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This paper aims to propose a vacuum-assisted post-processing method for use in binder jetted technology. The method is based on six key technological parameters and uses standard, commercially available consumables to achieve improvement in tensile strength, as well as the microstructure and porosity of the infiltrated matrix.

Six key technological parameters were systematically varied as factors on three levels, using design of experiment, i.e. definitive screening design. Surface response methodology was used to optimize the process and yield optimal tensile strength for the given range of input factors. Thus obtained, the optimized factor settings were used in a set of confirmation runs, where the result of optimization was experimentally confirmed. To confirm improvement in microstructure of the infiltrated matrix, SEM analysis was performed, while the reduction of porosity was analyzed using mercury porosimetry.

The obtained results indicate that, compared to its conventional counterpart, the proposed, optimized infiltration method yields improvement in tensile strength which is significant from both the statistical and engineering point of view, while reducing porosity by 3.5 times, using only standard consumables. Scanning electron microscopy examination of fractured specimens’ micrographs also revealed significant morphological differences between the conventional and proposed method of post-processing. This primarily reflects in higher surface area under hardened epoxy infiltrate, which contributes to increased load capacity of specimen cross-section.

At the present stage of development, the most important limitation of the proposed method is the overall size of models which can be accommodated in standard vacuum impregnation units. Although, in this study, the infiltration method did not prove statistically significant, further investigation is required with models of complex geometry, various sizes and mass arrangements, where infiltration would be more challenging and could possibly result in different findings.

The most important practical implication of this study is the experimentally verified result of optimization, which showed that tensile strength and matrix microstructure can be significantly improved, using just standard consumables.

Improved strength contributes to reduction of material consumption, which, in a longer run, can be beneficial for environment protection and sustainable development.

Based on literature review, there have been no previous investigations which studied the tensile strength of infiltrated specimens through design of experiment, which involved specimen preheating temperature, level and duration of vacuum treatment of infiltrate mixture and infiltrated specimens and infiltration method.

This study aims to investigate the impact of layer thickness, extrusion temperature, extrusion speed and build plate temperature on the tensile strength, crystallinity achieved during fabrication (herein, in-process crystallinity) and mesostructure of Poly(lactic acid) specimens. Both tensile strength and in-process crystallinity were optimized and verified as the function of processing parameters, and their relationship was thoroughly examined.

The four key technological parameters were systematically varied as factors on three levels, using the statistically designed experiment. Surface response methodology was used to optimize tensile strength and crystallinity for the given ranges of input factors. Optimized factor settings were used in a set of confirmation runs, where the result of optimization was experimentally confirmed. Material characterization was performed using differential scanning calorimetry and X-ray diffraction analysis, while the effect of processing parameters on mesostructure was examined by scanning electron microscopy.

Layer thickness and its quadratic effect are dominant contributors to tensile strength. Significant interaction between layer thickness and extrusion speed implies that these parameters should always be varied simultaneously within designed experiment to obtain adequate process model. As regards, the in-process crystallinity, extrusion speed is part of two significant interactions with plate temperature and layer thickness, respectively. Quality of mesostructure is vital contributor to tensile strength during FDM process, while the in-process crystallinity exhibited no impact, remaining below the 20 per cent margin regardless of process parameter settings.

According to available literature, there have been no previously published investigations which studied the effect of process parameters on tensile strength, mesostructure and in-process crystallinity through systematic variation of four critical processing parameters.

The purpose of this study was to examine the impact of five key build parameters – layer thickness, deposition angle, infill, extrusion speed and extrusion temperature, and their interactions – on the maximum flexural force in specimens which are made of polylactic acid (PLA).

Through a previous study on the flexural properties of PLA specimens, a statistically significant effect of layer thickness was indicated, requiring further experimentation to establish the values of quadratic term in the model, as well as to perform optimization. Instead of performing a conventional Central Composite Design, a novel, definitive screening design (DSD) was used as statistical method. DSD allowed the reduction of the number of runs required for optimization while minimizing aliasing.

Significance of deposition angle and infill as main effects was established. Moreover, significant two-way interactions between infill/layer thickness and infill/extrusion speed were detected and discussed. The optimization procedure showed that minimum level of deposition angle, maximum levels of extrusion speed and infill and near mid-level of layer thickness yield maximum flexural force.

In this study, the three levels of infill were 0.1, 0.2 and 0.3, which corresponds to 10, 20 and 30 per cent of infill, respectively. In everyday practice, infill is usually kept within this range since it allows time-efficiency, i.e. significant reduction of build time. Though, unsurprisingly, higher infill is positively correlated with flexural strength, this study provides practical directions for optimal selection of other key parameters when working with low infill values.

Optimal 3D printing with low infill can contribute to lower material waste and pollution, while PLA plastic’s biodegradability remains high on the environment protection agenda.

According to available literature, no previous studies have investigated the FDM extrusion of PLA material using a combination of low infill, deposition angle, layer thickness, extrusion speed and extrusion temperature.

Main purpose is to present methodology which allows efficient hand gesture recognition using low-budget, 5-sensor data glove. To allow widespread use of low-budget data gloves in engineering virtual reality (VR) applications, gesture dictionaries must be enhanced with more ergonomic and symbolically meaningful hand gestures, while providing high gesture recognition rates when used by different seen and unseen users.

The simple boundary-value gesture recognition methodology was replaced by a probabilistic neural network (PNN)-based gesture recognition system able to process simple and complex static gestures. In order to overcome problems inherent to PNN – primarily, slow execution with large training data sets – the proposed gesture recognition system uses clustering ensemble to reduce the training data set without significant deterioration of the quality of training. The reduction of training data set is efficiently performed using three types of clustering algorithms, yielding small number of input vectors that represent the original population very well.

The proposed methodology is capable of providing efficient recognition of simple and complex static gestures and was also successfully tested with gestures of an unseen user, i.e. person who took no part in the training phase.

The hand gesture recognition system based on the proposed methodology enables the use of affordable data gloves with a small number of sensors in VR engineering applications which require complex static gestures, including assembly and maintenance simulations.

According to literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a 5-sensor data glove.

The purpose of this paper is to propose a general model for locating and clamping workpieces of complex geometry with two skewed holes under multiple constraints.

Numerous constraints related to application of the proposed model are discussed as prerequisite to design of fixture solution. Based on theoretical model, a fixture was designed and successfully tested in experimental investigation. Experimental results were also verified using FEM simulations.

This study showed that, opposed to conventional approach, novel solution results in significantly smaller fixture dimensions, while providing greater stability. Insertion of mandrels and supports element sub-assemblies into the workpiece holes significantly increases workpiece stiffness through an increased moment of inertia, while the internal support elements largely diminish the problem of thin wall deformation in the workpiece.

The fixture designed in this case was actually used in industrial application to accommodate a thin-walled casting of gearbox housing, where it proved to be a very stable framework. It can be used in industry without any major readjustments.

According to available literature, this work is the first successful implementation of a fixture solution in which the problem of multiple constraints is solved by attaching centering elements, support sub-assemblies, and other fixture elements to the internal workpiece walls, and then locating them in the second part of the fixture.