Search

The ability of artificial intelligence (AI) to process large amounts of data, analyze complex patterns, and make predictions is driving innovation in the energy sector and transformation of energy markets. It helps optimize operations, improve efficiency, reduce costs, and accelerate the transition to cleaner and more sustainable energy sources. AI is playing an increasingly important role in transforming energy markets in various aspects of the industry in different ways, including smart grids and energy management, renewable energy integration, energy forecasting and trading, demand response and load management, energy efficiency and conservation, maintenance and asset management, energy storage optimization, carbon emission reduction, market analytics and risk management, exploration and production, regulatory compliance, and safety. The aim of this article is to discuss our own AI-based computational model in sustainable transformation of energy markets and to lay the foundations for further harmonious development based on a computational (AI/ML-based) models, with particular reference to current limitations and priority directions for further research. Such an approach may encourage new research for the practical application of AI algorithms in critical domains of the energy sector. [ABSTRACT FROM AUTHOR]

Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient. [ABSTRACT FROM AUTHOR]

Reviewing the current state of knowledge on sustainable production, this paper opens the Special Section entitled “Sustainability in production in the context of Industry 4.0”. The fourth industrial revolution (Industry 4.0), which embodies a vision for the future system of manufacturing (production), focuses on how to use contemporary methods (i.e. computerization, robotization, automation, new business models, etc.) to integrate all manufacturing industry systems to achieve sustainability. The idea was introduced in 2011 by the German government to promote automation in manufacturing. This paper shows the state of the art in the application of modern methods in sustainable manufacturing in the context of Industry 4.0. The authors review the past and current state of knowledge in this regard and describe the known limitations, directions for further research, and industrial applications of the most promising ideas and technologies. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI) can adopt/optimize important principles in the workflow of 3D printing. This article aims to examine to what extent the current possibilities for using CI in the development of 3D printing and reverse engineering are being used, and where there are still reserves in this area. Methodology: A literature review is followed by own research on CI-based solutions. Results: Two ANNs solving the most common problems are presented. Conclusions: CI can effectively support 3D printing and reverse engineering especially during the transition to Industry 4.0. Wider implementation of CI solutions can accelerate and integrate the development of innovative technologies based on 3D scanning, 3D printing, and reverse engineering. Analyzing data, gathering experience, and transforming it into knowledge can be done faster and more efficiently, but requires a conscious application and proper targeting. [ABSTRACT FROM AUTHOR]

The study aimed to develop a system supporting technological process planning for machining and 3D printing. Such a system should function similarly to the way human experts act in their fields of expertise and should be capable of gathering the necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods and their significant effectiveness in supporting technological process planning. The purpose of this article is to show an intelligent system that includes knowledge, models, and procedures supporting the company's employees as part of machining and 3D printing. Few works are combining these two types of processing. Nowadays, however, these two types of processing overlap each other into a common concept of hybrid processing. Therefore, in the opinion of the authors, such a comprehensive system is necessary. The system-embedded knowledge takes the form of neural networks, decision trees, and facts. The system is presented using the example of a real enterprise. The intelligent expert system is intended for process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise and are not very familiar with its machinery and other means of production. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is changing many areas of technology in the public and private spheres, including the economy. This report reviews issues related to machine modelling and simulations concerning further development of mechanical devices and their control systems as part of novel projects under the Industry 4.0 paradigm. The challenges faced by the industry have generated novel technologies used in the construction of dynamic, intelligent, flexible and open applications, capable of working in real time environments. Thus, in an Industry 4.0 environment, the data generated by sensor networks requires AI/CI to apply close-to-real-time data analysis techniques. In this way industry can face both fresh opportunities and challenges, including predictive analysis using computer tools capable of detecting patterns in the data based on the same rules that can be used to formulate the prediction. [ABSTRACT FROM AUTHOR]

The application of modern methods (AI and CI) in the field of mechanical engineering is particularly interesting due to its research and practical character and very strong references to Industry 4.0 [4-6]. His main research interest is in knowledge discovery in clinical data sets, applications of artificial intelligence in biomedicine, computational models of brain processes, AI-based optimization of additive machining, and rehabilitation robots including exoskeletons. Machine modelling and simulation is the use of models (e.g. a physical, mathematical, or logical representation) as a framework for simulation to develop the data used to make technical decisions, in the field of mechanical engineering. The work by Drelich et al. confirms the usefulness of the applied ultrasonic method for testing macroscopic inhomogeneity of corrugated fibre-cement boards. [Extracted from the article]

