Evaluation of a cooled wheel cleaning jet in minimum quantity lubrication grinding process
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ORIGINAL ARTICLE
Evaluation of a cooled wheel cleaning jet in minimum quantity lubrication grinding process Rafael Liberatti Javaroni 1 & José Claudio Lopes 1 & Fernando Sabino Fonteque Ribeiro 2 & Mateus Vinicius Garcia 1 & Luiz Eduardo de Angelo Sanchez 1 & Hamilton José de Mello 1 & Paulo Roberto Aguiar 3 & Eduardo Carlos Bianchi 1 Received: 11 June 2020 / Accepted: 29 September 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract The application of the minimum quantity lubrication (MQL) technique in grinding processes is still limited due to the frequent occurrence of overheating and wheel clogging. In this context, this research evaluates a cooled wheel cleaning jet (CWCJ) under different temperatures, applied simultaneously to MQL in the cylindrical grinding of AISI 4340 quenched and tempered steel, using a CBN wheel. For comparison, tests with the MQL, MQL + WCJ, and conventional techniques were also performed. Surface roughness, microhardness, and roundness deviation of the workpieces, wheel wear, and grinding power were the assessed output parameters. The machined workpieces were evaluated by optical, confocal, and scanning electron microscopies. The MQL + WCJ outperformed MQL and MQL + WCJ in all the tested conditions. The application of the cooled wheel cleaning jet reduced by up to 79% the surface roughness, 82% the wheel wear, 76% the roundness deviation, and 37% the grinding power, regarding MQL and MQL + WCJ techniques, contributing towards a sustainable and high-quality grinding process. Keywords Plunge cylindrical grinding . Cooled wheel cleaning jet . Minimum quantity lubrication . Surface integrity
1 Introduction The global industrial scenario is undergoing increasing changes in terms of efficiency and productivity [1]. New technologies have provided a reduction in manufacturing time, helping to reduce costs and develop complex mechanical components [2]. In this context, industry 4.0 focuses on the automation of manufacturing processes [3], through the use of technologies such as artificial intelligence in manufacturing [4], Industrial Internet of Things (IIoT) [5], cyber-physical systems [6], big
data analytics [1], and cloud computing [2]. The utilization of microsensors connected to acquisition systems and databases can promote a self-adaptive machining operation, in which the machine tool can automatically act on the machining parameters and propose the best-operating conditions [1–6]. However, even with all the advances related to the implementation of these new technologies, the chip formation mechanisms will remain the same, generating friction and heat during the machining process [7, 8], thus maintaining the need to use cutting fluids for lubrication, cooling, and cleaning the
* Eduardo Carlos Bianchi [email protected]
Hamilton José de Mello [email protected]
Rafael Liberatti Javaroni [email protected] José Claudio Lopes [email protected] Fernando Sabino Fonteque Ribeiro [email protected]
Paulo Roberto Aguiar paulo.aguiar@u
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