Support structure generation is a critical requirement in additive manufacturing (AM) to prevent material collapse in overhanging regions. However, it increases print time, material waste, and overall production cost, especially in extrusion-based AM. To mitigate these problems, design engineers often resort to manually finetuning or even redesigning prototype geometry to minimise support structures, which is time-consuming and inefficient. A direct geometric optimisation that preserves locality of shape changes whilst corresponding to the part orientation remains an underdetermined problem. In this paper, we present a novel alternating optimisation framework that finds the corresponding part geometry and orientation to minimise support structures under minimal geometric deviation. Whilst global-level support structure reduction is realised by the part orientation change, we introduce an efficient energy minimisation-based geometric optimisation framework, which is governed by saliency-aware elementwise projections and a set of manufacturing constraints. The proposed framework is validated through extensive computational and physical printing experiments employing multiple 3D printers and support structure types, on a diverse set of complex models including topologically non-trivial parts such as gyroid structures. Our results show an average reduction of 50 % in support structure print time, 27 % in material usage and 25 % in total print time, demonstrating the effectiveness of the proposed framework and its potential as a paradigm shift in manufacturing-oriented design.

The mouse, being the major means of inputting and controlling data on a computer, should beset right for best performance. Mouse gain, defined as the ratio of the movement distance on the computer screen to the movement distance of the mouse, controls the movement time when both speed and accuracy are important. Reported optimum gain values vary widely from about 2 to 15. An experiment having a targeting task was used to test 16 participants using a wide range of gain (1.2, 2.4, 14.5, and 38.9). An optimum gain of 2.4 was found for a display movement amplitude (A) of 100 mm, whereas the optimum gain was 14.5 for amplitudes of 200 and 400 mm. Depending on the gain and the movement amplitude, Fitts' law should be modified to accurately model movement time (MT) in the form of MT = A + b(ID) - c(A*ID). Only at low gains could a critical index of difficulty be determined, that being about 3.5.

This study investigated the distribution of movement time (MT) and submovement time as a function of Fitts' index of difficulty (ID) to help understand the underlying mechanisms of Fitts' law. Many previous studies have shown that movements in such a task can be broken into a series of submovements, but have not been able to account for the variation of MT with ID under different conditions and for different tasks. The trajectory of computer mouse movements was recorded and analyzed to identify the distribution of submovements and MTs. The movement trajectory consists of a series of submovements with predictable probabilities. The first submovement is ballistic in nature and has an initiation time proportional to the movement amplitude. The verification time is dependent on target size. The probabilities of occurrence of each submovement can account for the variations in MT. The developed method has application to the evaluation of computer input devices, as the structure of the movement shows the ability of a person to control a device to minimize the time for movement.

Targeting and tracking in graphical user interfaces have been widely studied, but attempts to model targeted-tracking are few. Targeted-tracking is essentially a two component task of tracking followed by targeting, where either or both components may dominate depending on the levels of difficulty in each component. The applicability of an empirical model based on computer mouse use is unknown with respect to other devices. In order to confirm the model validity for other input devices, experiments were carried out using a mouse, a pen mouse, a touch screen, and a graphics tablet. Fourteen participants were tested on 48 experimental conditions that included four difficulty levels and 12 conditions with varying track width (P), track length (D), and target width (W). Movement time, error rate, index of performance, and throughput were compared. Repeated-measures ANOVA indicated that factors in the targeted-tracking model were significant (ρ2> 0.8) to the model, confirming the generality of the model. A principal component analysis showed that a mouse is relatively superior in terms of both movement time and error rate. Thus, the targeted-tracking model is an effective way to compare and evaluate input devices.

With the increasing complexity of control rooms and the information explosion, effective multitasking is now desired. Monochronicity and polychronicity, which describe a person's ability to do one thing and many things at a time, respectively, have been studied for a long time. However, it is not clear these abilities are related to various individual characteristics. Forty-eight Chinese participants were tested on their perception, memory, judgement, attention ability and cognitive style. They also performed a task that required search and calculation under three conditions of unpaced, paced and paced with sequencing. There were significant differences in the performance and strategy between monochronic and polychronic individuals in the selective attention test. Monochronic individuals focused their attention on the primary task and achieved higher performance. Polychronic individuals had somewhat better total performance in more than one task under time-constrained conditions. The results clearly indicate that an individual's time use behaviours ought to be considered in training and control scenarios to account for differences among people.