Application of intelligent CNC in grinding

Rowe, W.B.; Li, Y.; Mills, B.; Allanson, David

doi:10.1016/0166-3615(96)00036-x

Cited by 27 publications

(19 citation statements)

References 26 publications

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“…In recent years, great efforts have been made to develop intelligent methodologies to address these needs. Rowe et al (1994Rowe et al ( , 1996 provided an extensive review on diverse applications of artificial intelligence (AI) to grinding processes, and categorized them by the underlying techniques: knowledge-based expert systems, fuzzy logic, neural networks, genetic algorithms (GAs), and adaptive control for optimization (ACO).…”

Section: Introductionmentioning

confidence: 99%

Generalized Intelligent Grinding Advisory System

Choi

Shin

2007

International Journal of Production Research

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The paper presents the current development of the Generalized Intelligent Grinding Advisory System (GIGAS), which provides a systematic way of modelling complex grinding processes and finding optimal process conditions while meeting the general class of process requirements. GIGAS provides a way of incorporating three different types of knowledge, including analytical models, experimental data and heuristic knowledge from experts, to describe complex grinding processes. The developed optimization algorithm can handle various optimization problems including different grinding processes and optimization objectives. Case studies are presented for surface grinding and cylindrical plungegrinding with various optimization objectives to demonstrate its capability of performing optimization. The overall architecture and the developed software with the graphical user interface are described.

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Section: Introductionmentioning

confidence: 99%

Generalized Intelligent Grinding Advisory System

Choi

Shin

2007

International Journal of Production Research

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“…It has not changed much in recent years. However, the advent of artificial intelligence (AI) for the design of the controller has had a significant effect on the grinding process, which can improve the grinding efficiency and the workpiece quality [2][3][4][5]. In the field of grinding, a variety of approaches have been employed such as knowledge-based system, expert system, fuzzy logic, neural networks, genetic algorithm, and adaptive control optimization (ACO).…”

Section: Introductionmentioning

confidence: 99%

“…From the viewpoint of AI, ACO is an integration of a knowledge-based system into a controller. It has the capabilities such as sensing and monitoring process parameters and uses rules and models to optimize the process and adaptively control the process [2].…”

Section: Introductionmentioning

confidence: 99%

“…Initial value of the current threshold a p 0 Initial value of a p v b 0 Initial value of v b a 2 Adjusted coefficient of a p a 3 Adjusted coefficient of v b E i Linguistic variable of e (error) E j Linguistic variable of ec (change of the error) U n(i, j) linguistic variable of u (output control signal) k u (u=1, 2, 3) Output scaling factor D Sphere diameter (x 0 , y 0 , z 0 ) Center coordinate parameters of the sphere (x i , y i , z i ) Coordinates of the ith sampling point R 1 Sphere radius before grinding R 2 Sphere radius after grinding t 1 Grinding time F N Applied normal force F TH Lower limit of the normal force below which no significant amount of material removal takes place …”

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confidence: 99%

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A dynamic threshold-based fuzzy adaptive control algorithm for hard sphere grinding

Wei

et al. 2011

Int J Adv Manuf Technol

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Grinding wheel wearing fast and metal adhering were severe in hard sphere grinding, which led to wheel overload and clogging. If a fixed-feed grinding was used, the normal pressure between the workpiece and the grinding wheel increased rapidly. Once the grinding load on the grinding wheel was greater than the strength of the retaining bond bridges, a large amount of grains dropped out, which can even damage the wheel. This led to the sphere surface to be scratched. In this study, a dynamic threshold-based fuzzy adaptive control algorithm (DTbFACA) is proposed for hard sphere grinding to avoid scratches on the workpiece. The grinding force was indirectly obtained by measuring the motorized spindle current which was used as a feedback to control hard sphere grinding process. The current threshold in DTbFACA was obtained and online-rectified automatically. The depth of cut and the cup wheel swing speed that affect the motorized spindle current was online-adjusted by fuzzy algorithm. The experimental results indicated that DTbFACA can avoid scratches on the workpiece without sacrificing the sphere form error and grinding efficiency. DTbFACA has been implemented on MD6050 sphere grinding machine tool in production.

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“…The plunge grinding process was either planned a priori or adaptively controlled to achieve a desired surface finish. Rowe et al (1996a) reviewed the use of intelligent control and optimisation techniques in grinding and proposed the incorporation of intelligent techniques into CNCs. Intelligent CNC with ACO has been shown to provide significant improvements in productivity.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an intelligent grinding assistant system

Morgan

Cai

Guidotti³

et al. 2007

IJAT

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In modern competitive manufacturing industry, machining processes are expected to deliver products with high accuracy and assured surface integrity, using shorter cycle times with reduced operator intervention and increased flexibility. To meet such demands, the trend towards increased use of machine intelligence in machining systems and operations is clear and unlikely to be revised. This paper describes the structure, content and relations employed in a fully integrated intelligent grinding system for adaptive controlled cycle optimisation, thermal damage avoidance, dressing interval optimisation and data retention. This system, termed: 'Intelligent Grinding Assistant' (IGA©) is a unique, robust and versatile software system with embedded links and protocols for communication with the Computer Numerical Control (CNC) and instrumentation of a grinding machine. The IGA© was evaluated and implemented on a commercially available production machine and had its international launch in September 2005. The CNC, machine tool instrumentation, process monitoring and data analysis systems, Profibus link, and control strategies all constituted the IGA© system. Grinding performance was monitored and assessed in real time. An intelligent database was also developed to support the IGA© in the provision of optimised and/or safe starting cycle data to the operator/adaptive system and in the selective Design and implementation of an intelligent grinding assistant system 107 retention of data. Importantly, the IGA© has been designed and implemented in a way to ensure that an operator can readily use the system and achieve process improvement without recourse to extensive training.

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Application of intelligent CNC in grinding

Cited by 27 publications

References 26 publications

Generalized Intelligent Grinding Advisory System

Generalized Intelligent Grinding Advisory System

A dynamic threshold-based fuzzy adaptive control algorithm for hard sphere grinding

Design and implementation of an intelligent grinding assistant system

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