Selection of optimal conditions for CNC multitool drilling system using non-traditional techniques

Satishkumar, S.; Asokan, P.

doi:10.1504/ijmmm.2008.017633

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…In recent years, considerable attention has been paid by the researchers and practitioners towards employing some non-traditional metaheuristic search techniques for global optimization, which can ensure the best feasible solution of an optimization problem [20][21][22][23][24]. Many metaheuristic methods are highly successful in solving a wide range of difficult optimization problems.…”

Section: The Desirability Optimization Methodology Combines Desirabilmentioning

confidence: 99%

A novel desirability function for multi-response optimization and its application in chemical engineering

Marinković

2020

CI&CEQ

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In many aspects of modern engineering (processes, products, and systems) there is a need to optimize multiple responses simultaneously, rather than optimizing one response at a time. The optimization of each individual response may generate as many different results as the responses are considered in the study. Then, it may be impossible to decide whether one solution is better than the other. On the other hand, some improvement in one response can significantly degrade at least one or more responses. There are a number of multiresponse optimization techniques available. Among the these multi-response optimization techniques, the desirability-based approaches take a prominent place because they are less sophisticated, easy to understand and implement, and more flexible with respect to other existing approaches, due to which they are very popular among researchers and practitioners. There are many different formulations of the desirability function. Unfortunately, most desirability functions known in the literature are piecewise, non-differentiable functions. In this paper, a novel desirability function is proposed, which is continuous and differentiable in its domain. This function is more suitable for applying some of the efficient gradient-based optimization methods. The efficiency and accuracy of the proposed method were analyzed on two chemical processes that were studied extensively in the literature.

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Section: The Desirability Optimization Methodology Combines Desirabilmentioning

confidence: 99%

A novel desirability function for multi-response optimization and its application in chemical engineering

Marinković

2020

CI&CEQ

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“…Numerous investigations have confirmed that responses in machining processes do not deviate much from linear functions [8,16,22,23,45]. For instance, in the turning process the material removal rate (MRR) is a linear function of process parameters.…”

Section: Examplementioning

confidence: 99%

“…In recent years, considerable attention has been paid by researchers and practitioners towards employing some successful metaheuristic search techniques for global optimization, such as genetic algorithm (GA), simulated annealing (SA) algorithm, particle swarm optimization (PSO) algorithm , ant colony optimization (ACO) algorithm, tabu search (TS) algorithm, artificial bee colony (ABC) algorithm, differential evolution (DE), including the later developed cuckoo search (CS) algorithm, imperialist competitive algorithm (ICA) teaching-learning-based optimization (TLBO) method, gray wolf optimizer (GWO), and many others, which can provide the best feasible solution of an optimization problem [14][15][16][17][18][19][20]. At the present time, among these algorithms, the classical genetic algorithm and its hybrid variants are still the most popular techniques for various optimization problems [15], [21], especially in material processing technologies [16], [22], [23]. Many of the above mentioned algorithms are used for single and/or multi-response optimization.…”

Section: Introductionmentioning

confidence: 99%

Some applications of a novel desirability function in simultaneous optimization of multiple responses

Marinković¹

2021

FME Transactions

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In the framework of multi-response optimization techniques, the optimization methodology based on the desirability function is one of the most popular and most frequently used methodologies by researchers and practitioners in engineering, chemistry, technology and many other fields of science and technique. Numerous desirability functions have been introduced to improve the performance of this optimization methodology. Recently, a novel desirability function for multi-response optimization is proposed, which is smooth, nonlinear, and differentiable, and thus more suitable for applying some of the more efficient gradient-based optimization methods. This paper evaluates the performance of the proposed method through six real examples. After a comparative analysis of the results, it is shown that the proposed method in a certain measure outperforms the other competitive optimization methods.

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“…The fraction of machining time spent on drilling holes can vary from an average of 40% for a typical product (Shunmugam et al, 2000) to more than 80% for products that incorporate dense matrices of holes such as heat exchangers, food-processing separators (Figure 1), as well as drum and trammel screens. And as such, cost optimization of CNC drilling operations has been an important field of study in the literature (Ke et al, 2006;Zhu & Zhang, 2008;Satishkumar & Asokan, 2008). Along with the opportunity of reducing the production via small improvements in the cutting conditions, the total travelled distance of the tool between drilling locations can be reduced through optimum path planning.…”

Section: Introductionmentioning

confidence: 99%

CNC Machining Path Planning Optimization for Circular Hole Patterns via a Hybrid Ant Colony Optimization Approach

Abbas

Hamza²,

Aly³

2014

MER

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This paper presents a path-planning optimization study for a Computer Numerical Control (CNC) machining center tasked with machining jobs involving a large number of holes to drill that are mostly arranged in concentric circular patterns. Benefits of this research may contribute to shortening the machining time in certain components used in heat exchangers, boilers, condensers, trammel screens and food separators. Optimization of tool travel distance and machining cost are typically overlooked aspects when generating tool paths and CNC codes from commercially available CAD software packages. Tool path travel distance minimization can be modelled Travelling Salesman Problem (TSP). Optimization algorithms have been heavily applied in the literature to the TSP with varying levels of success. Ant Colony Optimization (ACO) is one of the most prominent approaches that mimics the natural behavior of ant colonies. The research in this paper proposes a hybrid ACO that has a biasing mechanic designed to take advantage of the geometric hole-pattern arrangement, as well as a local search. Simulation examples show the proposed approach exhibiting superior performance compared to the classic ACO approach, a genetic algorithm (GA) approach, as well as the simple spiral path generated via commercial CAD software. The proposed approach is then applied to the drilling path planning of a two-thousand-hole food-industry separator plate.

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Selection of optimal conditions for CNC multitool drilling system using non-traditional techniques

Cited by 13 publications

References 13 publications

A novel desirability function for multi-response optimization and its application in chemical engineering

A novel desirability function for multi-response optimization and its application in chemical engineering

Some applications of a novel desirability function in simultaneous optimization of multiple responses

CNC Machining Path Planning Optimization for Circular Hole Patterns via a Hybrid Ant Colony Optimization Approach

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