Rough milling optimisation for parts with sculptured surfaces using genetic algorithms in a Stackelberg game

Krimpenis, A. A.; Vosniakos, George–Christopher

doi:10.1007/s10845-008-0147-8

Cited by 30 publications

(19 citation statements)

References 20 publications

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“…Krimpenis and Vosniakos approached this goal with genetic algorithm (GA) to discover optimal sequences after 3000 evaluations [15]. Two GAs were combined later to find good solutions on a three-dimensional pareto front [16]. In our previous work, material remove rate (MRR) and cutting efficiency coefficient were included in the selection decision to account for differences in speeds [8].…”

Section: Tool Selectionmentioning

confidence: 99%

A geometry search approach in case-based tool reuse for mould manufacturing

Zhou

Liu

et al. 2015

Int J Adv Manuf Technol

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Similar freeform surfaces are widely adopted in product family or different generations. Manufacturing of these surfaces with efficiency is essential to competitiveness of the company. Conventionally, milling tools were chosen mainly by experience; therefore, reuse of existing experience is essential to advanced manufacturing. This paper proposed a novel geometry search approach to recommend tool selection for review and reuse based on previous cases. First, a hierarchical representation is extracted from freeform meshes. Then, qualitative and quantitative properties were defined to support matching and comparison in the level of detail. Geometry search algorithm is then adapted with boundary condition to find best sequence of tools, where existing cases were stored in layered slices along with their tool information, and new case of cavity is explored in a sequence to progressively find the best match of each layer. Finally, retrieved tools were adapted to review and assist for process planners. A prototype system and a case study are provided to demonstrate the proposed method.

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Section: Tool Selectionmentioning

confidence: 99%

A geometry search approach in case-based tool reuse for mould manufacturing

Zhou

Liu

et al. 2015

Int J Adv Manuf Technol

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“…The contemporary techniques are those of fuzzy logic, scatter search technique, generic algorithm, Taguchi technique, and response surface methodology. Recently, Krimpenis and Vosniakos (2009) applied the technique of generic algorithm to achieve optimisation of rough milling for a part with sculptured surfaces. Three rough milling objectives were considered such as minimum machining time, maximum removed material and maximum uniformity of the remaining volume at the end of roughing.…”

Section: Optimisation Of Machining Parametersmentioning

confidence: 99%

A framework for machining optimisation based on STEP-NC

Ridwan

2010

J Intell Manuf

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Inappropriate machining conditions such as cutting forces cause tool failures, poor surface quality and worst of all machine breakdowns. This may be avoided by using optimal machining parameters, e.g. feed-rate, and continuing to monitor it throughout the machining process. To optimize feed-rate, we propose a system that consists of an optimisation module, a process control module and a knowledge based evaluation module. STEP-NC is the underlying data model for optimisation. Given the nominal powers, the cutting force can be estimated based on the higher-level production information such as workpiece properties, tool materials and geometries, and machine capabilities. The main function of the Process Control module is process monitoring and control. The output is the desired actual feed-rate. Finally, the actual feed-rate is recorded and evaluated in the Knowledge Based Evaluation module.

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“…Ramos et al [6] analyze different finishing milling strategies of a complex geometry part containing concave and convex surfaces. Krimpenis and Vosniakos [7] developed optimization methodology for obtaining parameter values of sculptured surface parts rough machining. Oktem et al [8] presents method for determination of optimum cutting parameters leading to minimum surface roughness in milling mod surfaces by coupling neural network and genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%