Additive Manufacturing (AM) brought new design possibilities and freedom to the product design process. However, as the existing design rules and design limitations do not apply to the AM, designers ...require new methods and tools to utilise the full potential of AM. The stated is especially noticeable in the design of metal products made with AM, particularly with the Direct Metal Laser Sintering (DMLS) process. The research proposes a methodological approach for analysing the existing DMLS products made with DMLS to extract design knowledge. The methodology is applied to the pool of DMLS from which 15 design principles are extracted that formalise design knowledge about DMLS. The design principles are intended to be used in the early design stages of the design process as a source of design knowledge and inspiration for conceptualising new and innovative products that will be made using DMLS.
Laser powder bed fusion (LPBF) process has a great ability to produce complex AlSi10Mg 3D components with uncommon degrees of freedom for broad span of applications in different industries. Presence ...of the microstructural imperfections such as porosity, dependent on the parameters of the process can be detrimental to the printed products for different engineering applications. Parameters of the process and different post-processing heat and surface treatments are recognized for decrease of the occurrence of microstructural defects and for the improvement of the mechanical properties. The influence of laser power by applying four different laser speeds at one layer thickness with a constant hatch distance, on the microstructure and microhardness of the AlSi10Mg alloy was examined in this paper. The goal of the research was to determine whether increasing the laser speed will have a significant impact on the change in microstructure and the appearance of porosity in the tested samples.
This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented ...research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control.
Predicting the quality of the robot end-effector grasp quality during an industrial robot manipulator operation can be an extremely complex task. As is often the case with such complex tasks, ...Artificial Intelligence methods may be applied to attempt the creation of a model - if sufficient data exists. The presented dataset uses a publicly available dataset, consisting of 992632 measurements of position, torque, and velocity - for each of the three joints of three fingers of the simulated end-effector. The dataset is first analyzed and pre-processed to prepare it for model training. The duplicate values are removed from the dataset, as well as the statistical outliers. Then, a multilayer perceptron (MLP) machine learning algorithm is applied to 80% of the data contained in the dataset, using the Grid Search algorithm to determine the best combination of MLP hyperparameters. As the dataset consists of torque, velocity, and speed measurements for separate joints and fingers of the tested end-effector the testing is performed to see if a subset of the inputs may be used to regress the robustness of the given grip. The normalization of the dataset is also applied, and its effect on the regression quality is tested. The results, evaluated with the coefficient of determination, show that while the best model is achieved using all the possible inputs, a satisfactory result can be obtained using only velocity and torque.The results also show that the normalization of the dataset improves the regression quality in all the observed cases.
This research investigates the impact of three process parameters of Laser Powder Bed Fusion (LPBF) - laser power, scanning speed, and base plate preheating temperature on the structure and ...mechanical properties of the EOS CoCr SP2 dental alloy. The LPBF process was used to fabricate dental Co–Cr alloy specimens for microstructural analysis and mechanical properties testing. Light and electron microscopy were used to determine microstructural parameters, including porosity, inclusions, and cracks. The material's chemical composition was analysed by EDS, while XRD and EBSD methods were used to determine the presence of microstructural phases and the crystallographic orientation of individual grains. The mechanical properties were evaluated through a static tensile test (Rp0.2, ε), a toughness test (KVa), and a three-point bending test to determine the flexural strength (Rms). In the microstructure, differences were observed that reflected statistically significant differences in mechanical properties (one-way analysis of variance (ANOVA) and Scheffé post hoc test (α = 0.05)) Using the base plate preheating temperature ϑp = 310 °C with a constant scanning speed v = 900 mm/s in combination with increasing laser power P from 160 W to 250 W the proportion of porosity decreased while the mechanical properties of toughness (KVa) and flexural strength (Rms) increase to maximum values.
