Fatigue life of machined components

A correlation between machining process and fatigue strength of machined components clearly exists. However, a complete picture of the knowledge on this is not readily available for practical applications. This study addresses this issue by investigating the effects of machining methods on fatigue life of commonly used materials, such as titanium alloys, steel, aluminium alloys and nickel alloys from previous literature. Effects of turning, milling, grinding and different non-conventional machining processes on fatigue strength of above-mentioned materials have been investigated in detail with correlated information. It is found that the effect of materials is not significant except steel in which phase change causes volume expansion, resulting in compressive/tensile residual stresses based on the amounts of white layers. It is very complex to identify the influence of surface roughness on the fatigue strength of machined components in the presence of residual stresses. The polishing process improves the surface roughness, but removes the surface layers that contain compressive residual stresses to decrease the fatigue strength of polished specimens. The compressive and tensile residual stresses improve and reduce fatigue strength, respectively. Grinding process induces tensile residual stresses on the machined surfaces due to high temperature generation. On the other hand, milling and turning processes induce compressive residual stresses. High temperature non-conventional machining generates a network of micro-cracks on the surfaces in addition to tensile residual stresses to subsequently reduce fatigue strength of machined components. Embedded grits of abrasive water jet machining degrade the fatigue performance of components machined by this method

Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel

Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions

Energy efficiency of machining operations: A review

Manufacturing processes are among the most energy intensive on earth. As negative ecological and economic impacts increase, reducing energy consumption is becoming critically important. In this article, a comprehensive overview of energy-saving strategies and opportunities for increasing energy efficiency in manufacturing operations is presented, with a focus on metal cutting processes. The issues and approaches involved in energy efficiency of machine tools and machining operations are reported in the literature and a structured research methodology is proposed for this purpose including prediction and modelling of machine energy consumption, determining the relationship between process energy consumption and process variables for material removal processes and optimization of cutting parameters in order to reduce energy consumption. Numerous techniques for increasing energy efficiency in manufacturing processes are identified and summarized, strengths and weaknesses of previous studies are discussed and potential avenues for future research are suggested.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by SAN-TEZ Project No. 00979.stz.2011-12 of the Turkish Ministry of Science, Technology and Industry