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8 Results Found

  • Article
  • Open Access
10 Citations
5,849 Views
16 Pages

Minimum Quantity Lubrication (MQL) Supply through Internal Cooling Channels in Drilling Processes

  • Lukas Schumski,
  • Teresa Tonn,
  • Jens Sölter,
  • Kerstin Avila and ,
  • Lizoel Buss,
  • Bernhard Karpuschewski and
  • Udo Fritsching
  • + 2 authors+ 0 author

Minimum quantity lubrication (MQL) technologies possess great potential for improving the sustainability of manufacturing processes, which can reduce the absolute quantity of metalworking fluid (MWF) and also enable near-dry chips that are easier to...

  • Article
  • Open Access
192 Views
39 Pages

8 October 2026

The objective of this study is to clarify the influence of MQL lubricant-delivery directionality on the thermo-mechanical response of Inconel 718 milling by systematically comparing unidirectional and multidirectional lubricant supply configurations...

(This article belongs to the Topic Surface Modification and Durability Enhancement of Advanced Alloys)
  • Article
  • Open Access
18 Citations
2,933 Views
20 Pages

11 February 2022

This paper describes the research on abrasive machining conditions and their influence on microhardness and residual stresses distribution in the technological surface layer of 20MnCr5 steel. The roughness of ground samples was also measured. Samples...

(This article belongs to the Special Issue Innovative and Modern Technologies of Material Machining in Cutting and Abrasive Processes)
  • Article
  • Open Access
2,319 Views
9 Pages

16 April 2020

Drilling work on Inconel, a well-known super-heat-resistant alloy, requires a significant quantity of cutting oil to prevent heat-induced abrasion and damage to the cutting tool, caused by the strength and toughness of the alloy. This high requiremen...

(This article belongs to the Section Mechanical Engineering)
  • Article
  • Open Access
41 Citations
5,868 Views
30 Pages

Hard Turning Performance Investigation of AISI D2 Steel under a Dual Nozzle MQL Environment

  • Rajashree Mallick,
  • Ramanuj Kumar,
  • Amlana Panda and
  • Ashok Kumar Sahoo

In recent years, hard turning has emerged as a burgeoning cutting technology for producing high-quality finishing of cylindrical-shaped hardened steel for a variety of industrial applications. Hard turning under dry cutting was not accepted because o...

(This article belongs to the Special Issue Minimum Quantity Lubrication: Environmental Alternatives in Processing)
  • Article
  • Open Access
8 Citations
3,486 Views
25 Pages

31 May 2020

This paper describes simulation studies regarding the application of the centrifugal minimum quantity lubrication (MQL) method simultaneously with the delivery of a compressed cooled air (CCA) stream in the internal cylindrical grinding process. The...

(This article belongs to the Section Manufacturing Processes and Systems)
  • Article
  • Open Access
3 Citations
2,314 Views
20 Pages

Assessing Nitrosamine Migration from Drinking Water Contact Materials Using a Validated LLE-GC-MS Method

  • Beatriz Antunes,
  • Andreia Videira,
  • Ana Penetra,
  • Vitor V. Cardoso and ,
  • Rui N. Carneiro and
  • Cristina M. M. Almeida
  • + 1 author+ 0 author

Nitrosamines (NAs) are toxic compounds associated with disinfection processes. Human exposure can occur through the hydraulic hoses and seals that are in contact with drinking water. This study develops and validates a chromatographic method to quant...

  • Article
  • Open Access
563 Views
26 Pages

Multi-Source Data-Driven Day-Ahead Load Forecasting for a Provincial Power Grid: A Case Study in Hunan, China

  • Changlong Wu,
  • Rui Xiang,
  • Jun Huang,
  • Xueshan Ai and ,
  • Qihang Gong,
  • Yangxin Yu and
  • Hao Hu
  • + 2 authors+ 0 author

22 September 2026

Accurate day-ahead load forecasting at the provincial scale is a basic input to unit commitment, reserve scheduling and market settlement, but the forecast has to capture periodic load behaviour, spatially distributed weather sensitivity and historic...

(This article belongs to the Special Issue Forecasting Electricity Demand Using AI and Machine Learning)