MACHINABILITY ESTIMATION BY DRILLING MONITORING

Machinability of Titanium Alloys in Drilling

Prediction of steel machinability by genetic programming

Purpose: This paper describes intelligent system to predict steel machinability. Design/methodology/approach: The prediction of machinability of steel, depending on input parameters (percentage of calcium, percentage of oxygen, percentage of sulphur), was performed by means of genetic programming and data on the batches of steel already made. Findings: The mathematical model to predict machinab...

Intelligent Control System for Monitoring Drilling Process

– World wide drilling is developing for oil, gas or water resources. Electric motors power 75% of the total drilling rigs installation, augmenting to 95% for the case of Romania. Monitoring diesel generators micro-power station and motors of the rig is the second important thing to do and complete the main task of drilling process monitoring. This paper describe a monitoring and control system ...

Nonlinear Estimation with Applications to Drilling

An essential part of an automated MPD control system is the hydraulic model, which in many cases is the limiting factor for achievable accuracy of the system. A lot of effort has therefore been put into developing advanced hydraulic models that capture all aspects of the drilling fluid hydraulics. However, a main drawback is the resulting complexity of these models, which require expert knowled...

Material Classification By Drilling

Underground coal mining is one of the most dangerous occupations. Years of effort have been dedicated to researching methods of characterizing mine roof and floor for improving the mining environment. This research investigates using a neural network to classify rock strata based on the physical parameters of a roof bolting drill. This paper presents our methodology, as well as early results ba...