Artificial Neural Networks as a Tool for Supporting a Moulding Sand Control System Based on the Dependency between Selected Moulding Sand Properties

The article presents the potential for using artificial neural networks to support decisions related to the rebonding of green moulding sand. The basic properties of the moulding sand tested in foundries are discussed, especially compactibility as it gives the most information about the quality of green moulding sand. First, the data that can predict the compactibility value without the need for testing are defined. Next, a method for constructing an artificial neural network is presented and the network model which produced the best results is analysed. Additionally, two applications were designed to allow the investigation results to be searchable by determining the range of values of the moulding sand parameters

Parameter Optimization of Sandcasting of Silumin (Aa6061) Using Genetic Algorithm

Automobile and engine components are often sand cast during manufacture because of its simplicity in operation. Manufacturing processes and conditions were appropriately carried out by developing a Design of Experiment platform to effect a fair randomization of the various experimental runs. Taguchi Orthogonal array was used to develop a layout for the sand casting experiment. Multiple linear Regression technique was used to develop mathematical models for the 3 responses-fatigue strength, wear rate and hardness. The Weighted Average method was applied in ascribing criteria weights. The single composite objective function generated was inputted into the Genetic algorithm tool box which yielded optimal levels for the four cases adopted.  Case 4 which is the maximum importance for fatigue strength had its optimal conditions to be 749.99oC, 49.999Hz, 30.01seconds and 265.35mm2 for pouring temperature, vibration frequency, vibration time and runner size respectively. Validation test conducted showed that the values obtained from the actual experiment were similar to that yielded by the predictive models. Keywords: Sand casting , Optimization,  Genetic algorithm and Taguchi design DOI: 10.7176/IEL/13-1-06 Publication date: April 28th 202

Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming

Für einen optimalen Betrieb erfordern moderne Produktionssysteme eine sorgfältige Einstellung der eingesetzten Fertigungsprozesse. Physikbasierte Simulationen können die Prozessoptimierung wirksam unterstützen, jedoch sind deren Rechenzeiten oft eine erhebliche Hürde. Eine Möglichkeit, Rechenzeit einzusparen sind surrogate-gestützte Optimierungsverfahren (SBO1). Surrogates sind recheneffiziente, datengetriebene Ersatzmodelle, die den Optimierer im Suchraum leiten. Sie verbessern in der Regel die Konvergenz, erweisen sich aber bei veränderlichen Optimierungsaufgaben, etwa häufigen Bauteilanpassungen nach Kundenwunsch, als unhandlich. Um auch solche variablen Optimierungsaufgaben effizient zu lösen, untersucht die vorliegende Arbeit, wie jüngste Fortschritte im Maschinenlernen (ML) – im Speziellen bei neuronalen Netzen – bestehende SBO-Techniken ergänzen können. Dabei werden drei Hauptaspekte betrachtet: erstens, ihr Potential als klassisches Surrogate für SBO, zweitens, ihre Eignung zur effiziente Bewertung der Herstellbarkeit neuer Bauteilentwürfe und drittens, ihre Möglichkeiten zur effizienten Prozessoptimierung für variable Bauteilgeometrien. Diese Fragestellungen sind grundsätzlich technologieübergreifend anwendbar und werden in dieser Arbeit am Beispiel der Textilumformung untersucht. Der erste Teil dieser Arbeit (Kapitel 3) diskutiert die Eignung tiefer neuronaler Netze als Surrogates für SBO. Hierzu werden verschiedene Netzarchitekturen untersucht und mehrere Möglichkeiten verglichen, sie in ein SBO-Framework einzubinden. Die Ergebnisse weisen ihre Eignung für SBO nach: Für eine feste Beispielgeometrie minimieren alle Varianten erfolgreich und schneller als ein Referenzalgorithmus (genetischer Algorithmus) die Zielfunktion. Um die Herstellbarkeit variabler Bauteilgeometrien zu bewerten, untersucht Kapitel 4 anschließend, wie Geometrieinformationen in ein Prozess-Surrogate eingebracht werden können. Hierzu werden zwei ML-Ansätze verglichen, ein merkmals- und ein rasterbasierter Ansatz. Der merkmalsbasierte Ansatz scannt ein Bauteil nach einzelnen, prozessrelevanten Geometriemerkmalen, der rasterbasierte Ansatz hingegen interpretiert die Geometrie als Ganzes. Beide Ansätze können das Prozessverhalten grundsätzlich erlernen, allerdings erweist sich der rasterbasierte Ansatz als einfacher übertragbar auf neue Geometrievarianten. Die Ergebnisse zeigen zudem, dass hauptsächlich die Vielfalt und weniger die Menge der Trainingsdaten diese Übertragbarkeit bestimmt. Abschließend verbindet Kapitel 5 die Surrogate-Techniken für flexible Geometrien mit variablen Prozessparametern, um eine effiziente Prozessoptimierung für variable Bauteile zu erreichen. Hierzu interagiert ein ML-Algorithmus in einer Simulationsumgebung mit generischen Geometriebeispielen und lernt, welche Geometrie, welche Umformparameter erfordert. Nach dem Training ist der Algorithmus in der Lage, auch für nicht-generische Bauteilgeometrien brauchbare Empfehlungen auszugeben. Weiter zeigt sich, dass die Empfehlungen mit ähnlicher Geschwindigkeit wie die klassische SBO zum tatsächlichen Prozessoptimum konvergieren, jedoch kein bauteilspezifisches A-priori-Sampling nötig ist. Einmal trainiert, ist der entwickelte Ansatz damit effizienter. Insgesamt zeigt diese Arbeit, wie ML-Techniken gegenwärtige SBOMethoden erweitern und so die Prozess- und Produktoptimierung zu frühen Entwicklungszeitpunkten effizient unterstützen können. Die Ergebnisse der Untersuchungen münden in Folgefragen zur Weiterentwicklung der Methoden, etwa die Integration physikalischer Bilanzgleichungen, um die Modellprognosen physikalisch konsistenter zu machen

