Fundamentals of Modern Manufacturing: Materials, Processes and Systems

Cloud-enabled prognosis for manufacturing

Classical multivariate statistical methods concern models, distributions and inference based on the Gaussian distribution. These are the topics in the first textbook for mathematical statisticians by T.W. Anderson that was published in 1958 and that appeared as a slightly expanded 3rd edition in 2003. Matrix theory and notation is used there extensively to efficiently derive properties of the multivariate Gaussian or the Wishart distribution, of principal components, of canonical correlation and discriminant analysis and of the general multivariate linear model in which a Gaussian response vector variable Ya has linear least-squares regression on all components of an explanatory vector variable Yb. In contrast, many methods for analysing sets of observed variables have been developed first within special substantive fields and some or all of the models in a given class were justified in terms of probabilistic and statistical theory much later. Among them are factor analysis, path analysis, structural equation models, and models for which partial-least squares estimation have been proposed. Other multivariate techniques such as cluster analysis and multidimensional scaling have been often used, but the result of such an analysis cannot be formulated as a hypothesis to be tested in a new study and satisfactory theoretical justifications are still lacking. Factor analysis was proposed by psychologist C. Spearman (1904), (1926) and, at the time, thought of as a tool for measuring human intelligence. Such a model has one or several latent variables. These are hidden or unobserved and are to explain the observed correlations among a set of observed variables, called items in that context. The difficult task is to decide how many and which of a possibly large set of items to include into a model. But, given a set of latent variables, a classical factor analysis model specifies for a joint Gaussian distribution mutual independence of the observed variables given the latent variables. This can be recognized to be one special type of a graphical Markov model; see Cox and Wermuth (1996), Edwards (2000), Lauritzen (1996), Whittaker (1990).

Multivariate Statistical Analysis.

Ecological trends in machining as a key factor in sustainable production – A review

The researchers have worked on many facets of machining of hardened steel using different tool materials and came up with their own recommendations. Researchers have tried to investigate the effects of cutting parameters, tool materials, different coatings and tool geometry on different machinability aspects like, the tool life, surface roughness, cutting forces, chip morphology, residual stresses and the tool–chip interface temperature under dry and/or semi-dry and/or flood cooling environment during machining of hardened steels while many of them have ventured to characterize the wear phenomenon. Good amount of research has been performed on an analytical and/or numerical and/or empirical modeling of the cutting forces, tool–chip interface temperature, and tool wear under orthogonal/oblique cutting conditions during machining of hardened steels. This paper presents a comprehensive literature review on machining of hardened steels using coated tools, studies related to hard turning, different cooling methods and attempts made so far to model machining performance(s) so as to give proper attention to the various researcher works.

Machining of hardened steel—Experimental investigations, performance modeling and cooling techniques: A review

An experimental investigation of the effect of coating material on tool wear in micro milling of Inconel 718 super alloy

Tool life and wear mechanism of coated and uncoated Al2O3/TiCN mixed ceramic tools in turning hardened alloy steel

Exp. (experimental) and num. (numerical) cutting forces were obtained by the exp. studies and FEM analysis.The best convergence between exp. and num. cutting forces was provided.Num. cutting temperatures were predicted by ANN within very low error interval.Exp. cutting temperatures were obtained using exp. cutting forces.Exp. temperature results were quite satisfactory. In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN+Al2O3+TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson-Cook (J-C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

A study of spur gear pitting formation and life prediction

Metastable β titanium alloys are widely used in the biomedical, automotive, and aerospace industry, due to their excellent corrosion resistance, fatigue strength, biocompatibility, and easy formability. Besides all these use areas, the suitable microstructure of the alloys for heat treatment increases the efficient usability day by day. In literature research, it has been found that heat treatment types such as cryogenic treatment and precipitation hardening can be applied efficiently to the alloys. Optimum strength/ductility, wear resistance, creep strength, and fatigue strength can be obtained with these heat treatments. For this reason, it has become important to understand the effect of heat treatments on the microstructural and mechanical properties of the alloys and parameters affecting this heat treatment efficiency. Precipitation hardening includes solution and aging treatment steps. The solution treatment can be applied at temperatures below and above the β transition temperature. While the aging treatment can be applied in four different ways, in the review article, the effects of single step and duplex aging treatment, which are applied with high efficiency, are emphasized. Precipitation hardening efficiency affects the chemical composition of the alloys, heat treatment steps, treatment temperature and times, and heating/cooling rate. The cryogenic treatment provides the formation of martensite α phases in metastable β titanium alloys cooled below the martensitic transformation temperature. Higher-strength and hardness have been obtained in the studies where aging treatment was applied after cryogenic treatment. Cryogenic treatment efficiency determines chemical composition of the alloys, treatment temperature and time, the heating/cooling rates, and heat treatments applied after cryogenic treatment.

Kubilay Aslantaş

Papers

An experimental investigation of the effect of coating material on tool wear in micro milling of Inconel 718 super alloy

Tool life and wear mechanism of coated and uncoated Al2O3/TiCN mixed ceramic tools in turning hardened alloy steel

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

A study of spur gear pitting formation and life prediction

A review on heat treatment efficiency in metastable β titanium alloys: the role of treatment process and parameters