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One of the rationalization areas is the material supply process. New tasks of material supply are: integration of supply with the operation of the entire system, especially at the product design stage, effective use of material resources, reduction of the level of supply costs from the point of view of production costs. In the conditions of unit and small batch production the materials requirement needs to be carefully planned and optimized due to the use of many different kinds, types and sizes of materials and dynamic changes of demand over time. In this situation there are needed tools enabling the measurement of production costs for particular tasks on the basis of currently realized processes. One of these tools is activity based costing which is a groundwork for decision making process in the material supply area. Basing on activity based costing, a model of materials requirements planning was developed, which considers minimizing the number of different kinds and sizes of materials by using alternative materials and, consequently, lowering production costs.

Calculation methods based on finite volume methods (CFD method) have proven to be very useful in optimizing (efficiency, emission) heat sources for wood burning. Such heat sources also include gasification hot-water boilers for the combustion of dry wood. One of the important parts of the entire process of converting the primary energy contained in the fuel to the transfer of heat to the heat carrier is the gasification of the fuel in the feed chamber and the subsequent burning of wood gas. The paper presents CFD simulation of wood gasification process in the filling chamber and subsequent burning of wood gas on a model heat source. As a model heat source, was selected a hot-water boiler with lower fuel firing with a flue gas-water heat exchanger tubular heat exchanger. It is a gasification hot water boiler for the combustion of dry wood. The interior of the boiler consists of a filling chamber where the fuel is dried and fused. The wood gas then passes through the nozzle to the combustion chamber, where it burns with the aid of secondary air. The flue gases pass their heat in the heat exchanger through the walls of the pipes into the water.

At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

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Using methods of planning automating of production processes as well as artificial intelligence, the methods presented in this paper were constructed for identifying the set value of manufacturing process parameters, which are key to evaluating the costs of the designed elements. The proposed solutions were adapted for systems used under the conditions of unit and small-batch production.

The article deals with methods of Artificial Intelligence and their utilisation in technical diagnostics. Special meaning will be given on methods such as Deep learning. The deep learning method seems to be a very good candidate for defect detection and pattern recognition. The method was applied for technical diagnostic in automotive factory and the problem will be described in the paper.