## 1 Introduction  
It is known that the method of powder metallurgy is one of the best ways to obtain products from pure metals and alloys based on them [1]. According to a pre-developed algorithm, the corresponding microstructure can be constructed (design) [2]. It considers the size and quantitative ratio of phases, the proportion of pores of a specific size, their shape, and the nature of the distribution of the cross-section of the product. Thus, the functional properties of products from these alloys can also be predicted. Intensive research is underway in this direction [3; 4; 5], which indicates the relevance of the aim set in this paper. It is primarily due to the biofunctionalization of titanium alloys due to the unique set of corrosion-mechanical properties compared to alternative biomaterials such as polymers and ceramics. Therefore, one of the most critical stages of powder metallurgy is making powders, their machining, alloying, heat, or chemical treatment [6]. It makes it possible to control the shape and size of the powder particles, which directly determines their value. The particles' size and shape in the powder affect the number and size of the macropores formed during pressing [7; 8]. The decrease in porosity with decreasing powder size is determined by the surface energy level per unit volume. A higher energy level characterizes smaller particles with a high specific surface area, and therefore they sinter better. The porosity during the sintering of spherical particles is about 50%, and the shape of the pores formed in this case is non-spherical [5; 9]. Thus, the use of powdered titanium alloys with a particular morphology of the location of the pores is based on the principle of structural homogeneity, which ensures a uniform distribution of pores of the same size and shape [10; 11]. Creating a multifactorial process of microstructure design of biocompatible materials should consider changes in material properties (corrosion resistance, adhesive strength, levels of passivation with bone tissue, etc.) depending on their structure. This became possible due to precise studies of titanium powder alloys of different alloying systems [10] and modern neural network modeling methods [12]. However, traditional research methods of powder alloys require large material resources [13], and the use of the existing machine learning methods [14] does not provide sufficient accuracy for their use in industrial systems. In addition, many problems accompany the use of artificial intelligence tools by materials scientists. It includes the lack of basic skills in data preprocessing and data analysis; lack of the necessary qualification when working with machine learning methods for the development, configuration, and use of artificial intelligence tools in solving applied problems of Materials Science, etc. Therefore, this paper aims are to improve the hybrid intelligent diagnostic system, which would increase the accuracy of work when solving the Ti-based alloy's quality evaluation task for medical implants manufacturing, as well as develop a simple, intuitive and fast smart web service of the quality assessment of sintered alloys for the manufacture of biomedical products. The main scientific results of this paper are the following:  
* we have generalized the approach to preprocessing small-sized and middle-sized datasets for further use by an arbitrary classifier.