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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.
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Inteligencia Artificial , Impresión Tridimensional , Industrias , Tecnología , PolímerosRESUMEN
Currently, there is an intensive development of bipedal walking robots. The most known solutions are based on the use of the principles of human gait created in nature during evolution. Modernbipedal robots are also based on the locomotion manners of birds. This review presents the current state of the art of bipedal walking robots based on natural bipedal movements (human and bird) as well as on innovative synthetic solutions. Firstly, an overview of the scientific analysis of human gait is provided as a basis for the design of bipedal robots. The full human gait cycle that consists of two main phases is analysed and the attention is paid to the problem of balance and stability, especially in the single support phase when the bipedal movement is unstable. The influences of passive or active gait on energy demand are also discussed. Most studies are explored based on the zero moment. Furthermore, a review of the knowledge on the specific locomotor characteristics of birds, whose kinematics are derived from dinosaurs and provide them with both walking and running abilities, is presented. Secondly, many types of bipedal robot solutions are reviewed, which include nature-inspired robots (human-like and birdlike robots) and innovative robots using new heuristic, synthetic ideas for locomotion. Totally 45 robotic solutions are gathered by thebibliographic search method. Atlas was mentioned as one of the most perfect human-like robots, while the birdlike robot cases were Cassie and Digit. Innovative robots are presented, such asslider robot without knees, robots with rotating feet (3 and 4 degrees of freedom), and the hybrid robot Leo, which can walk on surfaces and fly. In particular, the paper describes in detail the robots' propulsion systems (electric, hydraulic), the structure of the lower limb (serial, parallel, mixed mechanisms), the types and structures of control and sensor systems, and the energy efficiency of the robots. Terrain roughness recognition systems using different sensor systems based on light detection and ranging or multiple cameras are introduced. A comparison of performance, control and sensor systems, drive systems, and achievements of known human-like and birdlike robots is provided. Thirdly, for the first time, the review comments on the future of bipedal robots in relation to the concepts of conventional (natural bipedal) and synthetic unconventional gait. We critically assess and compare prospective directions for further research that involve the development of navigation systems, artificial intelligence, collaboration with humans, areas for the development of bipedal robot applications in everyday life, therapy, and industry.
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Robótica , Inteligencia Artificial , Fenómenos Biomecánicos , Marcha , Locomoción , Estudios Prospectivos , Robótica/métodos , CaminataRESUMEN
INTRODUCTION: GLP1 receptor agonist lixisenatide has demonstrated its efficacy in numerous clinical trials, nevertheless its real-life effectiveness data is limited. AIM: To describe effectiveness and safety of lixisenatide in routine clinical practice in the Czech Republic and the Slovak Republic, as recorded by the Registry-Based Observational Study. METHODS: Multinational, multicenter, observational, non-interventional, 6-month prospective product registry of patients with type 2 diabetes mellitus aged > 18 years who were initiating therapy with lixisenatide. Patients were enrolled into this registry, provided written informed consent, between 1 May 2013 and 31 December 2015. Evaluations were performed at baseline and after 3 and 6 months of lixisenatide treatment. The primary objective of the study was the absolute change in glycated hemoglobin (HbA1c) from baseline to month 6 after lixisenatide initiation. The study was approved by responsible ethics committees and performed in accordance with the Helsinki Declaration. Informed consent was obtained from all patients before enrolment in the study. RESULTS: Overall 772 eligible patients (51.4 % males), mean age 56.7 (± 9.3) years, with mean diabetes duration 7.7 (± 5.5) years, mean duration of treatment with oral antidiabetic drugs 6.8 (± 4.9) years, and body mass index 37.6 (± 5.9) kg/m2 were enrolled in the study. Overall, 93.6 % were obese, 86.3 % subject were treated for hypertension, and 76.0 % for dyslipidemia. In total 96.1 % of patients completed the 6 months therapy. Lixisenatide significantly reduced HbA1c (decrease by 9.7 ± 14.4 mmol/mol [3.1 ± 0.2 % DCCT] after 6 months in per protocol population), and body weight (decrease by 3.5 ± 5.4 kg). The best responders to the treatment were younger patients with higher BMI, who had a shorter duration of diabetes. Overall safety profile of lixisenatide was satisfactory in the study. The most frequent adverse events were functional disorders affecting the gastrointestinal system. There was no episode of severe hypoglycemia reported throughout the study. CONCLUSION: In a real-life practice cohort of patients with type 2 diabetes mellitus 6 months treatment with once-daily GLP1 receptor agonist lixisenatide significantly improved glucose control and decreased body weight without increasing the risk of symptomatic and/or severe hypoglycemia risk. FUNDING: Sanofi Czech Republic.Key words: GLP1 receptor agonist - glycated hemoglobin - HbA1c - lixisenatide - oral antidiabetic drugs (OAD) - observational study - hypoglycemia - type 2 diabetes mellitus.
