Chacterization of Cutting Force Induced Surface Shape Variation Using High-Definition Metrology

2012 ◽  
Author(s):  
Hai Trong Nguyen ◽  
Hui Wang ◽  
S. Jack Hu
Keyword(s):  
Cutting Force ◽  
Process Monitoring ◽  
Surface Shape ◽  
Machining Process ◽  
Monitoring And Control ◽  
High Definition ◽  
Surface Patterns ◽  
Cutting Processes ◽  
And Control ◽  
The Impact

High-definition metrology (HDM) systems with fine lateral resolution are capable of capturing the surface shape on a machined part that is beyond the scope of measurement systems employed in manufacturing plants today. Such surface shapes can precisely reflect the impact of cutting processes on surface quality. Understanding the cutting processes and the resultant surface shape is vital to identifying opportunities for high-precision machining process monitoring and control. This paper presents modeling and experiments of a face milling process to extract surface patterns from measured HDM data and correlate these patterns with cutting force variation. A relation is established between instantaneous cutting forces and the observed dominant patterns along the feed and circumferential directions. Potential applications of such relationship in process monitoring, diagnosis, and control are also discussed.

Characterization of Cutting Force Induced Surface Shape Variation in Face Milling Using High-Definition Metrology1

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Hai Trong Nguyen ◽  
Hui Wang ◽  
S. Jack Hu
Keyword(s):  
Cutting Force ◽  
Process Monitoring ◽  
Face Milling ◽  
Surface Shape ◽  
Machining Process ◽  
High Definition ◽  
Surface Patterns ◽  
Cutting Processes ◽  
And Control ◽  
The Impact

High-definition metrology (HDM) systems with fine lateral resolution are capable of capturing the surface shape on a machined part that is beyond the capability of measurement systems employed in manufacturing plants today. Such surface shapes can precisely reflect the impact of cutting processes on surface quality. Understanding the cutting processes and the resultant surface shape is vital to high-precision machining process monitoring and control. This paper presents modeling and experiments of a face milling process to extract surface patterns from measured HDM data and correlate these patterns with cutting force variation. A relationship is established between the instantaneous cutting forces and the observed dominant surface patterns along the feed and circumferential directions for face milling. Potential applications of this relationship in process monitoring, diagnosis, and control are also discussed for face milling. Finally a systematic methodology for characterizing cutting force induced surface variations for a generic machining process is presented by integrating cutting force modeling and HDM measurements.

An object-oriented architecture for sensorless cutting force feedback for CNC milling process monitoring and control

2010 ◽  
Vol 41 (5) ◽  
pp. 754-761 ◽  
Author(s):  
Roberto Augusto Gómez Loenzo ◽  
Pedro Daniel Alaniz Lumbreras ◽  
René de Jesús Romero Troncoso ◽  
Gilberto Herrera Ruiz
Keyword(s):  
Cutting Force ◽  
Process Monitoring ◽  
Force Feedback ◽  
Object Oriented ◽  
Milling Process ◽  
Cnc Milling ◽  
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.

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

2009 ◽  
Vol 3 (4) ◽  
pp. 445-456 ◽  
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.

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.

Image-Guided Variant Geometry Analysis of Layerwise Build Quality in Additive Manufacturing

2019 ◽  
Author(s):  
Farhad Imani ◽  
Ruimin Chen ◽  
Evan Diewald ◽  
Edward Reutzel ◽  
Hui Yang
Keyword(s):  
Quality Control ◽  
Additive Manufacturing ◽  
Process Monitoring ◽  
Flaw Detection ◽  
Monitoring And Control ◽  
Image Guided ◽  
Process Monitoring And Control ◽  
And Control ◽  
Geometry Analysis ◽  
The Impact

Abstract Additive manufacturing (AM) is a new paradigm in design-driven build of customized products. Nonetheless, mass customization and low volume production make the AM quality assurance extremely challenging. Advanced imaging provides an unprecedented opportunity to increase information visibility, cope with the product complexity, and enable on-the-fly quality control in AM. However, in-situ images of a customized AM build show a high level of layer-to-layer geometry variation, which hampers the use of powerful image-based learning methods such as deep neural networks (DNNs) for flaw detection. Few, if any, previous works investigated how to tackle the impact of AM customization on image-guided process monitoring and control. The proposed research is aimed at filling this gap by developing a novel real-time and multi-scale process monitoring methodology for quality control of customized AM builds. Specifically, we leverage the computer-aided design (CAD) file to perform shape-to-image registration and delineate the regions of interests in lay-erwise images. Next, a hierarchical dyadic partitioning methodology is developed to split layer-to-layer regions of interest into subregions with the same number of pixels to provide freeform geometry analysis. Then, we propose a semiparametric model to characterize the complex spatial patterns in each customized subregion and boost the computational speed. Finally, a DNN model is designed to learn and detect fine-grained information of flaws. Experimental results show that the proposed process monitoring and control methodology detects flaws in each layer with an accuracy of 92.50±1.03%. This provides an opportunity to reduce inter-layer variation in AM prior to completion of the build. The proposed methodology can also be generally applicable in a variety of engineering and medical domains that entail image-based process monitoring and control with customized designs.

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.

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