Monitoring and Control of Machining Process by Data Mining and Pattern Recognition

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.

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Monitoring and Control of Cutting Forces in Machining Processes: A Review

International Journal of Automation Technology ◽

10.20965/ijat.2009.p0445 ◽

2009 ◽

Vol 3 (4) ◽

pp. 445-456 ◽

Cited By ~ 26

Author(s):

Atsushi Matsubara ◽

◽

Soichi Ibaraki

Keyword(s):

Cutting Force ◽

Cutting Forces ◽

Tool Path ◽

Machining Process ◽

Monitoring And Control ◽

Machining Processes ◽

Control Schemes ◽

Process Monitoring And Control ◽

Optimization Schemes ◽

And Control

Much research has gone into machining process monitoring and control. This paper reviews monitoring and control schemes of cutting force and torque. Sensors to measure cutting force and torque, as well as their indirect estimation, are reviewed. Feedback control schemes and model-based feedforward scheduling schemes of cutting forces, as well as tool path optimization schemes for cutting force regulation, are reviewed. The authors’ works are also briefly presented.

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Multivariate Statistical Process Monitoring and Control of Machining Process using Principal Components based Hotelling T2 Charts: A Machine Vision Approach

International Journal of Productivity and Quality Management ◽

10.1504/ijpqm.2020.10034309 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Ketaki Joshi ◽

Bhushan Patil

Keyword(s):

Process Monitoring ◽

Principal Components ◽

Machining Process ◽

Monitoring And Control ◽

Multivariate Statistical ◽

Statistical Process ◽

Process Monitoring And Control ◽

Multivariate Statistical Process Monitoring ◽

Statistical Process Monitoring ◽

And Control

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Clustering techniques and their applications in engineering

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062jmes508 ◽

2007 ◽

Vol 221 (11) ◽

pp. 1445-1459 ◽

Cited By ~ 19

Author(s):

D T Pham ◽

A A Afify

Keyword(s):

Data Mining ◽

Clustering Algorithms ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Monitoring And Control ◽

Design Quality ◽

Clustering Problem ◽

Manufacturing System Design ◽

And Control

Clustering is an important data exploration technique with many applications in different areas of engineering, including engineering design, manufacturing system design, quality assurance, production planning and process planning, modelling, monitoring, and control. The clustering problem has been addressed by researchers from many disciplines. However, efforts to perform effective and efficient clustering on large data sets only started in recent years with the emergence of data mining. The current paper presents an overview of clustering algorithms from a data mining perspective. Attention is paid to techniques of scaling up these algorithms to handle large data sets. The paper also describes a number of engineering applications to illustrate the potential of clustering algorithms as a tool for handling complex real-world problems.

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

Journal of Manufacturing Science and Engineering ◽

10.1115/1.1707035 ◽

2004 ◽

Vol 126 (2) ◽

pp. 297-310 ◽

Cited By ~ 201

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.

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