Design and fabrication of conformal cooling channels in molds:Review and progress updates

Conformal cooling (CC) channels are a series of cooling channels that are equidistant from the mold cavity surfaces. CC systems show great promise to substitute conventional straight-drilled cooling systems as the former can provide more uniform and efficient cooling effects and thus improve the production quality and efficiency significantly. Although the design and manufacturing of CC systems are getting increasing attention, a comprehensive and systematic classification, comparison, and evaluation are still missing. The design, manufacturing, and applications of CC channels are reviewed and evaluated systematically and comprehensively in this review paper. To achieve a uniform and rapid cooling, some key design parameters of CC channels related to shape, size, and location of the channel have to be calculated and chosen carefully taking into account the cooling performance, mechanical strength, and coolant pressure drop. CC layouts are classified into eight types. The basic type, more complex types, and hybrid straight-drilled-CC molds are suitable for simply-shaped parts, complex-shaped parts, and locally complex parts, respectively. By using CC channels, the cycle time can be reduced up to 70%, and the shape deviations can be improved significantly. Epoxy casting and L-PBF show the best applicability to Al-epoxy molds and metal molds, respectively, because of the high forming flexibility and fidelity. Meanwhile, LPD has an exclusive advantage to fabricate multi-materials molds although it cannot print overhang regions directly. Hybrid L-PBF/CNC milling pointed out the future direction for the fabrication of high dimensional-accuracy CC molds, although there is still a long way to reduce the cost and raise efficiency. CC molds are expected to substitute straight-drilled cooling molds in the future, as it can significantly improve part quality, raise production rate and reduce production cost. In addition to this, the use of CC channels can be expanded to some advanced products that require high-performance self-cooling, such as gas turbine engines, photoinjectors and gears, improving working conditions and extending lifetime

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Optimisation of the squeeze forming process.

This thesis presents the optimisation of the squeeze forming process, considering both the thermal and mechanical aspects. The Finite Element Method has been used to simulate the process and a Genetic Algorithm was used as an optimisation tool. The thermal optimisation has been applied to the squeeze forming process to achieve near simultaneous solidification in the cast part. The positions of the coolant channels were considered as design variables in order to achieve such an objective. The formulation of the objective functions involved two points and also considered the whole domain. The validation aspects of the optimisation of the casting processes for 2D and axi-symmetric problems were presented. The influence of the interfacial heat transfer coefficient related to optimisation of the process was explored. For the multi-objective optimisation problem, the objective was to achieve near simultaneous solidification in the cast part and also near uniform von Mises stress distribution in the die for the first and also tenth cycles. This is because it has been found that the process starts to reach cyclic stabilisation after the tenth cycle. The comparison between the design obtained from the practical solution derived from the optimisation process and also the design which has been applied in industry was also discussed. The Design Sensitivity Analysis and Design Element Concept have been applied to the squeeze forming process. For parameter sensitivity analysis, the Youngs Modulus was considered as a design variable. A few design element subdivisions have been employed to explore its application to the process. For shape sensitivities involving the coolant channels, the parameterisation was required in order to consider the coolant channel as an entity. The extent to which the tendency to move the coolant channel either in the X or Y-direction with respect to the particular von Mises stress constraint in the die was also discussed

Numerical optimization of gating systems for light metals sand castings

This thesis proposed an optimization technique for design of gating system parameters of a light metal casting based on the Taguchi method with multiple performance characteristics. Firstly, the casting model with a gating system was designed and exported as International Graphics Exchange Standard (IGES) models by Unigraphic NX4.0. Based on the IGES models of the casting, Finite Element (FE) Models were generated using Hypermesh software. Then, mold filling and solidification processes of the castings were simulated with the MAGMASOFT. Finally, the simulation result can be converted to numerical data according to the 3D coordinates of the FE model by MAGMALink module of MAGMASOFT . The various designs of gating systems for the casting model were generated and the simulated results indicated that gating system parameters significantly affect the quality of the castings. To obtain the optimal process parameters of the gating system, the Taguchi method including the orthogonal array, the signal to noise (S/N) ratio, and the analysis of variance (ANOVA) were used to analyze the effect of various gating designs on cavity filling and casting quality using a weighting method. The gating system parameters were optimized with evaluating criteria including filling velocity, shrinkage porosity and product yield