In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

The submitted contribution focuses on the clarification of a laser beam cutting technology especially from the point of view of created surface topography. It provides a new view on a deformation process caused by laser beam action and on possibilities of using the surface topography. The measurement and characterisation of surface topography was performed in depth traces using a contact profilometer Surftest SJ 401 and by and an optical-contact profilometer Talysurf CLI 2000 (measured from the top edge of the sample). Thanks to this procedure, it was possible to observe and to measure a development of the numerical values of the surface (profile) roughness parameter Ra. Based on the measurement of the surface topography, there were analyzed and interpreted data with a purpose to theoretically describe surface topography and to develop an analytical solution for the profile topographical function. By using the profile topographical function, it is possible to solve the practical problems the most engineers and users face in laser beam cutting technology (LBC) process, as well as to maximize LBC manufacturing system performance and to determine the values of the process parameters that will reach the desired product quality.

The submitted article focuses on a detailed explanation of the average and range method (Automotive Industry Action Group, Measurement System Analysis approach) and of the honest Gauge Repeatability and Reproducibility method (Evaluating the Measurement Process approach). The measured data (thickness of plastic parts) were evaluated by both methods and their results were compared on the basis of numerical evaluation. Both methods were additionally compared and their advantages and disadvantages were discussed. One difference between both methods is the calculation of variation components. The AIAG method calculates the variation components based on standard deviation (then a sum of variation components does not give 100 %) and the honest GRR study calculates the variation components based on variance, where the sum of all variation components (part to part variation, EV & AV) gives the total variation of 100 %. Acceptance of both methods among the professional society, future use, and acceptance by manufacturing industry were also discussed. Nowadays, the AIAG is the leading method in the industry.Keywords: GRR study approach, the average and range method, the honest GRR study.

This contribution deals with the accuracy of machining during free-form surface milling using various technologies. The contribution analyzes the accuracy and surface roughness of machined experimental samples using 3-axis, 3 + 2-axis, and 5-axis milling. Experimentation is focusing on the tool axis inclination angle—it is the position of the tool axis relative to the workpiece. When comparing machining accuracy during 3-axis, 3 + 2-axis, and 5-axis milling the highest accuracy (deviation ranging from 0 to 17 μm) was achieved with 5-axis simultaneous milling (inclination angles βf = 10 to 15°, βn = 10 to 15°). This contribution is also enriched by comparing a CAD (Computer Aided Design) model with the prediction of milled surface errors in the CAM (Computer Aided Manufacturing) system. This allows us to determine the size of the deviations of the calculated surfaces before the machining process. This prediction is analyzed with real measured deviations on a shaped surface—using optical three-dimensional microscope Alicona Infinite Focus G5.

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The main aim is testing the basic properties of cobalt super alloys, under its own brand name HAYNES, marking No. 188, at machining and propose the most suitable cutting materials and machining parameters. The superalloys are developed for elevation of temperature service where relatively severe mechanical stressing is encountered and high surface stability is frequently required. The cobalt-based alloys have been in use for several decades in the manufacturing of various components. Although technology development rises in chipless machining such as moulding, precision casting and other manufacturing methods, the machining is still number one, at piece production which is typical for energy and chemical engineering. The driving force for their development still has been requirement of higher operating temperatures for many manufacturing fields in industry area.

Predicting the mechanical properties of cement-based mortars is essential in understanding the life and functioning of structures. Machine learning (ML) algorithms in this regard can be especially useful in prediction scenarios. In this paper, a comprehensive comparison of nine ML algorithms, i.e., linear regression (LR), random forest regression (RFR), support vector regression (SVR), AdaBoost regression (ABR), multi-layer perceptron (MLP), gradient boosting regression (GBR), decision tree regression (DT), hist gradient boosting regression (hGBR) and XGBoost regression (XGB), is carried out. A multi-attribute decision making method called TOPSIS (technique for order of preference by similarity to ideal solution) is used to select the best ML metamodel. A large dataset on cement-based mortars consisting of 424 sample points is used. The compressive strength of cement-based mortars is predicted based on six input parameters, i.e., the age of specimen (AS), the cement grade (CG), the metakaolin-to-total-binder ratio (MK/B), the water-to-binder ratio (W/B), the superplasticizer-to-binder ratio (SP) and the binder-to-sand ratio (B/S). XGBoost regression is found to be the best ML metamodel while simple metamodels like linear regression (LR) are found to be insufficient in handling the non-linearity in the process. This mapping of the compressive strength of mortars using ML techniques will be helpful for practitioners and researchers in identifying suitable mortar mixes.

The selection of elective courses based on an individual’s domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual’s domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student’s domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.