Search

This approach is focused on Machine Intelligence for Diagnosis Automation, a research program, which promotes « preventative maintenance in manufacturing plants through the development of a fully automated prototype for oil analysis and fault prediction. The prototype is based on Artificial Intelligence (A.I.) software and online hardware ». Monitoring the condition of lubricants requires the examination of the physical, chemical and additive states, which maintain the quality of the lubricants, which is necessary for the proper functioning of the equipment. A lubricant monitoring program, especially from a qualitative point of view, will need to focus on both machine tool wear and degradation of lubricants, as well as on detecting and describing abnormal working conditions for both lubricants and machine parts. This goal can be satisfied by examining all the oils used in a company by completing laboratory tests to generate steps and acceptance classes, as well as unplanned contingency analyzes. These laboratory tests can be concentrated and classified into technology-based data sheets based on test-based information and test results, ultimately constituting consistent databases needed to generate monitoring and prevention reports. Data on the condition of the oil parameters used in the hydraulic system for lubricating machine tools have been collected during six months. The data as matrix organized, with 258648 rows (observations) and 21 columns (parameters).

This paper addresses a delicate problem, namely the diagnosis of the state of the oils in the industrial systems, namely the machine tools. Based on measurements (the data set contains over five million records), within a Machine Intelligence for Diagnosis Automation (MIDA) project funded by the National Program PN II, ERA MANUNET: NR 13081221 / 13.08.2013, several applications of MATLAB toolbars are being developed in the field of artificial intelligence, specifically using the Support Vector Machine algorithms and neural networks. The tests were carried out on several distinct situations, followed by validation and verification tests on the devices designed and developed within the project (MIDA, Monitoil).

In the industrial practice, the product is intended to be flawless, with no technological difficulty in making the profile shapes. If this product results without defects, then any Finite Elements Method (FEM) based simulation can support that technology. A technology engineer does not propose, very often to analyze the simulation of the design technology, but rather to try to optimize a solution that he feels feasible. Experiments used as the basis for numerical optimization analysis support their research in the field of superplastic forming. Determining the influence of input parameters on the output parameters, Determining the optimal shape of the product and the optimal initial geometry, the prediction of the cracks and possibly the fractures, the prediction of the final thickness of the sheet, these are the objectives of the research and optimization for this project. The results of the numerical simulations have been compared with the measurements made on parts and sections of the parts obtained by superplastic forming. Of course, the consistency of the results, costs, benefits, and times required to perform numerical simulations are evaluated, but they are not objectives for optimizing the superplastic forming process.