The binary classifiers are appropriate for classification problems with two class labels. For multi-class problems, decomposition techniques, like one-vs-one strategy, are used because they allow the use of binary classifiers. The ensemble selection, on the other hand, is one of the most studied topics in multiple classifier systems because a selected subset of base classifiers may perform better than the whole set of base classifiers. Thus, we propose a novel concept of the dynamic ensemble selection based on values of the score function used in the one-vs-one decomposition scheme. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The proposed approach is compared with the static ensemble selection method based on the integration of base classifiers in geometric space, which also uses the one-vs-one decomposition scheme. In addition, other base classification algorithms are used to compare results in the conducted experiments. The obtained results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Knowledge management is understood as a set of formalized methods of collection and use of explicit and tacit knowledge of users of expert system which supports solving some problems with certain field. It is an attempt to make the best use of knowledge that is available in the company, creating new knowledge and increasing its understanding. It was presented using an expert system supporting coffee selection. The system was created with two tools, using CLIPS 6.3 and SPHINX 4.5. The usefulness of these two tools to create an expert system as a knowledge management tool has been compared. [ABSTRACT FROM AUTHOR]

The central objective of the present author's research is to develop a system supporting the design of a technological process (a computer-aided process planning system) that functions similarly to a human expert in the field in question. The use of neural networks makes the creation of such a system possible. The proposed method uses a system of three blocks of neural networks, and involves the creation of neural networks to be used for the selection of machines, tools, and machining parameters. These networks are built for each process operation separately; that is, a set of neural networks is created for each selection. For the construction of models, different types of neural networks (multilayer networks with error backpropagation, radial basis function, and Kohonen) with different structures were employed, and the networks that made the best selections were identified. A method was also developed for the elimination of defects occurring during the production process. When a defect comes to light, this method suggests changes to the technological process, thus improving the quality of that process. Guidelines for the elimination of defects are produced in the form of decision rules. Such a computer-aided process planning system will be especially useful for process engineers who do not yet have sufficient experience in the design of technological processes, or who have only recently joined a particular manufacturing enterprise and are not fully familiar with its machines and other means of production (tools and instrumentation). It should be emphasized that such a system performs an advisory role, and it is always the process engineer who makes the final decision. The neural network models were tested on real data from an enterprise. A computer-aided process planning system based on rules and neural network models enables the intelligent design of technological processes. [ABSTRACT FROM PUBLISHER]

The article presents an expert system for selection of semi-finished products using decision trees. The division of semi-finished products, the method of induction of decision trees, the expert system was discussed. The main part of the paper presents the methodology of creating an expert system using decision trees for the selection of semifinished products and sample screens of an implemented system. [ABSTRACT FROM AUTHOR]

The article presents the application of machine learning in eco-design of technological process. So far, eco-design of the product, taking into account its recycling, was reserved primarily for designers. Research have been made how eco-design can be used in the area of the design of technological process. The article contains a description of the author's method and application enabling the choice of material and / or a combination on the technology design stage taking into account the environmental aspect. The application has been created in the form of expert system. [ABSTRACT FROM AUTHOR]

The article presents the unidirectional multilayer neural network with error backpropagation for compensation of thermal deformations of ball screws of CNC machines. The need to improve the accuracy of positioning servo-drive has become the impetus to start research on new sensorless compensation method of thermal deformation of ball screw, which allows compensation of elongation of the screws on the basis of historical data. Neural networks have been developed with a different structures and parameters of learning. These networks were compared. The effectiveness of these networks was a measure of models evaluation. These models have been tested on real data. [ABSTRACT FROM AUTHOR]

This paper presents neural networks in process quality control. The control is a very important step in the production process. There are different methods for checking the quality of the process. In some manufacturing companies quality is checked by machine operators or quality control departments. Here can be used method for manually checking the quality or tools of statistical control in the form of specialized computer programs. Another approach is to treat the control card as an image instead of a set of points. We have developed the models of analysis of process control charts. The models were created in the form of multilayer networks with error back propagation (MLP neural networks). The models were tested on real data from the production company. [ABSTRACT FROM AUTHOR]

This paper attempts to conduct a comparative life cycle environmental analysis of alternative versions of a product that was manufactured with the use of additive technologies. The aim of the paper was to compare the environmental assessment of an additive-manufactured product using two approaches: a traditional one, based on the use of SimaPro software, and the authors’ own concept of a newly developed artificial intelligence (AI) based approach. The structure of the product was identical and the research experiments consisted in changing the materials used in additive manufacturing (from polylactic acid (PLA) to acrylonitrile butadiene styrene (ABS)). The effects of these changes on the environmental factors were observed and a direct comparison of the effects in the different factors was made. SimaPro software with implemented databases was used for the analysis. Missing information on the environmental impact of additive manufacturing of PLA and ABS parts was taken from the literature for the purpose of the study. The novelty of the work lies in the results of a developing concurrent approach based on AI. The results showed that the artificial intelligence approach can be an effective way to analyze life cycle assessment (LCA) even in such complex cases as a 3D printed medical exoskeleton. This approach, which is becoming increasingly useful as the complexity of manufactured products increases, will be developed in future studies. [ABSTRACT FROM AUTHOR]

