Application of optimization techniques in metal cutting operations: A bibliometric analysis

Bibliometric analysis focuses on the statistical analysis of publications published in a particular area. This method is used to classify the information with variables i.e. journals, institutions, authors and countries. This paper present the general overview of research that has been reported in the optimization techniques in various metal cutting operations. Optimization is becoming popular concept in the present time with its most common goal of optimizing the system by smarter use of both products and services. Optimization techniques are very popular in manufacturing industries as it is leads to time–cost savings, waste reduction and increased the quality level with higher customer satisfaction. These days optimization with the help of traditional approaches and machine learning approaches have become popular to achieve the sustainability in the manufacturing practices. The aim of present research work is to investigate the systematic literature review on optimization techniques applications in the cutting processes within the sustainable manufacturing context. This study reports the 20 years of bibliometric analysis of optimization techniques used in the metal cutting operations. The bibliometric analysis is done by using Scopus database with from the time period of 2000–2020. Keyword co-occurrence is found out with the help of network analysis. Top authors, institutes, countries and publication trends in cutting processes are investigated. It is found that majority of machine learning techniques have been applied in milling and turning applications. Optimization with machine learning techniques has enhanced the research area of metal cutting in last five years. Emerging economies like India and China are more focused towards the adoption of new optimization techniques in the machining area.