This chapter intends to develop a design process for service life prediction of machines and structures.
The generalized model allows unified prediction of machining operations with one mathematical model which covers all operations and tool geometries.
This paper proposes the development of neural network models for prediction of machining parameters in CNC turning process.
The prediction of machining stability is of great importance for the design of a machine tool capable of high-precision and high-speed machining.
This experimental study focuses on the prediction of machining parameters that yield better surface characteristics in order to avoid machining of hard materials such as fiber reinforced composite materials so that enormous money spent in machining could be saved to some extent.
For the performance prediction of machining system, it is important to provide the expressions for key indices including the queue length.
Hence, the prediction outputs of machine learning-based models have higher preference to match the distribution of the training set, resulting in a relatively lower generalization and coverage of the predictions.
The input is a description of a machine's geometry and material properties, and the output is a behavioral model for the machine and a concise qualitative/quantitative prediction of the machine's long-term behavior.
By including the F-policy Kumar and Jain (2013a) developed a queueing model for the performance prediction of the machine repair problems with standbys.
In recent years, many researchers also contributed to study on the performance prediction of the machine repair problems with standbys by incorporating some other distinct features like vacation, heterogeneous repairmen, N-policy, etc. (see Ke et al. 2009; Maheshwari et al. 2010; Jain et al. 2012).
