Automatic inspection of surface defects in die castings after machining

A new camera based machine vision system for the automatic inspection of surface defects in aluminum die casting was developed by the authors. The problem of surface defects in aluminum die casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. The casting surfaces are the most highly loaded regions of materials and components. Mechanical and thermal loads as well as corrosion or irradiation attacks are directed primarily at the surface of the castings. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks or tears, inclusions due to chemical reactions or foreign material in the molten metal, and pores that greatly influence the material ability to withstand these loads. Surface defects may act as a stress concentrator initiating a fracture point. If a pressure is applied in this area, the casting can fracture. The human visual system is well adapted to perform in areas of variety and change; the visual inspection processes, on the other hand, require observing the same type of image repeatedly to detect anomalies. Slow, expensive, erratic inspection usually is the result. Computer based visual inspection provides a viable alternative to human inspectors. Developed by authors machine vision system uses an image processing algorithm based on modified Laplacian of Gaussian edge detection method to detect defects with different sizes and shapes. The defect inspection algorithm consists of three parameters. One is a parameter of defects sensitivity, the second parameter is a threshold level and the third parameter is to identify the detected defects size and shape. The machine vision system has been successfully tested for the different types of defects on the surface of castings

Selected Principles of Feeding Systems Design: Simulation vs Industrial Experience

Abstract Simulation software dedicated for design of casting processes is usually tested and calibrated by comparisons of shrinkage defects distribution predicted by the modelling with that observed in real castings produced in a given foundry. However, a large amount of expertise obtained from different foundries, including especially made experiments, is available from literature, in the form of recommendations for design of the rigging systems. This kind of information can be also used for assessment of the simulation predictions. In the present work two parameters used in the design of feeding systems are considered: feeding ranges in horizontal and vertical plates as well as efficiency (yield) of feeders of various shapes. The simulation tests were conducted using especially designed steel and aluminium castings with risers and a commercial FDM based software. It was found that the simulations cannot predict appearance of shrinkage porosity in horizontal and vertical plates of even cross-sections which would mean, that the feeding ranges are practically unlimited. The yield of all types of feeders obtained from the simulations appeared to be much higher than that reported in the literature. It can be concluded that the feeding flow modelling included in the tested software does not reflect phenomena responsible for the feeding processes in real castings properly. Further tests, with different types of software and more fundamental studies on the feeding process modelling would be desirable

Planning complex engineer-to-order products

The design and manufacture of complex Engineer-to-Order products is characterised by uncertain operation durations, finite capacity resources and multilevel product structures. Two scheduling methods are presented to minimise expected costs for multiple products across multiple finite capacity resources. The first sub-optimises the operations sequence, using mean operation durations, then refines the schedule by perturbation. The second method generates a schedule of start times directly by random search with an embedded simulation of candidate schedules for evaluation. The methods are compared for industrial examples

Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process

The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical

Diagnosis of Missed Ductile Iron Melts with Process Modelling

The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes

System ekspertowy wspomagający proces przygotowania produkcji jachtów

Expert systems can be defined as computer programs, whose main task is to simulate a human expert, usually in a narrow field of expertise. Possible applications of modern information technology are very extensive, ranging from medicine, geology and technology to applications in the field of economic and financial decision support. The purpose of this paper is to present the practical application of an expert system that supports the process of managing the production of yachts and has a high suitability for use in this application. Using the expert system described in the paper reduces the time during the design and production preparation process.Systemy ekspertowe można określić jako programy komputerowe, których podstawowym zadaniem jest symulowanie działanie człowieka – eksperta, na ogół w wąskiej dziedzinie. Możliwości zastosowań tej nowoczesnej technologii informatycznej są bardzo duże, począwszy od medycyny, przez geologię, technikę aż do zastosowań w dziedzinie wspomagania podejmowania decyzji gospodarczych i finansowych. Celem niniejszego artykułu jest prezentacja praktycznego zastosowania systemu ekspertowego, który wspomaga proces przygotowania produkcji rekreacyjnych jednostek pływających i wykazuje dużą przydatność użytkową z jego stosowania. Korzystanie z opisanego systemu ekspertowego skraca czas w procesach projektowania i przygotowania produkcji jachtów

A R C H I V E S of F O U N D R Y E N G I N E E R I N G Modeling of feeding of grey iron castings

Abstract The aim of the paper was development and testing a new methodology for adjusting of simulation parameters of casting processes. Instead using production castings with limited shapes, the methodology utilizes especially designed virtual castings of arbitrary geometries, with rigging systems calculated according generally approved principles, based on industrial experience. The present work tests included risers for grey iron castings, designed according Karsay's recommendations; the simulations were made using the commercial software NovaFlow&Solid. The preliminary simulations have shown that the parameters, which are most important from the viewpoint of occurrence of the shrinkage defects, are the density change during solidification and the gravity influence. Further systematic simulations allowed to find that the feeding flow modeled in the computer program does fully correspond to those practical recommendations: the flow is too easy in vertical direction an to difficult in horizontal direction. Despite that, it was possible to formulate recommendations regarding settings of the above simulation parameters which would facilitate correct predictions of the shrinkage defects in grey iron castings: the influence of gravity should be 'high' and the density change between liquidus and solidus temperatures should be between 0 to 78 kg/m 3 , depending on the feeding distance

Application of Special Cause Control Charts to Green Sand Process

Statistical Process Control (SPC) based on the well known Shewhart control charts, is widely used in contemporary manufacturing industry, including many foundries. However, the classic SPC methods require that the measured quantities, e.g. process or product parameters, are not auto-correlated, i.e. their current values do not depend on the preceding ones. For the processes which do not obey this assumption the Special Cause Control (SCC) charts were proposed, utilizing the residual data obtained from the time-series analysis. In the present paper the results of application of SCC charts to a green sand processing system are presented. The tests, made on real industrial data collected in a big iron foundry, were aimed at the comparison of occurrences of out-of-control signals detected in the original data with those appeared in the residual data. It was found that application of the SCC charts reduces numbers of the signals in almost all cases It is concluded that it can be helpful in avoiding false signals, i.e. resulting from predictable factors