A Bio-Inspired Framework for a Self-Healing Assembly System

2011 ◽  
Author(s):  
Lee J. Wells ◽  
Jaime A. Camelio ◽  
Giovannina Zapata
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
System Level ◽  
Self Healing ◽  
Monitoring And Control ◽  
Statistical Process ◽  
Control Approach ◽  
Process Monitoring And Control ◽  
Manufacturing Assembly ◽  
And Control

Statistical process monitoring and control has been popularized throughout the manufacturing industry as well as various other industries interested in improving product quality and reducing costs. Advances in this field have focused primarily on more efficient ways for diagnosing faults, reducing variation, developing robust design techniques, and increasing sensor capabilities. System level advances are largely dependent on the introduction of new techniques in the listed areas. A unique system level quality control approach is introduced in this paper as a means to integrate rapidly advancing computing technology and analysis methods in manufacturing systems. Inspired by biological systems, the developed framework utilizes immunological principles as a means of developing self-healing algorithms and techniques for manufacturing assembly systems. The principles and techniques attained through this bio-mimicking approach will be used for autonomous monitoring, detection, diagnosis, prognosis, and control of station and system level faults, contrary to traditional systems that largely rely on final product measurements and expert analysis to eliminate process faults.

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalad Rao ◽  
Hui Yang
Keyword(s):  
Quality Control ◽  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Experimental Studies ◽  
Imaging Data ◽  
Monitoring And Control ◽  
Ultraprecision Machining ◽  
Statistical Process ◽  
Lacunarity Analysis

The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.

Characteristics and Technologies of Advanced CNC Systems

2011 ◽  
pp. 519-526 ◽  
Author(s):  
M. Minhat ◽  
X.W. Xu
Keyword(s):  
Computer Aided Design ◽  
Manufacturing Industry ◽  
Numerical Control ◽  
Monitoring And Control ◽  
Cnc Machine ◽  
Computer Aided ◽  
Process Monitoring And Control ◽  
Key Issues ◽  
Aided Design ◽  
And Control

Computer Numerical Control (CNC) systems are the “backbones” of modern manufacturing industry for over the last 50 years and the machine tools have evolved from simple machines with controllers that had no memory and were driven by punched tape, to today’s highly sophisticated, multiprocess workstations. These CNC systems are still being worked and improved on. The key issues center on autonomous planning, decision making, process monitoring and control systems that can adjust automatically to the changeable requirements. Introduction of CNC systems has made it possible to produce goods with consistent qualities, apart from enabling the industry to enhance productivity with a high degree of flexibility in a manufacturing system. CNC systems sit at the end of the process starting from product design using Computer Aided Design (CAD) tools to the generation of machining instructions that instruct a CNC machine to produce the final product. This process chain also includes Computer Aided Process Planning (CAPP) and Computer Aided Manufacturing (CAM).

A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems

2021 ◽  
Author(s):  
Zhangyue Shi ◽  
Chenang Liu ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Yang Chen
Keyword(s):  
Information Technologies ◽  
Manufacturing Systems ◽  
Rapid Development ◽  
Data Access ◽  
Sensor Data ◽  
False Alarms ◽  
Monitoring And Control ◽  
Stream Data ◽  
Physical Security ◽  
And Control

Abstract With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.

The Isolator of Industrial Automatic Control System Selection and Application

2011 ◽  
Vol 301-303 ◽  
pp. 1714-1718
Author(s):  
Ji Meng Zhang ◽  
Hong Shuo Wang ◽  
Ben De Gan
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Automatic Control System ◽  
Signal Transmission ◽  
Control Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Isolation Device ◽  
System Selection ◽  
And Control

In the automatic control system of industrial field, the production process monitoring and control process is dependent on Mutual coordination of various automation instrument, computer and corresponding actuators. The coordination is accurate or not, the key is signal transmission quality among those agencies. The application and selection of isolation device directly affect signal transmission. This paper discusses the application and choose of industrial site isolator from isolation principle, the principle and choose for isolator, commissioning and parameter selection based on practical application.

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