In the article the neural networks used for failures location for water supply networks are presented. To do this a hydraulic model of the water net, as well as an appropriate developed monitoring system have to be used. The current applications of monitoring systems installed in the waterworks do not realize their possibilities. The monitoring systems provided as autonomic programs to collect and record the information about flows and pressures of water in source pumping stations, in the pump stations bringing up the water pressure inside the water net and in the pipes of water supply network give a general knowledge about state of its work, but if they would be used as elements of IT systems supporting the water network management, they could help to solve the tasks concerning detection and localization of water leaks. The models of failures location in water nets described in the paper are created by means of neural networks in the form of MLP nets. [ABSTRACT FROM AUTHOR]

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing. [ABSTRACT FROM AUTHOR]

The failures location algorithms for water-supply network by use the neural networks were presented in the article. The algorithms have to be the part of monitoring and management system of water-supply network. The present utilization of the monitoring systems does not answer their possibilities. At present the monitoring systems provide as autonomic programs to gathering the information about flows and pressures of water in source pumping stations, the zone of hydrophore stations and the pipes of water-supply network, giving general knowledge about state of her work, when simultaneously they can and should be used as elements of systems of network management, in this particularly in the range of detection and location of water leakages. The models of network failure location are created relatively by use simple neural networks in the form of MLP, RBF, Kohonen networks and hybrid neural networks. [ABSTRACT FROM AUTHOR]

Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted). [ABSTRACT FROM AUTHOR]

Technological and material issues in 3D printing technologies should take into account sustainable development, use of materials, energy, emitted particles, and waste. The aim of this paper is to investigate whether the sustainability of 3D printing processes can be supported by computational intelligence (CI) and artificial intelligence (AI) based solutions. We present a new AI-based software to evaluate the amount of pollution generated by 3D printing systems. We input the values: printing technology, material, print weight, etc., and the expected results (risk assessment) and determine if and what precautions should be taken. The study uses a self-learning program that will improve as more data are entered. This program does not replace but complements previously used 3D printing metrics and software. [ABSTRACT FROM AUTHOR]

While the intensity, complexity, and specificity of robotic exercise may be supported by patient-tailored three-dimensional (3D)-printed solutions, their performance can still be compromised by non-optimal combinations of technological parameters and material features. The main focus of this paper was the computational optimization of the 3D-printing process in terms of features and material selection in order to achieve the maximum tensile force of a hand exoskeleton component, based on artificial neural network (ANN) optimization supported by genetic algorithms (GA). The creation and 3D-printing of the selected component was achieved using Cura 0.1.5 software and 3D-printed using fused filament fabrication (FFF) technology. To optimize the material and process parameters we compared ten selected parameters of the two distinct printing materials (polylactic acid (PLA), PLA+) using ANN supported by GA built and trained in the MATLAB environment. To determine the maximum tensile force of the exoskeleton, samples were tested using an INSTRON 5966 universal testing machine. While the balance between the technical requirements and user safety constraints requires further analysis, the PLA-based 3D-printing parameters have been optimized. Additive manufacturing may support the successful printing of usable/functional exoskeleton components. The network indicated which material should be selected: Namely PLA+. AI-based optimization may play a key role in increasing the performance and safety of the final product and supporting constraint satisfaction in patient-tailored solutions. [ABSTRACT FROM AUTHOR]

An exoskeleton is defined as a distinctive kind of robot to be worn as an overall or frame, effectively supporting, or in some cases substituting for, the user's own movements. In this paper a new three-dimensional (3D) printed bydgostian hand exoskeleton is introduced and biomedically characterized. The proposed concept is promising, and the described approach combining biomechanical factors and 3D modeling driven by detailed hand exoskeleton patterns may constitute a key future method of ergonomic hand exoskeleton design and validation prior to manufacturing. Despite the aforementioned approach, we should be aware that hand exoskeleton constitutes hand support and rehabilitation robot system developing with the user; thus, certain coordination and continuity of the "hardware" part of the whole system and the training paradigm are essential for therapy efficacy. [ABSTRACT FROM AUTHOR]