In investigating thermally sprayed Ni-based self-fluxing alloy coatings, widely applied under conditions of wear, corrosion, and high temperatures, designed experiments and statistical methods as a basis for modeling and optimization have become an important tool in making valid and comparable conclusions. Therefore, this paper gives an overview of investigating Ni-based self-fluxing alloy coatings deposited by thermal spraying by the use of designed experiments and statistical methods. The investigation includes the period of the last two decades and covers the treatments of flame spraying, high-velocity oxy/air fuel spraying, plasma spraying, plasma-transferred arc welding, and laser cladding. The main aim was to separate input variables, as well as measured responses, and to point out the importance of correct application of statistical design of experiment. After the review of the papers, it was concluded that investigators have used the process knowledge to analyze and interpret the results of the statistical analysis of experimental data, which is in fact the best way of using the design of experiment in every research. Nevertheless, more attention should be given to correct planning and conducting the experiments to derive the models suitable for the prediction of measured response and which could be an appropriate input for single- or multi-objective optimization.

Surface roughness is an important indicator of the quality of the machined surface. One of the methods that can be applied to improve surface roughness is ball burnishing. Ball burnishing is a finishing process in which a ball is rolled over the workpiece surface. Defining adequate input variables of the ball burnishing process to ensure obtaining the required surface roughness is a typical problem in scientific research. This paper presents the results of experiments to investigate ball burnishing of AISI 4130 alloy steel with a high-stiffness tool and a ceramic ball. The experiments were conducted following a randomized full factorial design for different levels of input variables. The input variables included the initial arithmetic mean roughness (the initial surface roughness), the depth of ball penetration, the burnishing feed, and the burnishing ball diameter, while the output variable was the arithmetic mean roughness after ball burnishing (the final surface roughness). The surface roughness modeling was performed based on the experimental results, using regression analysis (RA), artificial neural network (ANN), and support vector regression (SVR). The regression model displayed large prediction errors at low surface roughness values (below 1 μm), but it proved to be reliable for higher roughness values. The ANN and SVR models have excellently predicted roughness across a range of input variables. Mean percentage error (MPE) during the experimental training research was 29.727%, 0.995%, and 1.592%, and MPE in the confirmation experiments was 34.534%, 1.559%, and 2.164%, for RA, ANN, and SVR, respectively. Based on the obtained MPEs, it can be concluded that the application of ANN and SVR was adequate for modeling the ball burnishing process and prediction of the roughness of the treated surface in terms of the possibility of practical application in real industrial conditions.

The surface roughness is a very significant indicator of surface quality. It represents an essential exploitation requirement and influences technological time and costs, i.e. productivity. For that reason, the main objective of this paper is to analyse the influence of face milling cutting parameters (number of revolution, feed rate and depth of cut) on the surface roughness of aluminium alloy. Hence, a statistical (regression) model has been developed to predict the surface roughness by using the methodology of experimental design. Central composite design is chosen for fitting response surface. Also, numerical optimization considering two goals simultaneously (minimum propagation of error and minimum roughness) was performed throughout the experimental region. In this way, the settings of cutting parameters causing the minimum variability in response were determined for the estimated variations of the significant regression factors.

Alternative approach to surface roughness evaluation is mostly based on the analysis of digital images of machined surfaces i.e. on extracting various features from the matrix mathematically representing a digital image. This paper analyses correlation between 23 different digital image features and the surface roughness for two different materials: aluminium and stainless steel. Machined surfaces for both materials were acquired by face milling. Factorial design 6 x 5 x 2 with two replicates was conducted for each material with cutting parameters being varied on various numbers of levels. Based on the correlation coefficients the results showed that the best ranked features regardless of the machined material were the features based on statistic measures.

Continuation of research on solving the problem of estimation of CNC grinding process parameters of multi-layer ceramics is presented in the paper. Heuristic analysis of the process was used to define the attributes of influence on the...

This paper presents a proposal for the design, development, and validation of a smart mini greenhouse intended for small-scale agricultural production. The main objective is to explore the feasibility of a cost-efficient smart mini greenhouse prototype. This paper briefly analyses the parameters that influence the process of plant cultivation. Parameters such as air temperature, air and soil humidity, solar radiation and amount of carbon dioxide are analysed. Based on the analysis of the successful process of plant cultivation, sensors are selected for the condition monitoring in the mini greenhouse. By selecting various sensors, actuators and the control unit (Arduino), a system for condition monitoring and dosing of the necessary elements in the process of plant growth (agricultural production) is formed. With the help of computer design tools, a proposal for the construction of a mini-greenhouse is made. The proposed components for growth support and condition monitoring are integrated into the defined construction of the mini greenhouse. Testing is carried out by designing the control programme and implementing it. By building a mini greenhouse and integrating the selected components (sensors, actuators and other elements) with the control programme, the complete smart mini greenhouse was validated. The validation positively confirmed the proposed and built prototype model of a smart mini greenhouse for production.