Search results

Radar is a useful instrument to get target information in restricted visibility and night navigation. If there are many similar targets in a close area, navigators sometimes make errors in recognizing the radar’s target direction when they find the targets in a seascape using radar information. They sometimes indicate other targets instead of their intended target by mistake. We must prevent the errors, to reduce accidents and improve safe navigation. The purpose of this paper is to investigate why navigators make mistakes when identifying the direction between the radar’s target echo on the display and the actual vessel in the seascape. We tackle this problem in three steps: 1) we propose a navigator’s radar target cognitive model; 2) we evaluate the errors of the radar target cognition and its indication in the seascape and 3) we discuss the errors with the parallax.

The lack of geometric and dimensional accuracy of parts produced by additive manufacturing (AM) is directly related to the machine, material and process used. This paper aims to propose a method for the analysis and compensation of machine-related geometric errors applicable to any AM machine, regardless of the manufacturing process and technology used.

For this purpose, an error calculation model inspired by those used in computerized numerical control machines and coordinate measuring machines was developed. The error functions of the model were determined from the position deviations of a set of virtual points that are not sensitive to material and process errors. These points were obtained from the measurement of an ad hoc designed and manufactured master artefact. To validate the model, off-line compensation was applied to both the original designed artefact and an example part.

The geometric deviations in both cases were significantly smaller than those found before applying the geometric compensation. Dimensional enhancements were also achieved on the example part by using a correction parameter available in the three-dimensional printing software, whose value was adjusted from the measurement of the geometrically compensated master artefact.

The errors that persist in the part derive from both material and process. Compensation for these type of errors requires a detailed analysis of the influencing parameters, which will be the subject of future research.

The use of the virtual-point-based error model increases the quality of additively manufactured parts and can be used in any AM system.

The purpose of this paper is to improve the accuracy of fused deposition modeling (FDM) machines.

An integrated error model and compensation methods are developed to improve the accuracy of FDM machines. The effects of machine-dependent and process-dependent errors are included in this integrated model. The error model is then used to obtain compensated values for the printed object. A three-dimensional artifact is designed for the FDM machine characterization. This process takes place only once and an error model for the machine is then developed. An artifact is designed that is feature rich and its coordinates are measured by the coordinate measuring machine (CMM). The CMM digitized values for the three-dimensional artifact are used to calculate the coefficients of the model. The integrated error model of the machine can be used to obtain the compensated values for any given part models. The coefficients of the integrated error model are machine-dependent and represent machine error estimation. To demonstrate this, two test examples are used and modified based on the machine model to verify the effectiveness of the proposed method.

The errors from machine mechanical structure and process are evaluated. The variation trend of each error is analyzed. The uncompensated and compensated models are compared, and the effectiveness of the integrated error model and compensation method is analyzed and validated.

An effective integrated error model with compensation is developed, which can be used to improve the FDM machines accuracy.

Nowadays, rapid prototyping is emerging as end use product in low volume. The accuracy of the fabricated components depends on various process parameters. Process parameters used in this investigation are layer thickness (150, 200 and 250 µm), infill pattern (linear, hexagonal and star fill), raster angle (0°, 45° and 90°) and infill density (40, 60 and 80%). Linear and radial dimension of knuckle joint are selected for the response factor.

The experiments are design by using response surface methodology (RSM). Four design variables at three levels are used to examine their influence on percentage error in linear dimension and radial dimension of the component. A prototype Knuckle joint is selected as component. Minitab-14 software is used for the design of experiments.

Experimental measure data is analyzed by using “smaller is better” quality characteristics. A regression model for the forecasting of percentage error in linear and radial dimension is developed. The developed model is within precision range. The optimum level of process for linear and radial dimensions are obtained: layer thickness of 150 µm, Infill pattern of linear, Raster angle of 90° and infill density of 40%.

It proves that both the mathematical model is significant and can be able to approximate the desired output value close to the accurate dimensions. While comparing the calculated F-values for both linear and radial dimension with the standard table (F-table, 0.05), it is found that at the given set of degree of freedom the standard F-values (6.61) is lower for that regression, linear, square and interaction source of the predicted model, for which p-values have already less than 0.05. It is desirable for significant process parameters.

The dimensional accuracy with respect to average percentage error of FDM produced knuckle joint is successfully examined. The effect of process parameters, namely, layer thickness, infill pattern, raster angle and infill density on average percentage error was investigated by RSM and analysis of variance table.

The novelty of this work lies in the fact that only few studies are available in archival literature related to influence of these process parameters on percentage error in linear and radial dimension for Polycarbonate (PC) material.

The novelty of this work lies in the fact only few studies are available in archival literature related to influence of these process parameters on percentage error in linear and radial dimension for Polycarbonate (PC) material.

The purpose of this paper is to present an experimental approach to measure and quantify the three‐dimensional geometrical manufacturing errors on a mass production of parts.

