0
Research Papers

Networked Fuzzy Control System for a High-Performance Drilling Process

[+] Author and Article Information
Rodolfo E. Haber-Guerra

 Institute of Industrial Automation (CSIC), km. 22800 N-III, La Poveda 28500 Madrid, Spain and Escuela Politécnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Viejo, km 15, 28049 Madrid, Spain

Rodolfo Haber-Haber

Department of Automatic Control, Universidad de Oriente, Avenue Las Américas, s/n, 90400, Santiago de Cuba, Cuba

Diego Martín Andrés, Angel Alique Palomar

 Institute of Industrial Automation (CSIC), km. 22800 N-III, La Poveda, 28500, Madrid, Spain

J. Manuf. Sci. Eng 130(3), 031009 (May 12, 2008) (6 pages) doi:10.1115/1.2783280 History: Received February 21, 2007; Revised August 07, 2007; Published May 12, 2008

The high-performance drilling (HPD) process has a significant impact on production in many industries, such as the automotive, die/mold and aerospace industries. However, cutting conditions for drilling are generally chosen from a machining-data handbook, requiring operator experience and skill. In order to improve drilling efficiency while preserving tool life, the current study focuses on the design and implementation of a simple, optimal fuzzy-control system for drilling force. The main topic of this study is the design and implementation of a networked fuzzy controller. The control system consists of a two-input (force error and change of error), single-output (feed-rate increment) fuzzy controller with nine control rules, the sup-product compositional operator for the compositional rule of inference, and the center of area as the defuzzification method. The control algorithm is connected to the process through a multipoint interface (MPI) bus, a proprietary programming, and communication interface for peer-to-peer networking that resembles the PROFIBUS protocol. The output (i.e., feed-rate) signal is transmitted through the MPI; therefore, network-induced delay is unavoidable. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) criterion. The goal is to obtain the optimal tuning parameters for the input scaling factors while minimizing the ITAE performance index. In this study, a step in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. The optimization is performed using the Nelder–Mead simplex (direct search) method. The main advantage of the approach presented herein is the design of a simple fuzzy controller using a known maximum allowable delay to deal with uncertainties and nonlinearities in the drilling process and delays in the network-based application. The results demonstrate that the proposed control strategy provides an excellent transient response without overshoot and a slightly higher drilling time than the CNC working alone (uncontrolled). A major issue in high performance drilling is the increase in cutting force and torque that occurs as the drill depth increases. Therefore, the fuzzy-control system reduces the influence of these factors, thus eliminating the risk of rapid drill wear and catastrophic drill breakage.

FIGURES IN THIS ARTICLE
<>
Copyright © 2008 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Fuzzy partitions and membership functions for: (a) ΔF,Δ2F; (b) Δf

Grahic Jump Location
Figure 2

Network-based fuzzy-control system architecture for a high-performance drilling process

Grahic Jump Location
Figure 3

Drilling-force response to command feed in high-performance drilling process using a network-based environment

Grahic Jump Location
Figure 4

Scheme of the experimental facilities and fuzzy-control system

Grahic Jump Location
Figure 5

Implementation of the fuzzy-control system in SIMULINK/MATLAB

Grahic Jump Location
Figure 6

(a) ITAE performance index resulting from the optimization and; (b) the corresponding input scaling factors of the fuzzy controller

Grahic Jump Location
Figure 7

Step response of the cutting force: simulation results

Grahic Jump Location
Figure 8

(a) Cutting-force behavior; and (b) feed-rate variations for the minimum, mean, and maximum delay

Grahic Jump Location
Figure 9

Behavior of the ITAE (dashed line) and ITSE (solid line) performance indices in the presence of delays

Grahic Jump Location
Figure 10

Behavior of the overshoot in the presence of delays

Grahic Jump Location
Figure 11

Cutting-force behavior in relationship with drilling depth

Grahic Jump Location
Figure 12

Cutting-force and feed-rate variations in uncontrolled drilling and drilling controlled by a fuzzy regulator

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In