Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA

Abstract

The manuscript discusses the abrasive water jet machining (AWJM) of Al-NiTi and Al-NiTi-nano SiC composites to understand the influences and the effect of each parameter and to indentify optimal combination of AWJM parameters. The experiments are planned and conducted based on L27 orthogonal array. Pressure, standoff distance, and transverse feed rate are considered as input parameters; surface roughness and kerf angle are considered as output parameters. Gravitational search algorithm (GSA) is employed to identify the best possible combination of AWJM parameters. Regression model is used to develop the surface roughness and kerf angle model for Al-NiTi and Al-NiTi-nano SiC composites. The developed mathematical model is used as fitness function in GSA. It is found from the result of GSA that the optimal value for surface roughness of Al-NiTi composite is set at pressure 176 kPa, standoff distance 1 mm, and transverse feed 20 mm/min and for Al-NiTi-nano SiC composite is set at pressure 180 kPa, standoff distance 1.1 mm, and transverse feed 20 mm/min. Similarly, for kerf angle, the optimal value for Al-NiTi composite is set at pressure 260, standoff distance 1 mm, and transverse feed 20 mm/min and for Al-NiTi-nano SiC composite is set at pressure 255 kPa, standoff distance 1 mm, and transverse feed 20 mm/min. Analysis of variance is also performed to understand the effect of each input parameter on output response.

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

“…Chakraborty and Mitra (2018) have applied a grey wolf optimizer for parametric optimization of AWJM processes, and demonstrated its potentiality through comparison of results applying other algorithms. Purusothaman et al (2017) have adopted the gravitational search algorithm for parametric optimization and performed ANOVA to examine the significance of AWJM process parameters (namely, pressure, stand-off distance and traverse feed rate) on the output responses (namely, surface roughness (Ra) and kerf angle) of Al-NiTi and Al-NiTi-nano SiC composites.…”

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

“…(2018) employed the Taguchi-DEAR (data envelopment analysis-based ranking) methodology to study the impact of process parameters on the machining of Al7075 composites reinforced with TiB2 particles during abrasive water jet machining (AWJM). Various other AWJM processes have identified optimal parameters using algorithms such as cohort intelligence (CI) (Gulia and Nargundkar, 2019), multi-objective cuckoo search (MOCS) (Qiang et al ., 2018), artificial bee colony (ABC) (Pawar et al ., 2018), Jay algorithm (Venkata Rao, 2019), multi-objective optimization by ratio analysis (MOORA) (Kalirasu et al ., 2017), Gray wolf optimizer (GWO) (Chakraborty and Mitra, 2018), gravitational search algorithm (GSA) (Mokkandi et al ., 2017), response surface methodology (RSM) (Ma et al ., 2020), artificial neural network (ANN) (Gong et al ., 2022; Sing et al ., 2021), as well as Taguchi method and evolutionary optimization (Shukla and Singh, 2017).…”