Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites

From last two decades, plant fiber reinforced polymer/polyester composites have been effectively used in structural and automotive applications. Researchers and manufacturers are looking forward for an effective utilization of these composites. However, despite the outstanding properties in terms of load bearing capacity and environmental sustainability of plant fibers the uptake of these composites are limited due to its poor machinability characteristics. Hence in this paper, Taguchi based fuzzy logic model for the optimization and prediction of process output variable such as surface roughness during Abrasive Water Jet Machining (AWJM) of new class of plant fiber reinforced polyester composites i.e., Discontinuously Reinforced Caryota Urens Fiber Polyester (DRCUFP) composites has been explored. Initially machining experiments has been carried out using L27 orthogonal array obtained from Taguchi Design of Experiments (TDOE). Finally, Taguchi based fuzzy logic model has been developed for optimisation and prediction of surface roughness. From the extensive experimentation using TDOE it was observed that the optimum cutting conditions for obtaining minimum surface roughness value, water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and Abrasive Size:200 microns. Further from FLM, it is observed that minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size:100 microns gave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size:200 microns the surface roughness values (3.25 microns).

Super fine cerium hydroxide abrasives for SiO2 film chemical mechanical planarization performing scratch free

Face-centered-cubic crystallized super-fine (~ 2 nm in size) wet-ceria-abrasives are synthesized using a novel wet precipitation process that comprises a Ce4+ precursor, C3H4N2 catalyst, and NaOH titrant for a synthesized termination process at temperature of at temperature of 25 °C. This process overcomes the limitations of chemical–mechanical-planarization (CMP)-induced scratches from conventional dry ceria abrasives with irregular surfaces or wet ceria abrasives with crystalline facets in nanoscale semiconductor devices. The chemical composition of super-fine wet ceria abrasives depends on the synthesis termination pH, that is, Ce(OH)4 abrasives at a pH of 4.0–5.0 and a mixture of CeO2 and Ce(OH)4 abrasives at a pH of 5.5–6.5. The Ce(OH)4 abrasives demonstrate better abrasive stability in the SiO2-film CMP slurry than the CeO2 abrasives and produce a minimum abrasive zeta potential (~ 12 mV) and a minimum secondary abrasive size (~ 130 nm) at the synthesis termination pH of 5.0. Additionally, the abrasive stability of the SiO2-film CMP slurry that includes super-fine wet ceria abrasives is notably sensitive to the CMP slurry pH; the best abrasive stability (i.e., a minimum secondary abrasive size of ~ 130 nm) is observed at a specific pH (6.0). As a result, a maximum SiO2-film polishing rate (~ 524 nm/min) is achieved at pH 6.0, and the surface is free of stick-and-slip type scratches.

Optimisation of Lapping Process Parameters for Single-Crystal 4H–SiC Using Orthogonal Experiments and Grey Relational Analysis

Lapping is one of the standard essential methods to realise the global planarization of SiC and other semiconductor substrates. It is necessary to deeply study the mechanism to obtain SiC lapping process parameters with a strong comprehensive lapping performance (i.e., high material removal rate (MRRm), small surface roughness (Ra), and low total thickness variation (TTV)). The effects of the lapping process parameters and their interactions on lapping performance for SiC were investigated using orthogonal experiments; the effects on the MRRm, Ra, TTV, and optimal parameters under the conditions of a single evaluation index were investigated using intuitive analysis (range analysis, variance analysis, and effect curve analysis). The entropy value method and grey relational analysis were used to transform the multi-evaluation-index optimisation into a single-index optimisation about the grey relational grade (GRG) and to comprehensively evaluate the lapping performance of each process parameter. The results showed that the lapping plate types, abrasive size, and their interaction effect had the most significant effects on MRRm and Ra, with a contribution of over 85%. The interaction between the lapping plate types and abrasive size was also found to have the most significant effect on TTV, with a contribution of up to 51.07%. As the lapping plate’s hardness and abrasive size increased, the MRRm and Ra also gradually increased. As the lapping normal-pressure increased, MRRm increased, Ra gradually decreased, and TTV first decreased and then increased. MRRm, Ra, and TTV first increased and then decreased with increasing abrasive concentration. Compared to the optimisation results obtained by intuitive analysis, the process parameter optimised by the grey relational analysis resulted in a smooth surface with an MRRm of 90.2 μm/h, an Ra of 0.769 nm, and a TTV of 3 μm, with a significant improvement in the comprehensive lapping performance. This study reveals that a combination of orthogonal experiments and grey relational analysis can provide new ideas for optimising the process parameters of SiC.

