Product assembly is often the last step in the manufacturing process. This task is usually performed by an assembly worker who needs to have practical experience and expertise. For complex products, the assembly may require a long time to study assembly plans. This paper presents a custom-designed Pick-to-Light system using Poka Yoke principles to make this activity easier. The created modular system with two-stage verification serves to guide the assembler precisely. It shows him on the display which parts he should use in a strictly defined assembly step. Our proposal aims to shorten assembly time and reduce the number of errors, which was supported by a case study in a small company with 30 employees. After analysing the data, we can declare that the proposed system significantly reduces the time required for assembly from 7 to 35% and reduces the error rate by 35%. The solution is scalable and flexible, as it can be easily adapted to display assembly steps for a different product.

The application of topological optimization is currently considered one of the current trends. Because the shape of the components thus designed is the result of a design generated based on external influences acting on the model, their form can be considered almost optimal. For example, the extent of material savings resulting from shortening production cycles and reducing energy requirements is significant. Due to the way models are produced by layering the material in 3D printing, this technology makes it possible to get a little closer to the models’ optimal shape, for example, to produce prototype models for the production of injection moulds. The amazing amount of plastic and composite materials that this technology brings allows for a variable change in manufactured models based on requirements or external influences. These materials also include a group of materials and composite materials that are classified as biodegradable due to their composition. This fact, combined with the possibility of achieving the most optimal shape of components, contributes to reducing the environmental burden of such oriented production. This work presents the opportunities for modifying topological optimization outputs based on operating parameters and limits of additive production equipment fused deposition modeling (FDM). It gives the possibilities of using alternative ecological materials, their direct application, and the impact on creating models with the help of this technology. The final phase represents the result of the optimization process of the subsystem mechanism and the influence of the mechanical properties of biodegradable materials on the production process and the energy intensity of production. The aim of this work is to point out the fact and possibilities of using composite materials on a natural basis and their possible impact on reducing the environmental burden.

This article presents the variability of Fused deposition modelling (FDM) technology and the possibilities of its use in the design and implementation of a prototype atypical device. The assumptions of the behaviour of individual components and subsystems of the design result from an extensive application of the finite element method and motion analysis of subsystems and various parts of the structure. The use of this method to such an extent accelerated the design process and its implementation. The proposal itself reflects the current state of this technology and its focus is on improving sustainable development. As is generally known, great efforts are currently being made to reduce plastic waste volume and its environmental burden. The proposed concept is modified to replace the final treatment of the top layers of the models, called “ironing” by non-planar layering of material. At the same time, it points out the advantages of this method in reducing energy requirements and the time required to produce models. The conclusion is a conceptual design of a printhead for a proposed prototype, designed to use recycled FDM, intending to streamline the possibility of recycling with little serial and piece production. This process thus closes the circle of opportunities published by us, which in the future can contribute to the optimisation of this technology towards increasing the efficiency of resource use, reduction of energy demands and environmental burden.

This paper aims to develop a method that could serve as a tool for evaluating extracted raw materials in terms of use by considering the place of extraction and consumption. Dijkstra´s algorithm solves many of the shortest path problems observed in the production planning of raw materials. The algorithm requires knowledge of the relative distance between the vertices and the definition of the Euclidean distance of the vertices from the target vertex. The algorithm scans all of the paths and chooses the one with the minimum distance. At the same time, it would be able to identify the places of sale of raw materials and transport sites for the transportation of raw materials. It would have a database of point and line sources of occurrence (mining, deposit), places of transport (transmission network), and points of sale (seller). At present, geo-statistics is becoming an essential tool for solving various problems in modern deposit geology. Its results are used to calculate reserves and the economic valuation of the deposit. In the process of production planning, it is necessary to constantly process and analyze the geological information obtained during the mining survey.

One of the basic points of Industry 5.0 is to make the industry sustainable. There is a need to develop circular processes that reuse, repurpose, and recycle natural resources, and thus, reduce waste. This part can also include composite materials, which were used for some time in many areas. An essential feature of their applicability is the properties of these materials. The ratio of the individual components determines the properties of composite materials, and artificial intelligence machine learning (ML) techniques are already used to determine the optimal ratio. ML can be briefly described as computer science that uses existing data to predict future data. This approach is made possible by the current possibilities of collecting and analysing a large amount of data. It improves the chance of finding more variable influences (predictors) in the processes. These factors can be quantified more objectively; their mutual interactions can be identified, and, thanks to longer-term sampling, their future development behavior can be predictively modelled. The present article deals with the possibility of applying machine learning in predicting the absorption properties of composite material, which consists of a thermoplastic and matrix recycled polyvinyl butyral (PVB), obtained after recycling car glass windshields.