A comparison of model-based machining force control approaches

“…Job-shops environments require adaptive techniques since tool-part combinations are different at each operation, batch and high volume environments are characterized by fixing or varying within a known range tool-part combinations [1]. In this work, job-shop environments are taken into account to design the control scheme.…”

mentioning

confidence: 99%

Intelligent adaptive control of forces in milling processes

Rubio

Sen

Bilbao-Guillerma

2007

2007 Mediterranean Conference on Control &Amp; Automation

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“…The machining can be stopped once a certain threshold is crossed. However, in an adaptive controlled system, the signals from the on-line measurement have to be processed, and a feedback is given to the machine tool controller to adjust the cutting parameters [30][31][32][33][34][35][36][37][38][39]. In the literature, three major types of adaptive control, namely adaptive control constraint (ACC), geometric adaptive control (GAC), and adaptive control optimization (ACO) have been mentioned.…”

Section: Introductionmentioning

confidence: 99%

Development of a feed rate adaption control system for high-speed rough and finish end-milling of hardened EN24 steel

Saikumar

Shunmugam

2011

Int J Adv Manuf Technol

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High-speed machining centers are used for end-milling operations of a variety of parts, dies, and molds needed in power and transport industries. Different approaches are used for rough and finish endmilling, since desired productivity and quality are important in the respective cases. In the present work, a feed rate adaption control system is proposed by integrating different requirements of high-speed endmilling. Hardened EN 24 steel which is being widely used in the production of dies, molds, and other parts is taken as a candidate work material for implementation of the proposed control system. Based on extensive experimentation, investigations have been carried out on highspeed rough and finish end-milling operations, and the details are reported by the authors (Saikumar and Shunmugam, Int J Adv Manuf Technol, under review). In this paper, relevant response surface and artificial neural network models have been used, and suitable reference parameters are obtained for the proposed control system. In the case of rough end-milling, material removal volume is taken as the objective, and the reference values for cutting force and cutting time are used. Only a reference cutting force is used for finish end-milling in which surface roughness is considered as the objective. Implementation details of the proposed PC-based control system are presented. The results obtained for a newly devised H-A-S-H test (short run) along with those for long-run tests are presented and discussed.

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“…Since then, the machine operators´ experience or handbooks are typically used to select the process cutting parameters, the tool and even the process in-self. Nowadays, the increased competence makes to plan thoroughly machining conditions to minimize costs and improve final product (Landers et al, 2004). This paper bring forward the concept of selecting an appropriate mill cutter, among a known set of candidate cutters, while obtaining the adequate cutting parameters for milling operations through an expert system.…”

Section: Introductionmentioning

confidence: 99%

An Expert Mill Cutter and Cutting Parameters Selection System incorporating a control strategy

Rubio

Sen

2008

IFAC Proceedings Volumes

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An expert mill cutter and cutting parameters selection system incorporating a control strategy Original CitationRubio Rodriguez, Luis and De la Sen Parte, Manuel (2008) The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or notforprofit purposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way. Abstract: This paper discusses the selection of tools and cutting parameters in milling operations. To carry out this research, an expert system has been developed based on numerical methods of performance evaluation. The knowledge base is given by limitations in process variables, which let us to define the allowable cutting parameter space. The mentioned limitations are instabilities due to tool-work-piece interaction, known as chatter vibration, the power available in the spindle motor and milling forces control restrictions. Then, a tool cost model is contrived. It is used to choose a suitable cutting tool, among a known set of candidate available cutters, and to obtain the appropriate cutting parameters, which are expert system outputs. Finally, the expert system can automatically provide other cutter and/or cutting parameters if process specifications change. An example is presented to illustrate the method.

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A comparison of model-based machining force control approaches

Cited by 54 publications

References 14 publications

Intelligent adaptive control of forces in milling processes

Intelligent adaptive control of forces in milling processes

Development of a feed rate adaption control system for high-speed rough and finish end-milling of hardened EN24 steel

An Expert Mill Cutter and Cutting Parameters Selection System incorporating a control strategy

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