This study offers a complete analysis of the use of deep learning or machine learning, as well as precise recommendations on how these methods could be used in the creation of machine components and nodes. The examples in this thesis are intended to identify areas in mechanical design and optimization where this technique could be widely applied in the future, benefiting society and advancing the current state of modern mechanical engineering. The review begins with a discussion on the workings of artificial intelligence, machine learning, and deep learning. Different techniques, classifications, and even comparisons of each method are described in detail. The most common programming languages, frameworks, and software used in mechanical engineering for this problem are gradually introduced. Input data formats and the most common datasets that are suitable for the field of machine learning in mechanical design and optimization are also discussed. The second half of the review describes the current use of machine learning in several areas of mechanical design and optimization, using specific examples that have been investigated by researchers from around the world. Further research directions on the use of machine learning and neural networks in the fields of mechanical design and optimization are discussed.

The aim of this paper is to demonstrate the use of MATLAB and Simulink SimScape functions and toolboxes to model and evaluate key properties of an engineering system. In this case, it was a parcel delivery drone. The drone is assembled from four electric motors, propellers, a battery, four structural-bearing arms, and a body that holds the package. The mathematical model of the quadcopter is partitioned based on its subsystems such as sensors, flight dynamics, 3D animation, environment, etc. As science pushes the use of sustainable electronics further and further, the model includes a simplified model of battery performance. The battery capacity for each motor was 7600 mAh, while the 3D printing material had a density of 1400 kg/m^3. The weight of the drone (including batteries and motors) was 2,538 grams carrying a 1 kg package (effective weight ratio). The actuators provided a total maximum thrust of 107 newtons with overall drone dimensions of 48 x 48 x 12 cm. The trajectory was designed to replicate delivery from two locations within a 50 x 50-meter reference plane.

The aim of the work was to create input data for the neural network. A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. A neural network needs a large amount of data to be properly trained. Creo was used as a tool, which has a generative design tool in it that brings several significant benefits. The output of generative design is usually dozens of different design options, which the designer only must evaluate based on various criteria and choose the most suitable design. Primarily, it is used to reduce weight by approximately 30 to 40% compared to a conventional design. The lower weight not only brings material savings, but above all increased functionality of the components, which can achieve higher speeds and accelerations due to lower momentum, etc. The subject of the generation was the frame of the bicycle. A set of designs were generated that meet the input criteria and then will be used to train the neural network.

The thesis focuses on the analysis and calculation of the aerodynamic lift force of the propeller that is applied to the drone. The unmanned aerial vehicle, a drone, can be remotely controlled or capable of autonomous flight using pre-programmed flight plans or more complex dynamic autonomous systems. The main input is a model of the propeller, in this particular case a model of the three-bladed propeller that is used on the drone. The thesis summarizes the theoretical knowledge of the physics of flight. Subsequent analysis and mathematical calculation establishes the dependence of the lift force on the rotation of the propeller, which is shown in the table. The result of the work is the maximum load that the propeller can sustain in the process of flight due to the aerodynamic lift force.

The paper aims to classify the maintenance of internal combustion engines in 812 series diesel multiple units, to analyse operating failures, and to identify methods and procedures for determining their current mechanical condition. The paper specifies the required procedures and methods for maintenance starting from the development of the DMU (diesel multiple units) 812 series to the present. It also focuses on understanding how diagnosis and control sensors are attached in individual cable harnesses. A protocol for diagnosing combustion engine defects is proposed in the final part of the paper. A part of the paper is a proposed list of codes for individual faults to facilitate the work. Another method proposed within this paper is the definition and attachment of individual sensor connections on the engine in the EDC (Electronic Diesel Control) M(S) 5 cable harness. Faults leading to engine stop are identified.

DLC coatings have several characteristics that have industrial applications. Principal advantages include a low coefficient of friction, hardness, abrasion resistance, and corrosion resistance. Coatings are evaluated using a number of test procedures. The ball-on-flat method was used to conduct the tests that are the focus of this paper. The tribological pair consisted of a DLC-coated plate and test balls made from three different materials. Experiments provide the friction coefficient curves as well as an optical evaluation of the DLC coating's wear. In all three types of examined tribological pairings, the coating was worn, but the DLC coating on the plate was not completely removed. After the break-in phase, the coefficient of friction has approximately constant values.