Application of response surface methodology to investigate nano-finishing of the silicon wafer

The aim of this research is study the effect of polishing factors to the reduction ratio rate in surface roughness (%[Formula: see text] during the Magnetic Abrasive Finishing (MAF) process using Response Surface Methodology (RSM). The parameters studied were machining gap, rotational speed, abrasive size and magnetic abrasive particle (MAP) size. Quadratic models were developed by applying Box–Behnken Design (BBD). Also, experiments were carried out on the silicon wafer and results of surface roughness data were analyzed by using analysis of variance (ANOVA) and significant factors were identified. According to our findings, the maximum %[Formula: see text] value and the best surface roughness of silicon wafer achieved 3.70 and 51 nm, respectively.

Abrasive wear response of Al-Si–SiCp composite: Effect of friction heat and friction coefficient

The effects of friction heat and friction coefficient on the abrasive wear response of Al-7.5Si–SiCp composite against low-cost hypereutectic (Al-17.5Si) alloy were investigated as functions of the abrasive size and applied load in both as-cast and after heat-treatment conditions. Experiments were performed on pin-on-disc apparatus at 38 –80 μm abrasive size, 5 – 20 N applied load, 100 –400 m abrading (sliding) distances and 1 m s–1 constant sliding speed. The frictional heating of as-cast and heat-treated composite was superior compared to the matrix alloy and hypereutectic alloy, whereas the trend reversed for the friction coefficient. The frictional heating and friction coefficient of the materials increased with the abrasive size and applied load in both as-cast and after heat-treatment. The worn surface and wear debris particles were examined by using field emission scanning electron microscopy to understand the wear mechanism.

High-efficiency nano polishing of steel materials

The application of a specific rheological polishing slurry is proposed first for high-efficiency machining of steel materials to achieve high-quality ultraprecision finished surfaces. The rheology of the polishing slurry was explored to show that the non-Newtonian medium with certain parameters of content components exhibits shear-thickening behavior. Then the new high-efficiency nano polishing approach is applied to process spherical surfaces of bearing steel. Several controllable parameters such as shear rheology, abrasive data, rotational speed, and processing time are experimentally investigated in this polishing process. A special finding is that the surface roughness and material removal rate can increase simultaneously when a small abrasive size is applied due to the thickening mechanism during the shearing flow of slurries. Excessive abrasives can decrease surface quality due to the uneven agglomeration of particles scratching the surface. Under optimized conditions, a high-accuracy spherical bearing steel surface with a roughness of 12.6 nm and roundness of 5.3 μm was achieved after a processing time of 2.5 h. Thus, a potential ultraprecision machining method for target materials is obtained in this study.

The Surface Condition of Ni-Cr after SiC Abrasive Blasting for Applications in Ceramic Restorations

Abrasive blasting is a process widely used in dentistry. One of the uses is the development of metal surfaces for connections with ceramics in fixed prosthetic restorations. The purpose of this paper was to check how the rough surface profile (width, height, and depth on unevenness) impacts the surface’s condition, like its wettability and percentage of stuck abrasives. The Ni-Cr alloy surface was abrasive blasted by silicon carbide with the various pressure parameters (0.2, 0.4, and 0.6 MPa) and abrasive particle sizes (50, 110, and 250 µm). Cleaned surfaces were examined for roughness, wettability, and percentage of stuck abrasive particles on the surface. The surface after abrasive blasting using 110 µm of abrasive size and 0.4 MPa pressure has the best wettability results. The width of unevenness may cause it. When the unevenness has too small or too large width and depth, the fluids may not cover the entire cavities because of locking the air. The surface condition of dental alloys directly affects metal–ceramic connection strength. The knowledge about the impact of the abrasive blasting parameters on the bond strength will allow one to create durable dental restorations.