Automated cutting tool selection and cutting tool sequence optimisation for rotational parts

Oral, Ali; Çakır, Mustafa

doi:10.1016/j.rcim.2003.10.006

Cited by 37 publications

(20 citation statements)

References 15 publications

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“…In the introduced procedure, the specific cutting tool types (e.g., the solid end mill, long edge end mill, and indexable end mill), as substitutes for general types (e.g., end mill and face mill), are determined in the first step, which can restrict the searching domain in the database, as well as the processing time. In addition, the proposed expert system can automatically generate rules for cutting tool type selection depending on the given conditions, in comparison with other manually generated rules used in previous research [1][2][3][4]. It can significantly reduce errors while manually constructing a variety of complex rules.…”

Section: Expert System and Discussionmentioning

confidence: 99%

“…Thus, a computer-aided cutting tool selection system based on an available cutting tool library is essential in modern manufacturing, especially for CAPP. Oral et al developed an automatic tool selection system for the turning process based on rules [1]. With this method, according to feature information, selection is made from an appropriate cutting tool library, but the evaluation of multicriteria is ignored.…”

Section: Introductionmentioning

confidence: 99%

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Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection

2019

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Cutting tool selection plays an important role in achieving reliable quality and high productivity work, and for controlling the total cost of manufacturing. However, it is complicated for process planners to choose the optimal cutting tool when faced with the choice of multiple cutting tools, multiple conflict criteria, and uncertain information. This paper presents an effective method for automatically selecting a cutting tool based on the machining feature characteristics. The optimal cutting tool type is first selected using a proposed multicriteria decision-making method with integrated fuzzy analytical hierarchy process (AHP). The inputs of this process are the feature dimensions, workpiece stability, feature quality, specific machining type, and tool access direction, which determine the cutting tool type priority after evaluating many criteria, such as the material removal capacity, tool cost, power requirement, and flexibility. Expert judgments on the criteria or attributes are collected to determine their weights. The cutting tool types are ranked in ascending order by priority. Then, the rule-based method is applied to determine other specific characteristics of the cutting tool. Cutting tool data are collected from world-leading cutting tool manufacturer, Sandvik, among others. An expert system is established, and an example is given to describe the method and its effectiveness.

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Section: Expert System and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection

2019

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“…Fourthly, features are consistent with the related non-geometric entities. For example, the presence of features or the values of feature parameters may be determined by functional requirements or machining conditions (Anderl and Mendgen 1996;Oral and Cakir 2004). Lastly, the feature model is consistent with the engineering intent.…”

Section: Using Features To Maintain a Product Model's Validitymentioning

confidence: 99%

Paradigm shift: unified and associative feature-based concurrent and collaborative engineering

Chen

Thimm

2008

J Intell Manuf

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With widely used concurrent and collaborative engineering technologies, the validity and consistency of product information become important. In order to establish the state of the art, this paper reviews emerging concurrent and collaborative engineering approaches and emphasizes on the integration of different application systems across product life cycle management (PLM) stages. It is revealed that checking product information validity is difficult for the current computer-aided systems because engineering intent is at best partially represented in product models. It is also not easy to maintain the consistency among related product models because information associations are not established. The purpose of this review is to identify and analyze research issues with respect to information integration and sharing for future concurrent and collaborative engineering. A new paradigm of research from the angle of feature unification and association for product modeling and manufacturing is subsequently proposed.Keywords Concurrent and collaborative engineering · Feature-based design and manufacturing · Product life cycle modeling · Information validity and consistency

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“…For example, Ahmad et al (2010) propose a new system approach to optimize tool sequences using genetic algorithms. Oral and Cakir (2004) define computeraided optimum operation and tool sequencing to be used in the generative process planning system developed for rotational parts. These literatures are mainly focused on tool sequence optimization.…”

Section: Introductionmentioning

confidence: 99%

Tool selection method based on transfer learning for CNC machines

et al. 2018

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Abstract. Owing to the changes in product requirements and development of new tool technology, traditional tool selection approach based on the human experience is leading to time-consuming and low efficiency. Under the cooperation of historical data resource accumulated by manufacturing enterprises, with human expert resource, a new tool selection mechanism can be established. In this paper, we apply transfer learning to tool selection issue. Starting from the foundation of migration, we showed a unified expression of expert experience and process case in a multi-source heterogeneous environment. Then, we propose a transfer learning algorithm (TLrAdaBoost) based on AdaBoost, which uses a small amount of target domain data (expert experience sample) and a large number of source domain low-quality data (process case sample), to build a high-quality classification model. Experimental results show the effectiveness of the proposed algorithm.

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Automated cutting tool selection and cutting tool sequence optimisation for rotational parts

Cited by 37 publications

References 15 publications

Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection

Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection

Paradigm shift: unified and associative feature-based concurrent and collaborative engineering

Tool selection method based on transfer learning for CNC machines

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