Qualification of the Low-pressure Cold Gas Spraying for the Additive Manufacturing of Copper–Nickel–Diamond Grinding Wheels

Grinding wheels are usually manufactured by powder metallurgical processes, i.e., by molding and sintering. Since this requires the production of special molds and the sintering is typically carried out in a continuous furnace, this process is time-consuming and cost-intensive. Therefore, it is only worthwhile for medium and large batches. Another influencing factor of the powder metallurgical process route is the high thermal load during the sintering process. Due to their high thermal sensitivity, superabrasives such as diamond or cubic boron nitride are very difficult to process in this way. In this study, a novel and innovative approach is presented, in which superabrasive grinding wheels are manufactured by thermal spraying. For this purpose, flat samples as well as grinding wheel bodies were coated by low-pressure (LP) cold gas spraying with a blend of a commercial Cu-Al2O3 cold gas spraying powder and nickel-coated diamonds. The coatings were examined metallographically in terms of their composition. A well-embedded superabrasive content of 12 % was achieved. After the spraying process, the grinding wheels were conditioned and tested for the grinding application of cemented carbides and the topographies of both the grinding wheel and the cemented carbide were evaluated. Surface qualities of the ground surface that are comparable to those of other finishing processes were reached. This novel process route offers great flexibility in the combination of binder and hard material as well as a cost-effective single-part and small-batch production.

Qualification of the Low-Pressure Cold Gas Spraying for the Additive Manufacturing of Copper-Nickel-Diamond Grinding Wheels

Grinding wheels are usually manufactured by powder metallurgical processes, i.e. by moulding and sintering. Since this requires the production of special moulds and the sintering is typically carried out in a continuous furnace, this process is time-consuming and cost-intensive. Therefore, it is only worthwhile for medium and large batches. Another influencing factor of the powder metallurgical process route is the high thermal load during the sintering process. Due to their high thermal sensitivity, superabrasives such as diamond or cubic boron nitride are very difficult to process in this way. In this study, a novel and innovative approach is presented, in which superabrasive grinding wheels are manufactured by thermal spraying. For this purpose, flat samples as well as a grinding wheel body were coated by low-pressure (LP) cold gas spraying with a blend of a commercial Cu-Al2O3 cold gas spraying powder and nickel-coated diamonds (8-12 μm). The coatings were examined metallographically in terms of their composition. Afterwards, the grinding wheel was conditioned for the grinding application and the topography was evaluated. This novel process route offers great flexibility in the combination of binder and hard material as well as a costeffective single-part and small-batch production.

Statistical modeling of temperature operating modes of heating furnaces for hot strip mills

The article provides a method of mathematical modeling to improve temperature operating modes of heating furnaces for hot strip mills. The object of the research is the thermal operation of a continuous walking beam furnace for heating slabs before rolling. The subject of the research is statistical modeling of metal heating in furnaces of this type. The creation of a statistical model consists of factors selection, construction of regression model, correlation analysis and assessment of the variables significance, adjustment of factors and obtaining regression equations. The main part of the research refers to a statistical model based on a comprehensive analysis. This model is based on the results of 15 automated industrial experiments on Russian heating furnaces of hot strip mills and describes the heating process in walking-beam furnace with acceptable accuracy. The adaptation of the statistical model and error calculation has been carried out. The article contains graphs comparing real temperatures and temperatures calculated on the basis of mathematical and statistical models for one of the experiments. The main conclusions are formulated based on the results of the research done. For the first time in metallurgical practice, a statistical model has been developed that describes the process of metal heating in a five-zone continuous furnace with eight heating subzones. Since the regression function is defined, interpreted and justified, and the assessment of the accuracy of the regression analysis meets the requirements, it can be assumed that the model and predicted values have sufficient reliability.

Prediction Model of Temperature of Cast Billet Based on Its Heating Retrospection Using Boosting “Random Forest” Structure

The scope of this research is the prediction of a cast billet surface temperature, which it will have in the rolling mill after the heating process. The main problem is that such a prediction is needed before the cast billet will really leave the furnace. In many cases, the boundary value problem of the heat transfer, particularly the differential equations of the transient heat conduction, is used to solve this problem. But in this research an alternative data-driven approach is proposed, which is based on a model of the dependence of the billet temperature on the retrospection of its heating in the continuous furnace. Such a model is developed as a result of the analysis of the data from the furnace control system. Such data from the real furnace were collected and stored in the data warehouse. Their exploratory analysis was conducted. All data were splitted into training, testing and validation subsets. As a part of this research, the regression model previously developed by the authors was also validated. It seemed to be overfitted (the error on the test set was significantly higher than the one on the training set). To overcome this disadvantage, an alternative method to develop the required data-based model is proposed by authors on the basis of the Boosting and Bagging algorithms. They belong to the machine learning field. As a result of the experiments with the bagging and boosting, the required model structure was chosen as a “Random Forest” with special class of the regression trees known as DART (Dropout Adaptive Regression Trees). Based on a significant number of experiments with that model, the two confidence intervals of the temperature prediction were found: 68 % and 95 % ones. The mean value of the temperature prediction error was estimated as ~ 9 °C for both the test and validation sets.