Purpose: – 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. Design/methodology/approach: – 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. Findings: – 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. Practical implications: – 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. Originality/value: – 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.

Purpose: 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). Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Social implications: 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. Originality/value: 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.

To analyze the influence of cavity design preparation on stress values in three-dimensional (3D) solid model of maxillary premolar restored with resin composite.3D solid model of maxillary second premolar was designed using computed-tomography (CT) data. ...