The OGC SWE standard Sensor Model Language (SensorML) provides a means to describe sensors and processes in machine-harvestable encodings [2].
Interdisciplinary modular concept is a solution to optimize the automation engineering process in machine and plant manufacturing.
It avails machining process in machining cost, high quality, less down machining time, lower investment, and smaller tooling inventory while get rid of non-value added activities.
Recently, fuzzy logic principles have been applied for selecting cutting conditions in the turning process in machining operations [Hashmi, EL Baradie, Ryan, Fuzzy logic based intelligent selection of machining parameters, in: Proceedings of 22nd Conference on Computers and Industrial Engineering (ICC&IE), The American University in Cairo, Egypt, December 1997, pp 309 312].
The specific value of a minimum acceptable MR mass charge for the investigated system and its correlation with internal processes in the machine loop are established as well.
This paper introduces a novel approach for measurement process planning in machine tool assembly by applying the observability principle of state space model which is widely used in control engineering.
In this article, we primarily adopt the token-view of information, the predominant view within information systems research: information is seen as "inputs and outputs of processes, in minds, machines, or organizations" [27].
The cutting forces, mechanical vibration and emission acoustic signals obtained using dynamometer, accelerometer, and acoustic emission sensors respectively have been extensively used to monitor several aspects of the cutting processes in automated machining operations.
Process monitoring in machining constitutes machine performance and machine condition for performing the desired objective.
In UAT, vibration is superimposed on the cutting tool movement, resulting in several advantages of the process, especially in machining of high-strength engineering materials.
These become even more important with a rising number of features during the classification process in a machine learning paradigm.
