Multi-objective optimization of laser curve cutting of aluminium metal matrix composites using desirability function approach

Proceedings of the Institution of Mechanical Engineers, Part E:

2017

Self Cite

In the present work, the effects of input variables of laser beam machining on the machining characteristics of the metal matrix composites reinforced with SiC, Al 2 O 3 , and ZrO 2 particles were investigated. The comparison of the machining characteristics has been done to analyze the behavior of various reinforced particles with the variation of laser machining variables. Various output characteristics such as dross height, kerf deviation, and striations angle have been investigated and compared with each metal matrix composite material. Parameters such as cutting speed, reinforced particles, and cut profile were found to be the most significant factors influencing the various output characteristics. The morphological changes in the structure have been examined using scanning electron microscopy and X-ray diffraction technique for the agglomeration of the reinforced particles. The crack and recast layer formation has been examined in the specimens of higher quantity of the reinforced particles. It was analyzed that the metal matrix composites material reinforced with SiC particles has shown different behaviors as compared to other metal matrix composites materials.

Section: Resultsmentioning

confidence: 99%

Comparison of machining characteristics of metal matrix composites using CO₂ laser curve cutting process

Sharma

Proceedings of the Institution of Mechanical Engineers, Part E:

2017

Self Cite

“…A desirability function approach postulated by Harrington and later revitalized by Derringer and Suich was used to accomplish this objective [35,36]. This approach is commonly used to optimize the multiple output responses simultaneously [37][38][39][40][41]. In this approach, each of the response variable y i is normalized to a desirability value d i which lies in between 0 and 1.…”

Section: Multi-response Optimization Of Rt-musm Process With Wc Toolmentioning

confidence: 99%

Effect of tool materials on performance of rotary tool micro-USM process during fabrication of microchannels

J Braz. Soc. Mech. Sci. Eng.

Dvivedi

2019

The tool wear affects the efficiency of micro-USM process and the quality of machined micro-features. In this research endeavor, wear mechanism of soft/ductile and hard/brittle tool materials is investigated comprehensively. Stainless steel-304 (SS-304) and tungsten carbide (WC) were selected as tool materials for rotary tool micro-USM (RT-MUSM) process. The effect of tool material properties on tool wear and performance of RT-MUSM process is also discussed. The effect of RT-MUSM process parameters, viz. rotation speed, feed rate, power rating and slurry concentration on material removal rate (MRR), depth of channel (DOC) and width over cut (WOC) are reported. The experimental results showed that SS-304 tool worn out rapidly due to plastic deformation followed by strain hardening. The superior properties of WC such as high wear resistance, compressive strength and good acoustic property led to reduction in tool wear and thereby significantly improved the performance of RT-MUSM. Additionally, multi-response optimization was applied to obtain maximum MRR, DOC and minimum WOC simultaneously.

“…However, in real life situation, both these responses have to be optimized concurrently. Desirability function approach, originally postulated by Harrington in 1965, has been used to deal with such situations [41][42][43][44]. This concept was further revitalized by Derringer and Suich in 1980 [28].…”

Section: Parametric Optimization Using Desirabilitymentioning

confidence: 99%

Machining optimization in rotary ultrasonic drilling of BK-7 through response surface methodology using desirability approach

J Braz. Soc. Mech. Sci. Eng.

Singh

2018

In the present work, optical glass BK-7 was drilled by rotary ultrasonic machine. Response surface methodology was amalgamated with desirability approach to frame the experimental matrix and seek pragmatic solutions to cope with the real machining problems. Spindle speed, ultrasonic power and feed rate were extensively scrutinized to evaluate the machining proficiency in terms of material removal rate (MRR) and surface roughness (SR). Thereafter, ANOVA check was exercised to scrutinize the adequacy of developed SR and MRR models and cast light on the significant model terms with their impact intensity on responses. The feed rate was observed to be the most influential factor in determining the qualitative (SR) and quantitative (MRR) aspect of the machining process. The machined surface along with tool surface was examined by scanning electron microscope to shed light on the material fractography and tool wear. Processed surface topography revealed the concluding evidence of brittle fracture dominance along with few traces of plastic flow of material. Severe tool wear was observed in the initial stage of experimentation due to bond fracture and grain fracture. Confirmatory tests validated the prediction accuracy of developed models by keeping the error within 5% between the predicted and experimental value at 95% confidence level.