An optimization paradigm based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations is proposed. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. In order to find optimal cutting parameters during a turning process, the genetic algorithm has been used as an optimal solution finder. Process optimization has to yield minimum production time, while considering technological and material constrains

This paper presents a finite-element modeling (FEM) of tool temperature distribution during high pressure coolant assisted turning of Inconel 718, which belongs to the heat resistance superalloys of the Nickel-Chromium family. Machining trials were conducted under four machining conditions: dry, conventional wet machining, high pressure coolant at 50 bar, and high pressure coolant at 80 bar. Temperature during machining plays a very important role in the overall performance of machining processes. Since in the current investigation a high pressure coolant jet was supplied in the cutting zone between tool and work material, it was a very difficult task to measure the tool temperature correctly. Thus, FEM was used as a modeling tool to predict tool temperature. The results of the modeling showed that the temperature was considerably influenced by coolant pressure: the high pressure jet was able to penetrate into the interface between tool and work material, thus providing both an efficient cooling and effective lubricating action.

Porous structures exhibiting randomly sized and distributed pores are required in biomedical applications (producing implants), materials science (developing cermet-based materials with desired properties), engineering applications (objects having controlled mass and energy transfer properties), and smart agriculture (devices for soilless cultivation). In most cases, a scaffold-based method is used to design porous structures. This approach fails to produce randomly sized and distributed pores, which is a pressing need as far as the aforementioned application areas are concerned. Thus, more effective porous structure design methods are required. This article presents how to utilize fractal geometry to model porous structures and then print them using 3D printing technology. A mathematical procedure was developed to create stochastic point clouds using the affine maps of a predefined Iterative Function Systems (IFS)-based fractal. In addition, a method is developed to modify a given IFS fractal-generated point cloud. The modification process controls the self-similarity levels of the fractal and ultimately results in a model of porous structure exhibiting randomly sized and distributed pores. The model can be transformed into a 3D Computer-Aided Design (CAD) model using voxel-based modeling or other means for digitization and 3D printing. The efficacy of the proposed method is demonstrated by transforming the Sierpinski Carpet (an IFS-based fractal) into 3D-printed porous structures with randomly sized and distributed pores. Other IFS-based fractals than the Sierpinski Carpet can be used to model and fabricate porous structures effectively. This issue remains open for further research.