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Diabetes Mellitus Tipo 2 , Hipoglucemia , Hipoglucemiantes , Péptidos , Sistema de Registros , Adulto , Anciano , Glucemia , República Checa , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Femenino , Hemoglobina Glucada , Humanos , Hipoglucemiantes/efectos adversos , Hipoglucemiantes/uso terapéutico , Masculino , Persona de Mediana Edad , Péptidos/efectos adversos , Péptidos/uso terapéutico , Estudios Prospectivos , EslovaquiaRESUMEN
Artificial intelligence methods and techniques creatively support the processes of developing and improving methods for selecting shredders for the processing of polymer materials. This allows to optimize the fulfillment of selection criteria, which may include not only indicators related to shredding efficiency and recyclate quality but also energy consumption. The aim of this paper is to select methods of analysis based on artificial intelligence (AI) with independent rule extraction, i.e., data-based methods (machine learning-ML). This study took into account real data sets (feature matrix 1982 rows × 40 columns) describing the shredding process, including energy consumption used to optimize the parameters for the energy efficiency of the shredder. Each of the 1982 records in a .csv file (feature vector) has 40 numbers divided by commas. The data were divided into a learning set (70% of the data), a testing set (20% of the data), and a validation set (10% of the data). Cross-validation showed that the best model was LbfgsLogisticRegressionOva (0.9333). This promotes the development of the basis for an intelligent shredding methodology with a high level of innovation in the processing and recycling of polymer materials within the Industry 4.0 paradigm.
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Recently, a dynamic increase in the number of polymer elements ending their life cycle has been observed. There are three main ways of dealing with polymer waste: reuse in an unchanged form, recycling (both material and energy), and disposal (mainly in the form of landfilling or incineration). The legislation of European countries promotes in particular two forms of waste management: reuse and recycling. Recycling processes are used to recover materials and energy especially from contaminated waste, which are structurally changed by other materials, friction, temperature, machine, process, etc. The recycling of polymers, especially of multi-plastic structural elements, requires the use of special technological installations and a series of preparatory operations, including crushing and separating. Due to the universality and necessity of materials processing in recycling engineering, in particular size reduction, the aim of this study is to organize and systematize knowledge about shredding in the recycling process of end-of-life polymeric materials. This could help properly design these processes in the context of sustainable development and circular economy. Firstly, an overview of the possibilities of end-of-life plastics management was made, and the meaning of shredding in the end-of-life pathways was described. Then, the development of comminution in recycling processes was presented, with special emphasis given to quasi-cutting as the dominant mode of comminution of polymeric materials. The phenomenon of quasi-cutting, as well as factors related to the material, the operation of the shredding machine, and the technological process affecting it were described. Research conducted on quasi-cutting as a phenomenon when cutting single material samples and quasi-cutting as a machine process was characterized. Then, issues regarding recycling potentials in the context of shredding were systematized. Considerations included the areas of material, technical, energy, human, and control potentials. Presented bases and models can be used to support the innovation of creative activities, i.e., environmentally friendly actions, that produce specific positive environmental results in the mechanical processing of recycled and reused materials. The literature survey indicates the need to explore the environmental aspect of the shredding process in recycling and connect the shredding process variables with environmental consequences. This will help to design and control the processes to get the lowest possible environmental burdens.
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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.
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This paper presents results of a study on the effect of filler size in the form of 15 wt% corn stalk (CS) fibers on the mechanical and thermomechanical properties of polylactide (PLA) matrix composites. In the test, polylactidic acid (PLA) is filled with four types of length of corn stalk fibers with a diameter of 1 mm, 1.6 mm, 2 mm and 4 mm. The composites were composed by single screw extrusion and then samples were prepared by injection molding. The mechanical properties of the composites were determined by static tensile test, static bending test and Charpy impact test while the thermo-mechanical properties were determined by dynamic mechanical thermal analysis (DMTA). The composite structures were also observed using X-ray microcomputed tomography and scanning electron microscopy. In the PLA/CS composites, as the filler fiber diameter increased, the degradation of mechanical properties relative to the matrix was observed including tensile strength (decrease 22.9-51.1%), bending strength (decrease 18.9-36.6%) and impact energy absorption (decrease 58.8-69.8%). On the basis of 3D images of the composite structures for the filler particles larger than 2 mm a weak dispersion with the filler was observed, which is reflected in a significant deterioration of the mechanical and thermomechanical properties of the composite. The best mechanical and thermomechanical properties were found in the composite with filler fiber of 1 mm diameter. Processing resulted in a more than 6-fold decrease in filler fiber length from 719 ± 190 µm, 893 ± 291 µm, 1073 ± 219 µm, and 1698 ± 636 µm for CS1, CS1.6, CS2, and CS4 fractions, respectively, to 104 ± 43 µm, 123 ± 60 µm, 173 ± 60 µm, and 227 ± 89 µm. The fabricated green composites with 1 to 2 mm corn stalk fiber filler are an alternative to traditional plastic based materials in some applications.
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To assess the impact of graphite fillers on the thermal processing of graphite/poly(lactic acid) (PLA) composites, a series of the composite samples with different graphite of industrial grade as fillers was prepared by melt mixing. The average size of the graphite grains ranged between 100 µm and 6 µm. For comparative purposes, one of the carbon fillers was expandable graphite. Composites were examined by SEM, FTIR, and Raman spectroscopy. As revealed by thermogravimetric (TG) analyses, graphite filler slightly lowered the temperature of thermal decomposition of the PLA matrix. Differential scanning calorimetry (DSC) tests showed that the room temperature crystallinity of the polymer matrix is strongly affected by the graphite filler. The crystallinity of the composites determined from the second heating cycle reached values close to 50%, while these values are close to zero for the neat polymer. The addition of graphite to PLA caused a slight reduction in the oxidation induction time (OIT). The melt flow rate (MFR) of the graphite/PLA composites was lower than the original PLA due to an increase in flow resistance associated with the high crystallinity of the polymer matrix. Expandable graphite did not cause changes in the structure of the polymer matrix during thermal treatment. The crystallinity of the composite with this filler did not increase after first heating and was close to the neat PLA MFR value, which was extremely high due to the low crystallinity of the PLA matrix and delamination of the filler at elevated temperature.
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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.