Modeling and adaptive force control of milling by using artificial techniques

Župerl, Uroš; Čuš, F.; Reibenschuh, Marko

doi:10.1007/s10845-010-0487-z

Cited by 47 publications

(22 citation statements)

References 22 publications

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“…ANNs consist of a large number of processing elements called neurons that operate in parallel [6,7]. Computing with neural networks is non-algorithmic.…”

Section: Ann-based Prediction Of Natural Gas Consumptionmentioning

confidence: 99%

Natural gas consumption prediction in Slovenian industry – a case study

Kovačić

Šarler

Župerl

2016

Materials and Geoenvironment

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In accordance with the regulations of the Energy Agency of the Republic of Slovenia, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences represent up to 2% of supplied natural gas costs. All the natural gas users, especially industry, have huge problems finding the proper method for efficient natural gas consumption prediction and, consequently, the decreasing of mentioned costs. In this study, prediction of the natural gas consumption in Štore Steel Ltd. (steel plant) is presented. On the basis of production data, several models for natural gas consumption have been developed using linear regression, genetic programming and artificial neural network methods. The genetic programming approach outperformed linear regression and artificial neural networks.Key words: natural gas, consumption, modeling, linear regression, genetic programming, artificial neural networks, industry Izvleček V skladu s pravili Agencije republike Slovenije za energijo vsak dobavitelj regulira in določa stroške na račun razlik med naročeno in dejansko količino odjetega zemeljskega plina. Letni stroški zaradi teh razlika znašajo do 2% stroškov odjetega zemeljskega plina. Vsi odjemalci zemeljskega plina imajo tako težave s samim napovedovanjem odjema in posledično zmanjšanjem omenjenih stroškov. V članku je predstavljeno napovedovanje odjema zemeljskega plina v Štore Steel d.o.o. (železarna). Na podlagi podatkov o proizvodnji je bilo razvitih več modelov s pomočjo linearne regresije, genetskega programiranja in nevronskih mrež. Genetsko programiranje prekaša linearno regresijo in nevronske mreže.

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“…ANNs consist of a large number of processing elements called neurons that operate in parallel [6,7]. Computing with neural networks is non-algorithmic.…”

Section: Ann-based Prediction Of Natural Gas Consumptionmentioning

confidence: 99%

Natural gas consumption prediction in Slovenian industry – a case study

Kovačić

Šarler

Župerl

2016

Materials and Geoenvironment

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“…Zuperl et al (2012) proposed an adoptive control system by digital adoption of cutting parameters for controlling the cutting force and surface roughness being milled in 2012. Gokulachandran, and Mohandas introduced two software computing techniques for predictting the remaining uesful life of cutting tools in Apr.…”

Section: Literature Reviewmentioning

confidence: 99%

A network-based manufacturing model for spiral bevel gears

Deng

2015

J Intell Manuf

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Aimed at satisfying the development demands of modern manufacturing for high quality spiral bevel gears, it is thought to be urgent and crucial to improve their machining efficiency and quality. The continuous maturing and fusion of information and network technologies open a new route for the manufacture of spiral bevel gears. With the consideration of requirements for the fast growth of such industry, this paper proposes a network-based manufacturing model for spiral bevel gears by combining the technologies of gear manufacturing with network, information and management. Firstly, the application integration framework and network communication architecture for spiral bevel gears network-based manufacture is built. On that basis, the business model of integrated platform for spiral bevel gears network-based manufacture is established. Subsequently, the information integration framework of network-based manufacturing processes is set up. Finally, through the network-based manufacturing application experiment and effect analysis, the feasibility of the proposed model, as well as the effectiveness on improving the processing efficiency and quality of such gears is verified.

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“…One solution to compensate for the change of engagement is to control the feed rate. Several experiments were made in this field with a view to keeping the value of the material removal rate constant [20][21][22][23]; nevertheless, some of the associated problems have remained unsolved. On the one hand, the applied machine tool, when operating at slower and higher speeds, should be capable of reaching the desired speed within short periods of time [24], and, on the other hand, it must also be borne in mind that high engagements, which are sometimes used, may cause chatter and a thermal shock [25].…”

Section: Introductionmentioning

confidence: 99%

The fast constant engagement offsetting method for generating milling tool paths

Jacsó

Mátyási

Szalay

2019

Int J Adv Manuf Technol

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It is a well-established fact that when equidistant tool paths are used for 2.5D milling operations tool load varies based on the functions of path curvature. This phenomenon makes the optimal selection of cutting parameters more difficult, and the local load peaks are also detrimental to machining stability and tool life. Numerous publications address possible solutions to eliminate the problems caused by varied cutting parameters. The best solution for this is to keep the cutter engagement at a constant value. Nowadays, several methods are available to generate tool paths which are able to maintain constant cutter engagement, but the widespread use of such solutions is significantly hindered, because in this scenario complex calculations are required. This article offers a solution to this problem by presenting a new non-equidistant offsetting method for ensuring a constant cutter engagement angle. The algorithm developed for this purpose is based on simple geometrical equations and allows for its widespread use just like pixel-based methods. Concerning this new solution, computation needs and uniform tool load are verified by simulation and through experiments. The experiments have shown favourable results. Keywords Tool path generation. Cutter engagement. High-speed milling. Cutting force. Non-equidistant offsetting Nomenclature a e Effective radial immersion [mm] a p Axial depth of cut [mm] c(t) Parametric representation of workpiece contour [{mm, mm}] f z Feed per tooth [mm] h ex Maximum chip thickness [mm] i, j Step index [−] n Spindle speed [1/min] p(t) Parametric representation of tool path [{mm, mm}] r tool Tool radius [mm] s Stepover [mm] t Free parameter of curve equations [−] v Feed vector [{mm, mm}] v c Cutting speed [m/min] v f Feed rate [mm/min] w Trochoidal step [mm] z Number of teeth [−] C Entry point of the cutting edge [{mm, mm}] D tool Tool diameter [mm] MRR Material removal rate [mm 3 /min] P Tool path point [{mm, mm}] Q Exit point of cutting edge [{mm, mm}] V(x, y) Vector field [{mm, mm}, {mm, mm}] a, β Angle parameters [ ∘ ] Δs Stepsize[−] θ Cutter engagement angle [ ∘ ]

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Modeling and adaptive force control of milling by using artificial techniques

Cited by 47 publications

References 22 publications

Natural gas consumption prediction in Slovenian industry – a case study

Natural gas consumption prediction in Slovenian industry – a case study

A network-based manufacturing model for spiral bevel gears

The fast constant engagement offsetting method for generating milling tool paths

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