A methodology is developed to model and analyse the combined effect of these errors on a machined feature. Deviation of a machined feature due to the combined errors is expressed in terms of the small displacement torsor (SDT) parameters. Given a tolerance on the machined feature, constraints are specified for that feature to establish a relationship between the tolerance zone of the feature and the torsor parameters. These constraints provide boundaries within which the machined feature must lie. This is used for tolerance analysis of the machined feature. An experimental approach is proposed to measure and quantify the three‐dimensional manufacturing variations as torsors. The results are used to verify the analytical model.

Results show that it is possible to quantify manufacturing dispersions. The paper proposes a measuring method which can be done during the production. In the context of process planning, these experimental data allow us to perform realistic geometrical simulation of manufacturing. The results of this method are torsor components dispersions. Analysis and synthesis of the geometrical simulation of manufacturing are viable with reliable numerical data in order to predict the defects.

To perform realistic geometrical simulation of manufacturing, an experimental approach to measure and quantify the three‐dimensional geometrical manufacturing errors is proposed which is based on the SDT concept.

The purpose of this paper is to improve the dimensional accuracy of inclined thin-walled parts fabricated by laser direct metal deposition (DMD) under an open-loop control system.

In this study, a novel method of the adaptive slicing method and DMD process with feedback adjustment of deposition height has been developed to successively fabricate complex inclined thin-walled square tube elbow parts. The defocus amount was used as a variable to the matching between the deposition thickness and the adaptive slicing height.

The low relative error of dimensional accuracy between experimental and designed parts shows that the matching of the single-layer deposition thickness and the adaptive slicing height can be realized by optimizing the defocusing amount. The negative feedback of the thin-wall part height can be achieved when the defocus amount and the z-axis increment are less than deposition thickness. The improvement of dimensional accuracy of inclined thin-walled parts is also attributed to the optimized scanning strategy.

The slicing method and deposition process can provide technical guidance for other additive manufacturing (AM) systems to fabricate metal thin-walled parts with high dimensional accuracy because the feedback control of deposition height can be realized only by the optimized process.

This study provides a novel adaptive slice method and corresponding the deposition process, and expands the slicing method of AM metal parts.

Laminated object manufacturing (LOM) is a rapid prototyping process where a part is built sequentially from layers of paper. Studied in the present paper are the precision and accuracy of the LOM process and the dimensional stability of LOM parts. The process was found to exhibit both constant and random sources of error in the part dimensions. The dimensional error was the largest normal to the plane of the paper, exacerbated by the moisture absorption and subsequent swelling. The key process parameters were identified and optimized for sufficient bonding and cutting accuracy.

Gives a bibliographical review of the error estimates and adaptive finite element methods from the theoretical as well as the application point of view. The bibliography at the end contains 2,177 references to papers, conference proceedings and theses/dissertations dealing with the subjects that were published in 1990‐2000.

To obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized.

Based on back-propagation neural network (BPNN), the particle swarm optimization (PSO) algorithm was improved for optimizing the parameters of BPNN, and then the model precision was predicted with the improved PSO-BPNN (IPSO-BPNN) taking nozzle temperature, etc. as the influencing factors.

It was found from the experimental results that the prediction results of IPSO-BPNN were closer to the actual values than BPNN and PSO-BPNN, and the prediction error was smaller; the average error of dimensional precision and surface precision was 6.03% and 6.54%, respectively, which suggested that it could provide a reliable guidance for 3D printing optimization.

The experimental results verify the validity of IPSO-BPNN in 3D printing precision prediction and make some contributions to the improvement of the precision of finished products and the realization of 3D printing optimization.

The purpose of this paper is to review research studies on process optimisation and machine development that lead to the enhancement of final products in various aspects of the fused deposition modelling (FDM) process.

An overview of the literature, focussing on process parameters, machine developments and material characterisations. This study investigates recent research studies that studied FDM capabilities in printing a vast range of materials from thermoplastics to metal alloys.

FDM is one of the most common techniques in additive manufacturing (AM) processes. Many parameters in this technology have effects on three-dimensional printed products. Therefore, it is necessary to obtain the optimum elements, for example, build orientation, layer thickness, nozzle diameter, infill pattern and bed temperature. By selecting a proper variable range of parameters, the layers adhere strongly and building end-use products of high quality are achievable. A vast range of materials and their properties from polymers to composite-based polymers are presented. Novel techniques to print metal alloys and composites are examined to increase the productivity of the FDM process. Additionally, defects such as shrinkage and warpage are discussed to eliminate the system’s limitations and improve the quality of final products. Multi-axis and mobile machines brought enhancements throughout the process to eliminate obstacles such as staircase defects in the conventional FDM process. In brief, recent developments were identified and a summary of major improvements was discussed in this study for future research.

This paper is an overview that provides information about research and developments in FDM. This review focusses on process optimisation and obstacles in printing polymers, composites, geopolymers and novel materials. Therefore, machine characteristics were examined to find out the accessibility of printing novel materials for different applications.