Trustor Precision Components (Sweden)

The need to improve reliability and support decision-making in manufacturing has drawn attention to the application of diagnostic and decision-support tools. Particularly in the investment casting industry, data-driven methods can be the enabler for process diagnostics and decision support. Images from the microscopic examination in the investment casting process are used as data input, to detect defects in produced pieces. The microscopic examination usually relies solely upon the ability of the operator to determine whether an image from the microscope contains a defect. Therefore, an effective strategy for this decision-making process is crucial to improve the reliability of the examination. The use of the machine learning classifier Random Forest is introduced to derive predictions on the existence of a defect in the input image. This work focuses on employing machine learning tools for image recognition and the developed approach constitutes a decision support model to assist the operator and improve the reliability of their assessment.

Investment casting is a highly dynamic process during which multiple competing physical phenomena are at work. Those seeking to understand and simulate such processes computationally are confronted with a considerable task, balancing accuracy with efficiency. Approximations and models based on well-understood and documented fundamental physics are powerful tools in a modeller’s arsenal. Driven by observed discrepancies between experimental thermocouple measurements and simulation predictions of casting temperatures, this work explores the additional alloy cooling mechanism of mould transparency to infrared radiation, targeting a new mathematical approximation applicable in such situations. Direct attenuation, scattering from coarse sand, sand distribution in the mould and material temperatures play a role in the extent of radiation transparency that must be considered. From this model, estimation of the additional cooling rate resulting from expected mould transparency can be determined and applied as a corrective measure to computation fluid dynamics (CFD) simulation results that do not capture this phenomenon.

Defects in cast metals remain a common problem in many areas of the foundry industry, particularly in the investment casting of large area, thin-walled components for aerospace applications. During previous research, the thermophysical properties, density and porosity of a fibre reinforced ceramic investment casting mould were determined using several experimental techniques. Without verification, these experimental results remain nothing more than educated guesswork. The purpose of this study is to verify previous results and to more fully characterise the ceramic mould material with complementary measurements. A commercially available computational fluid dynamic (CFD) simulation package, Flow-3D®, was used in conjunction with a full-scale Ni-superalloy (IN718) casting to assess the accuracy of these results. By placing thermocouples strategically across the mould thickness, temperature profiles were determined and compared directly to predicted profiles extracted from the simulation by a custom-written Python script.

Simulation tools have improved significantly and are now capable of accurately predicting mould filling behavior. The quality of prediction is highly dependent on material properties and set-up of boundary conditions for the simulation. In this work material properties were measured and casting conditions were analyzed to accurately replicate the casting process in simulation. The sensitivity of the predictions to minor process variations commonly found in foundries was evaluated by comparing simulation and cast samples. The observed discrepancies between simulation and cast samples were evaluated and discussed in terms of their dependency on process variations. It was concluded that the simulation set-up was capable of reasonable predictions and could replicate the asymmetry of the filling however did not accurately predict the absolute value of the unfilled area. It was discovered that asymmetric flow due to variations in the orientation of the casting mould during filling could have greater influence on the predictions than the actual variation in fill time. The quality of simulation is dependent on equipment and techniques used in the foundry as well as the metallurgical model to simulate the process.

Birefringence in the plastic lenses of optical pickups (OPs) arises from preferential molecular orientation and internal stresses. This birefringence introduces astigmatic aberration to the transmitted wavefront when a polarized light is incident on the lens. It is necessary to quantitatively measure this birefringence-induced astigmatism (BIA) since it affects the aberration of the focused spot on the disk which, in turn, influences the readout signals of the drive system. The measurement of this astigmatism is, however, complicated by the fact that it couples with the astigmatism resulting from the deviation of the lens's shape, owing to fabrication errors of the molding inserts, from its originally designed formula. This paper presents a method for analyzing these two components of astigmatism separately so that the BIA of OP lenses can be evaluated.