed method provides a practical approach for PDM in semiconductor manufacturing processes and blister packing machines.

A Review of Manufacturing Process Control

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Robert G. Landers ◽  
Kira Barton ◽  
Santosh Devasia ◽  
Thomas Kurfess ◽  
Prabhakar Pagilla ◽  
...  
Keyword(s):  
Process Control ◽  
Manufacturing Process ◽  
Raw Materials ◽  
The Body ◽  
Smart Manufacturing ◽  
Future Research ◽  
Manufacturing Processes ◽  
Monitoring And Control ◽  
Substantial Impact ◽  
Manufacturing Process Control

Abstract Smart manufacturing concepts are being integrated into all areas of manufacturing industries, from the device level (e.g., intelligent sensors) to the efficient coordination of business units. Vital components of any manufacturing enterprise are the processes that transform raw materials into components, assemblies, and finally products. It is the manufacturing process where smart manufacturing is poised to make substantial impact through process control, i.e., the intelligent manipulation of process variables to increase operation productivity and part quality. This article discusses three areas of manufacturing process control: control-oriented modeling, sensing and monitoring, and the design and construction of controllers. The discussion will center around the following manufacturing processes: machining, grinding, forming, joining, and additive. While many other important processes exist, the discussions of control of these mechanical manufacturing processes will form a framework commonly applied to these processes and the discussion will form a framework to provide insights into the modeling, monitoring, and control of manufacturing processes more broadly. Conclusions from these discussions will be drawn, and future research directions in manufacturing process control will be provided. This article acknowledges the contributions of two of the pioneering researchers in this field, Dr. Yoram Koren and Dr. Galip Ulsoy, who have made seminal contributions in manufacturing process control and continued to build the body of knowledge over the course of many decades.

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