Influence of Machining Parameter on Tool Life While Machining Hybrid Metal Matrix Composites

A metal matrix composite constitutes a continuous metallic matrix and a discontinuous phase known as reinforcement. The hybrid metal matrix composites (Hmmcs) have been used to manufacture drive shafts, disc brake rotors, brake drums, connecting rods pistons, engine block cylinder liners for automotive and rail vehicle applications. The Hmmcs castings of diameter 120 mm and length 300 mm were prepared through sand mould technique following stir casting methodology. The cast components further subjected to evaluation of physical properties and machining tests using two grades of coated inserts and PCD inserts. The experiments were carried out following ISO 3685 standards. The coating thickness of the TiN coated and TiAlN coated inserts were measured using Kalo testing method; the results of the test show that the interface of the substrate and coating was free from the porosity, and the coating thickness of TiN coating was 4.84 microns and TiAlN coating was measured 4.6 microns. The results of the experiments show that performance of the PCD insert was better than coated inserts at 0.1 mm/rev feed; however at 0.2 mm/revolution feed PCD insert failed by micro chipping of cutting edge while machining Hmmcs. When TiAlN coated inserts were used to machine Hmmcs the coated inserts failed by gradual wear and BUE formation.

Influence of Machining Parameter on Surface Roughness and Tool Life While Machining EN24 Grade Alloy Steel

The requirements of high quality machined surface as well as demand of enhanced contact time of cutting tools drive towards adopting multilayer coated carbide inserts. The industry requires higher productivity, hence higher machining parameters need to be used in order to meet the industry requirements. The alloy steel material used to fabricate machine parts consists of alloying elements like nickel, chromium and molybdenum difficult to machine, since the cutting tool fails by high tool wear, if we use uncoated carbide inserts to machine alloy steels. Hence in the present research work it is intended to use tungsten carbide inserts coated with different coatings for the experiments. The turning experiments were carried out using different grades of uncoated and coated carbide inserts of identical tool signature. The cutting speed selected for the experiments was 100 to 500 m/min in steps of 100 m/min, and the feed per revolution was 0.1 mm to 0.4 mm in step of 0.1 mm. The experimentation was carried out following ISO3685 standards. The results of the experiments revealed that the surface roughness measured was the least at cutting speed 500 m/min and feed per revolution of 0.1 mm, however the chip breaking found better when the feed used was greater than 0.2 mm/revolution.

Automatically Identifying Calling-Prone Higher-Order Functions of Scala Programs to Assist Testers

For the rapid development of internetware, functional programming languages, such as Haskell and Scala, can be used to implement complex domain-specific applications. In functional programming languages, a higher-order function is a function that takes functions as parameters or returns a function. Using higher-order functions in programs can increase the generality and reduce the redundancy of source code. To test a higher-order function, a tester needs to check the requirements and write another function as the test input. However, due to the complex structure of higher-order functions, testing higher-order functions is a time-consuming and labor-intensive task. Testers have to spend an amount of manual effort in testing all higher-order functions. Such testing is infeasible if the time budget is limited, such as a period before a project release. In practice, not every higher-order function is actually called. We refer to higher-order functions that are about to be called as calling-prone ones. Calling-prone higher-order functions should be tested first. In this paper, we propose an automatic approach, namely PHOF, which predicts whether a higher-order function of Scala programs will be called in the future, i.e., identifying calling-prone higher-order functions. Our approach can assist testers to reduce the number of higher-order functions of Scala programs under test. In PHOF, we extracted 24 features from source code and logs to train a predictive model based on known higher-order function calls. We empirically evaluated our approach on 4832 higher-order functions from 27 real-world Scala projects. Experimental results show that PHOF based on the random forest algorithm and the Synthetic Minority Oversampling Technique Processing strategy (SMOTE) performs well in the prediction of calls of higher-order functions. Our work can be used to support the scheduling of limited test resources.