Electropolishing at high current densities without agitation of the electrolyte results in a pitting phenomenon that produces dimple-like surface features. Although pitting is unfavorable in the ...electropolishing process, its effect on surface modification, such as surface texturing, has not been thoroughly investigated. Surface topography and chemical composition analyses of electropolished steel revealed surface pits and an oxide surface layer, indicating the presence of surface texture and coating. The resulting surface is characterized by negative skewness and high kurtosis values. The tribological behavior of the electropolished steel-bronze pair is investigated by evaluating coefficients of friction and bronze wear using sliding tests conducted in mixed and boundary lubrication regimes. The results are compared to those of the ground steel-bronze pair. In the mixed and upper range of the boundary lubrication regime, coefficients of friction reduction up to 30% and shorter running-in phases are observed for electropolished steel (electropolished steel μavg = 0.019 vs. ground steel μavg = 0.028). In contrast, the coefficient of friction increased in the lower range of boundary lubrication regime by 50% (electropolished steel μavg = 0.098 vs. ground steel μavg = 0.065). Electropolishing, as a cost- and time-effective method applicable to complex geometries, presents an alternative method for achieving surface modifications aimed at friction reduction and improved tribological behavior for non-conformal contacts in the boundary and mixed lubrication regimes.
The importance of error detection is high, especially in modern manufacturing processes where assembly lines operate without direct supervision. Stopping the faulty operation in time can prevent ...damage to the assembly line. Public dataset is used, containing 15 classes, 2 types of faultless operation and 13 types of faults, with 463 force and torsion datapoints. Four different methods are used: Multilayer Perceptron (MLP) selected due to high classification performance, Support Vector Machines (SVM) commonly used for a low number of datapoints, Convolutional Neural Network (CNN) known for high performance in classification with matrix inputs and Siamese Neural Network (SNN) novel method with high performance in small datasets. Two classification tasks are performed-error detection and classification. Grid search is used for hyperparameter variation and F.sub.1 score as a metric, with a 10 fold cross-validation. Authors propose a hybrid system consisting of SNN for detection and CNN for fault classification.
Obtaining a dynamic model of the robotic manipulator is a complex task. With the growing application of machine learning (ML) approaches in modern robotics, a question arises of using ML for dynamic ...modeling. Still, due to the large amounts of data necessary for this approach, data collection may be time and resource-intensive. For this reason, this paper aims to research the possibility of synthetic dataset creation by using pre-existing dynamic models to test the possibilities of both applications of such synthetic datasets, as well as modeling the dynamics of an industrial manipulator using ML. Authors generate the dataset consisting of 20,000 data points and train seven separate multilayer perceptron (MLP) artificial neural networks (ANN)—one for each joint of the manipulator and one for the total torque—using randomized search (RS) for hyperparameter tuning. Additional MLP is trained for the total torsion of the entire manipulator using the same approach. Each model is evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE), with 10-fold cross-validation applied. With these settings, all individual joint torque models achieved R2 scores higher than 0.9, with the models for first four joints achieving scores above 0.95. Furthermore, all models for all individual joints achieve MAPE lower than 2%. The model for the total torque of all joints of the robotic manipulator achieves weaker regression scores, with the R2 score of 0.89 and MAPE slightly higher than 2%. The results show that the torsion models of each individual joint, and of the entire manipulator, can be regressed using the described method, with satisfactory accuracy.
Determining the residuary resistance per unit weight of displacement is one of the key factors in the design of vessels. In this paper, the authors utilize two novel methods – Symbolic Regression ...(SR) and Gradient Boosted Trees (GBT) to achieve a model which can be used to calculate the value of residuary resistance per unit weight, of displacement from the longitudinal position of the center of buoyancy, prismatic coefficient, length-displacement ratio, beam-draught ratio, length-beam ratio, and Froude number. This data is given as results of 308 experiments provided as a part of a publicly available dataset. The results are evaluated using the coefficient of determination (R2) and Mean Absolute Percentage Error (MAPE). Pre-processing, in the shape of correlation analysis combined with variable elimination and variable scaling, is applied to the dataset. The results show that while both methods achieve regression results, the result of regression of SR is relatively poor in comparison to GBT. Both methods provide slightly poorer, but comparable results to previous research focussing on the use of “black-box” methods, such as neural networks. The elimination of variables does not show a high influence on the modeling performance in the presented case, while variable scaling does achieve better results compared to the models trained with the non-scaled dataset.
Abstract The spare parts play a vital role in sustaining the operation and longevity of products and systems, but their unavailability can lead to prolonged downtime or expensive replacements. The ...integration of 3D scanning and Additive Manufacturing (AM) presents a promising path for spare part production. However, to utilise the full potential of AM, sometimes, redesign of the original part is needed. This paper investigates and proposes a new approach that integrates reverse engineering and redesign of an original part based on functional analysis to support the manufacturing of AM spare parts.
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