Methodology of Fault Diagnosis in Ductile Iron Melting Process

Abstract

Statistical Process Control (SPC) based on the Shewhart's type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including datadriven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input -output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.

Structural Transformations Versus Hard Particles Motion in the Brass Ingots

“…When the solidification rate is higher than the threshold rate (characteristic / typical of a given alloy) then the rejection is very intensive, [38]. Some advanced methods could be applied to make the diagnosis of a given alloy subjected to melting or solidification, [39]. This kind of diagnosis allows to confirm localization of the defects / inclusions in the alloy morphology.…”

Synergy of modeling processes in the area of soft and hard modeling

“…Adjustment of a given network for the results obtained from the Calibrate model was estimated using the formula (1). The best matching is presented by the model no.…”

“…4 (MLP-BFGS_Model_No#4) with tangent function of activation for both hidden and output layer. The network behavior was also examined with different number of neurons : (1-3), (1)(2)(3)(4)(5), (3)(4)(5), (3)(4)(5)(6)(7)(8) Results of the conducted simulation indicate, that the MLP network with the BFGS algorithm is good in predicting of solidification times for a higher number of records (at least several dozens). As such, ready model of the network can be used to shorten time of calculations in the hard modeling simulation systems.…”

“…Starting from the 90s of the last century, a distinct trend can be observed in scope of use of dedicated methods and computer systems for modeling and simulation of production processes [1][2][3][4][5][6]. It is related to high technological complexity of these processes, as well as complexity of physicochemical phenomena accompanying them.…”

Diagnosis of Missed Ductile Iron Melts with Process Modelling

“…Two different problems of that kind were addressed and solved in [9,10]. Similarly, finding the most probable cause of appearance of specific product defect or machine failure can be aided by models linking the process input parameters with the process outcomes.…”