Titlebook: Intelligent Manufacturing; Sunil Pathak Book 2021 Springer Nature Switzerland AG 2021 Intelligent manufacturing.optimization.Soft computin

Physiatrist

Kurtosis Analysis of Tool Drilling Geometries and Cutting Conditions for Deep Twist Drilling Procesimal tool geometries are sharp point angle 115°, widen helix angle 35° and balanced clearance angle 120° from minimum kurtosis acceleration analysis and verified with signal processing analyses. The optimum tool geometries with process parameters can be determined based on minimum kurtosis value in

Chronic

,Development of Ti50Ni50−XCox (X = 1 and 5 at. %) Shape Memory Alloy and Investigation of Input Proc of TiNiCo alloy to achieve a higher metal removal rate with a better surface finish. Response surface design (DOE) is applied for an experimental plan and created L-33 orthogonal array. Machining has been carried out of TiNiCo alloy as per the experimental plan and measured the machining responses

艺术

Application Potential of Fuzzy Embedded TOPSIS Approach to Solve MCDM Based Problems, health care, business and manufacturing etc. This chapter highlights the utilization potential of Fuzzy embedded TOPSIS approach in clarifying different MCDM-based obstacles correlated witha real time manufacturing system and other relevant sectors as well. A case study consisting of experimental d

保守

Multi-criteria Decision Making Through Soft Computing and Evolutionary Techniques,meter determination and prediction of results in machinability studies. Throughout this chapter, research based on the studies on multi-criteria decision-making tools is discussed. Moreover, various characteristics and difference among these tools are also reported.

ENACT

天然热喷泉

Hybrid Approach for Prediction and Modelling of Abrasive Water Jet Machining Parameter on Al-NiTi Cveness of the hybrid algorithm in predicting and optimizing the Abrasive Water Jet Machining (AWJM) parameters. Grey Relational Analysis (GRA) is used as a feature selection and optimizing tool. The result of feature selection by GRA–Entropy, reveals that the most influencing control variables are r

低能儿

Hargovind Soni,S. Narendranath,M. R. Ramesh,Dumitru Nedelcu,P. Madindwa Mashinini,Anil Kumar

尾巴

健谈

远足