Modeling and optimization of machining parameters during grinding of flat glass using response surface methodology and probabilistic uncertainty analysis based on Monte Carlo simulation

Comparative Assessment of Wear Indicators in Grinding Discs During Grinding of Selected Steels

“…On the basis of conducted tests, a comparative assessment of wear indicators of grinding wheels was found:  during the tests of wheels used for metal grinding, it was found that S355 steel is the most susceptible to abrasive machining among the four grinded materials (Fig. 3),  the lowest grinding efficiency of S355 steel was obtained for a C wheel (525 g) of 15 minutes process, while the highest efficiency for wheel I (1155 g),  NC6 steel and Hadfield steel are characterized by high resistance to abrasion, and the effectiveness of their abrasion is the lowest for all tested grinding wheels,  when grinding S235 steel, the best KG coefficient is distinguished by the wheel I (Fig. 4).…”

“…This process is being studied by many researchers [1][2][3][4][5][6][7]. Special interest concern the process of wear of the cutting discs and their reducing [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Cutting discs are mostly assembled for angle grinders.…”

“…The hardness of cutting discs is influenced by the type of abrasive grain used and the type of binder used [16][17][18][19][20][21][22][23][24][25]. Modeling of the cutting processes allows to optimize the selection of tools, their parameters and technological conditions [8][9][10][11][12][13][14][15]. Among the abrasive grains, you can highlight the grain of natural origin, that is [1]: diamond, corundum, emery.…”

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

“…The ability to predict surface roughness before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction of surface roughness is currently determined by using various techniques such as theoretical models [ 3 , 32 , 33 , 34 , 35 ], response surface methodology (RSM) [ 3 , 6 , 9 , 36 , 37 , 38 ], the Taguchi procedure [ 3 , 6 , 28 , 38 , 39 , 40 , 41 , 42 , 43 ], multiple linear regression equations [ 44 ], the Monte Carlo (MC) method [ 7 , 24 , 33 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ], artificial intelligence through the use of the artificial neural networks (ANNs) [ 1 , 3 , 26 , 29 , 30 , 53 , 54 , 55 , 56 ], genetic algorithms (GAs) [ 3 , 57 ], fuzzy logic (FL) [ 3 , 36 , 54 , 58 , …”

“…Many research works show the use of these methods in the forecasting and optimization of surface roughness [ 3 ]. Researchers usually do not use only one modeling approach in their works, but look for a mutual compilation of the above strategies [ 3 , 6 , 36 , 37 , 38 , 39 , 54 , 59 ]. The benefits of using surface roughness prediction methods include an increase in the productivity and competitiveness of the production process and a simultaneous reduction in the need to re-machine a material to meet technical requirements [ 3 , 9 , 10 , 24 ].…”