A classic solution for the control of a high-performance drilling process

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

show abstract

“…In our simulation, the formulas for the plant examined are given as follows [34]:

\begin{matrix} G (s) = \frac{1958}{s^{3} + 17.89 s^{2} + 103.3 s + 190.8} . \end{matrix}

…”

Section: Two Real-world Optimization Problemsmentioning

confidence: 99%

Bare-Bones Teaching-Learning-Based Optimization

Zou

Wang

Hei

et al. 2014

The Scientific World Journal

View full text Add to dashboard Cite

show abstract

“…For the study, an approximate representation of the process behavior was used [10]. The linear model represented via a third-order transfer function is expressed accordingly …”

Section: The High-performance Drilling Processmentioning

confidence: 99%

“…From the experimental trials performed previously [10], the estimated maximum delay is 0.4 s, including both the dead time and network-induced delay. Fig.…”

Section: A Networked System Architecturementioning

confidence: 99%

Internal Model Control Based on a Neurofuzzy System for Network Applications. A Case Study on the High-Performance Drilling Process

Gajate¹,

Haber

2009

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

This paper presents the design and implementation of a neurofuzzy system for modeling and control of a high-performance drilling process in a networked application. The neurofuzzy system considered in this work is an Adaptive-Network-Based Fuzzy Inference System (ANFIS), where fuzzy rules are obtained from input/output data. The design of the control system is based on the internal model control paradigm. The results obtained are significant both in simulation as well as the real-time application of networked control of the cutting force during high-performance drilling processes.Note to Practitioners-This paper discusses a method to control a highperformance drilling process in a network-based application. A neurofuzzy system learns direct and inverse dynamics of the process through input/ output data, and creating direct and inverse models. Both models are introduced in an internal model control scheme. The main goal is to control the cutting force through a communication network by modifying the feed rate. As detailed in a case study, the proposed method performs better than the PID control strategy. The main advantages of the suggested approach are the use of a neurofuzzy system to deal with nonlinear drilling process behavior and process uncertainty, and the elimination of the need for an exact mathematical model to design/tune the control system. Instead, input/output experimental data are used to obtain direct and inverse neurofuzzy models.Index Terms-High-performance drilling, internal model control, networked control, neurofuzzy systems.

show abstract

“…Major difficulty in modeling drilling process is that dynamics of the process are not completely understood (Chung and Tomizuka, 2001). Different modeling and control techniques using empirical methods, fuzzy logic, neural network and transfer function models of different order are available in literature (Stone and Krishanmurthy, 1996;Chung and Tomizuka, 2001;Kawaji et al, 2001;Kim et al, 1994Kim et al, , 2003Haber-Haber et al, 2007;del-Toro et al, 2007;Sheng et al, 2000;Furness et al, 1999;Oh et al, 2003;Kawaji and Suenaga, 1995). Damage due to delamination is also related to torque produced during drilling (Singh and Bhatnagar, 2006a, b).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly fuzzy logic controller can be used to track a second-order reference thrust force having critical damping ratio (z ¼ 1) (Chung and Tomizuka, 2001). Classical PI, PID controller, pole placement, predictive control and intelligent supervisory control can be used to control thrust force or torque (Kim et al, 1994;Haber-Haber et al, 2007;del-Toro et al, 2007;Sheng et al, 2000;Furness et al, 1999;Oh et al, 2003;Kawaji and Suenaga, 1995;Dharan and Won, 2000).…”

mentioning

confidence: 99%

Optimal control during drilling in GFRP composite laminates

Singh

Sharma

Singh

2014

Multidiscipline Modeling in Materials and Structures

View full text Add to dashboard Cite

Purpose -Damage due to delamination is an important issue during drilling in polymer-matrix composites (PMCs). It depends on thrust force and torque which are functions of feed rate. Transfer function of thrust force with feed rate and torque with feed rate is constructed through experiments. These transfer functions are then combined in state-space to formulate a sixth-order model. Then thrust force and torque are controlled by using optimal controller. The paper aims to discuss these issues. Design/methodology/approach -A glass fiber reinforced plastic composite is drilled at constant feed rate during experimentation. The corresponding time response of thrust force and torque is recorded. Third-order transfer functions of thrust force with feed rate and torque with feed rate are identified using system identification toolbox of Matlab s . These transfer functions are then converted into sixth-order combined state-space model. Optimal controller is then designed to track given reference trajectories of thrust force/torque during drilling in composite laminate. Findings -Optimal control is used to simultaneously control thrust force as well as torque during drilling. There is a critical thrust force during drilling below which no delamination occurs. Therefore, critical thrust force profile is used as reference for delamination free drilling. Present controller precisely tracks the critical thrust force profile. Using critical thrust force as reference, high-speed drilling can be done. The controller is capable of precisely tracking arbitrary thrust force and torque profile simultaneously. Findings suggest that the control mechanism is efficient and can be effective in minimizing drilling induced damage in composite laminates. Originality/value -Simultaneous optimal control of thrust force and torque during drilling in composites is not available in literature. Feed rate corresponding to critical thrust force trajectory which can prevent delamination at fast speed also not available has been presented.

show abstract

A classic solution for the control of a high-performance drilling process

Cited by 37 publications

References 11 publications

Bare-Bones Teaching-Learning-Based Optimization

Bare-Bones Teaching-Learning-Based Optimization

Internal Model Control Based on a Neurofuzzy System for Network Applications. A Case Study on the High-Performance Drilling Process

Optimal control during drilling in GFRP composite laminates

Contact Info

Product

Resources

About