Energy consumption model for cutting operations in a stochastic environment
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ORIGINAL ARTICLE
Energy consumption model for cutting operations in a stochastic environment Mariangela Quarto 1
&
Gianluca D’Urso 1
&
Claudio Giardini 1
Received: 10 June 2020 / Accepted: 9 September 2020 / Published online: 15 September 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Nowadays, everyone agrees that it is urgent to reduce the consumption of energy and raw materials when manufacturing industries are concerned. Among all the transformation technologies, those related to chip removal are particularly interesting because of the high volume of processed material and because the final quality of the products largely depends on the fact that these processes correspond to the final stages of the production chain. Compromising the quality of the piece at this stage means not only discarding the piece but also losing the energy used to prepare the raw piece and to carry out the previous processes. Since unsuitable use of productive resources leads to a waste of time and money, in the past, many researchers have been developing models to optimize production processes by maximizing productivity and/or minimizing costs. Today, however, it is necessary to optimize the same processes from the total energy consumption point of view. Many authors already addressed this problem using a deterministic approach, when trying to identify the optimal cutting conditions. This means that tools are considered to be completely reliable elements in the production processes. The present work proposes an alternative methodology based on a stochastic approach to describe the tool resource; this approach is able to take into consideration the actual resources reliability and the consequent penalties deriving from their unpredicted failure, occurring before the expected replacement time. Keywords Sustainable cutting . Stochastic environment . Optimization
Nomenclature ap Depth of cut C60 Cutting speed for a unitary tool life Ctool Cost of the tool in terms of insert and tool stem Cwork Total machining cost per unit of time F(α) Cumulative function distribution of tool life E(N) Average number of parts that can be cut by one tool in the stochastic environment E Total energy consumption to complete the production
* Gianluca D’Urso [email protected] Mariangela Quarto [email protected] Claudio Giardini [email protected] 1
Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7/b, 24044 Dalmine, BG, Italy
Eaux Ec Efluid Epen Erough1 Es Etc Etool1 f f(h) h hα hα, j hi hm hm, k
Energy for auxiliary operations at the end of the production Cutting energy Energy for lubricant flush Energy penalty due to the premature tool breakage (its life is less than hα) Energy required for producing one rough part Set-up energy Tool change energy Energy required for producing one tool Feed rate Probability density function Generic tool life in the stochastic environment Expected tool life corresponding to R(α) reliability Tool substitution interval
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