Adaptive systems for machining process monitoring and control

1997 ◽  
Vol 64 (1-3) ◽  
pp. 75-84 ◽  
Author(s):  
G.E. D'Errico
Keyword(s):  
Process Monitoring ◽  
Adaptive Systems ◽  
Machining Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
And Control

Modular CNC Design for Intelligent Machining, Part 2: Modular Integration of Sensor Based Milling Process Monitoring and Control Tasks

1996 ◽  
Vol 118 (4) ◽  
pp. 514-521 ◽  
Author(s):  
Y. Altintas¸ ◽  
W. K. Munasinghe
Keyword(s):  
Process Control ◽  
Real Time ◽  
Process Monitoring ◽  
Tool Path ◽  
Position Control ◽  
Milling Process ◽  
Machining Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
And Control

Modular integration of sensor based milling process monitoring and control functions to a proposed CNC system architecture is presented. Each sensor based process control algorithm resides in a dedicated processor in the AT bus with a modular software. The CNC system’s motion control module has been designed to accomodate rapid manipulation of feeds, cutting conditions and NC tool path which may be demanded by machining process control modules in real time. Modular integration of adaptive control of cutting forces, tool condition monitoring, chatter detection and suppression tasks are illustrated as examples. The process control and monitoring modules are serviced in the real-time multi-tasking environment within one millisecond time intervals without disturbing the position control system. The paper present constraints and guidelines in designing CNC systems which allow modular integration of user developed real time machining process control and monitoring applications.

Machining Process Monitoring and Control: The State-of-the-Art

2004 ◽  
Vol 126 (2) ◽  
pp. 297-310 ◽  
Author(s):  
Steven Y. Liang ◽  
Rogelio L. Hecker ◽  
Robert G. Landers
Keyword(s):  
Control Systems ◽  
Process Monitoring ◽  
Machining Process ◽  
Monitoring And Control ◽  
Process Automation ◽  
The Past ◽  
Process Monitoring And Control ◽  
Monitoring And Control Systems ◽  
Control Technologies ◽  
And Control

Research in automating the process level of machining operations has been conducted, in both academia and industry, over the past few decades. This work is motivated by a strong belief that research in this area will provide increased productivity, improved part quality, reduced costs, and relaxed machine design constraints. The basis for this belief is two-fold. First, machining process automation can be applied to both large batch production environments and small batch jobs. Second, process automation can autonomously tune machine parameters (feed, speed, depth of cut, etc.) on-line and off-line to substantially increase the machine tool’s performance in terms of part tolerances and surface finish, operation cycle time, etc. Process automation holds the promise of bridging the gap between product design and process planning, while reaching beyond the capability of a human operator. The success of manufacturing process automation hinges primarily on the effectiveness of the process monitoring and control systems. This paper discusses the evolution of machining process monitoring and control technologies and conducts an in-depth review of the state-of-the-art of these technologies over the past decade. The research in each area is highlighted with experimental and simulation examples. Open architecture software platforms that provide the means to implement process monitoring and control systems are also reviewed. The impact, industrial realization, and future trends of machining process monitoring and control technologies are also discussed.

Machining Process Monitoring and Control: The State–of–the–Art

Manufacturing ◽  
2002 ◽  
Author(s):  
Steven Y. Liang ◽  
Rogelio L. Hecker ◽  
Robert G. Landers
Keyword(s):  
Process Monitoring ◽  
State Of The Art ◽  
The State ◽  
Machining Process ◽  
Feed Speed ◽  
Monitoring And Control ◽  
Process Automation ◽  
Research Attention ◽  
Process Monitoring And Control ◽  
And Control

Automation at the process level for machining operations and machine tools has been a focus of research attention in both academia and industry alike for several decades. Research in this area has carried strong expectations in the context of increased productivity, improved part quality, reduced costs, and relaxed part design constraints. The basis for these expectations is two-fold. First, machining process automation, if exercised strategically and advantageously, can perform consistently for large batch production or flexibly for small batch jobs. Secondly, process automation can be set up to autonomously tune the machine parameters (feed, speed, depth of cut, etc.) in pursuit of desirable performance (tolerance, finish, cycle time, etc.), thereby bridging the gap between product design and process planning while reaching beyond the human operators’ capability. The success of manufacturing process automation hinges primarily on the effectiveness of process monitoring and control systems. This paper reviews the evolution and the state of the art of machining process monitoring and control technologies. Key issues to be presented include sensor techniques, control techniques, hardware availability, and implementation examples. Also to be reviewed are the benefits of the systems and the reasons for their delayed realization in many of today’s industrial application domains.

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.

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