Search results

The problem of inaccuracies of models manufactured by layer manufacturing technology has greatly limited its intended level of exploitations especially in the down‐stream applications and integration into the FMS and CIM. The principles of cellular manufacturing together with various examples of its application and benefits are well documented. Increased competition combined with customer demand for quality, speed, product performance and lower costs are putting an ever‐greater burden on manufacturing. Not only do products need to be launched faster than ever, but also manufacturing needs to be flexible in rapidly retuning its facilities. The move to cellular manufacturing, TQM or CIM or other techniques alone is thus no longer enough. Instead, the challenge is increasingly one of compressing the time it takes to continually “re‐invent” the manufacturing system in order to meet the prevailing demands of the market place. This paper presents a comparative study on the effects of hatch patterns on stereolithography models using epoxy‐based resin in a CIM environment. A unique experimental investigation has been carried out with two new hatch patterns, Divergent Star‐weave, (DSW), and Diagonal Divergent Star‐weave (DDSW), which have significantly improved the stereolithography product quality, and reduced lead time thus yielding a significant tool in agile manufacturing.

From the early 1990s, rapid prototyping (RP) system integration has become the desire of both industries and researchers. Presents a summary of findings from the Internet Conference on the state of the art of RP system integration. Attempts to define “integration” and relates it to RP technology. Presents technical issues that can enable RP system integration and the benefits of such integration. Finally, identifies and highlights key requirements for the success of RP integrated systems.

The purpose of this paper is to develop a novel process of integral ceramic molds for investment casting of hollow turbine blades.

At first, a resin pattern of a hollow turbine blade prototype is fabricated by stereolithography (SL). And then aqueous gelcasting process is utilized to fill the resin pattern with ceramic slurry of low viscosity and low shrinkage, through in situ polymerization of the slurry a ceramic mold is formed. At last, the ceramic mold for investment casting of hollow turbine blade is obtained by vacuum drying, pyrolyzing and sintering.

An integral ceramic mold is successfully fabricated by combining SL and gelcasting process, cores and shell are connected with each other and thus high relative position accuracy is guaranteed. Properties of integral ceramic mold at room temperature and high temperature satisfy the requirements of directional casting for complex‐shaped thin‐walled blades.

Because the integral ceramic mold is a close body, it is very difficult to directly measure its inner dimensions and the relative position accuracy of cores and shell, and the further research is needed.

This method enhanced the versatility of using SL prototype in the fabrication of integral ceramic mold for investment castings. Although this paper took a hollow turbine blade as an example, this method is also capable of fabricating integral ceramic molds for other complex investment castings.

The purpose of this paper is to investigate the adoption of rapid prototyping (RP) technology using three dimensional (3D) printer for infusing agility in traditional manufacturing environment.

The computer aided design (CAD) model of a knob of an electronics switch is developed using Pro/E software. Keeping this model as a reference, CAD models of new six knobs are developed. A 3D printer is used to build the prototypes of five of those CAD models. The receptivity of the practitioners over adopting CAD models and 3D printer for achieving agility is investigated.

The sensitisation of the industry captains and employees of traditional manufacturing sector is the imperative for exploiting the power of 3D printer and achieving mass customisation.

The paper reports an original research in which the practicality of using 3D printer is investigated with the objective of enabling the traditional manufacturing companies to imbibe agile characteristics.

This study aims to optimize the process parameters of ZPrinter® 450 for measured porosity (MP) and compressive strength (CS) of calcium sulfate-based porous bone scaffold using Taguchi approach.

Initially, a porous scaffold with smallest pore size that can be de-powdered completely is identified through a pilot study. Five printing parameters, namely, layer thickness (LT), build orientation (BO), build position (BP), delay time (DT) and binder saturation (BS), each at three levels have been optimized for MP and CS of the fabricated scaffolds using L27 orthogonal array (OA), signal-to-noise ratio and analysis of variance (ANOVA).

The scaffolds with 600 µm pores could be de-powdered completely. Optimum levels of parameters are LT₂, BO₁, BP₂, DT₁ and BS₁ for MP and LT₁, BO₁, BP₂, DT₁ and BS₂ for CS. The ANOVA reveals that the BS (49.12%) is the most and BP (8.34%) is the least significant parameter for MP. LT (50.84%) is the most, BO (33.79%) is second most and DT (2.59%) is the least significant parameter for CS. Taguchi confirmation test and linear regression models indicate a good agreement between predicted and experimental values of MP and CS. The experimental values of MP and CS at the optimum levels of parameters are found 38.12% and 1.29 MPa, respectively.

The paper presents effect of process parameters of ZPrinter® 450 on MP and CS of calcium sulfate-based porous scaffolds. Results may be used as guideline for powder bed binder jetting three-dimensional printing of ceramic scaffolds.

To improve the quality of the additive manufacturing (AM) products, it is necessary to estimate surface roughness distribution in advance. Although surface roughness estimation has been previously studied, factors leading to the creation of a rough surface and a comprehensive test for model validation have not been adequately investigated. Therefore, this paper aims to establish a robust model using empirical data based on optimized artificial neural networks (ANNs) to estimate the surface roughness distribution in fused deposition modelling parts. Accordingly, process parameters such as time, cost and quality should be optimized in the process planning stage.

Process parameters were selected via a literature review of surface roughness estimation modelling by analytical and empirical methods, and then a specific test part was fabricated to provide a complete evaluation of the proposed model. The ANN structure was optimized by trial and error method and evolutionary algorithms. A novel methodology based on the combination of the intelligent algorithms including the ANN, linked to the particle swarm optimization (PSO) and imperialist competitive algorithm (ICA), was developed. The PSOICA algorithm was implemented to increase the capability of the ANN to perform much faster and converge more precisely to favorable results. The performances of the ANN models were compared to the most well-known analytical models at build angle intervals of equal size. The most effective process variable was found by sensitivity analysis. The validity of proposed model was studied comprehensively where different truncheon parts and medical case studies including molar tooth, skull, femur and a custom-made hip stem were built.

This paper presents several improvements in surface roughness distribution modelling including a more suitable method for process parameter selection according to the design criteria and improvements in the overall surface roughness of parts as compared to analytical methods. The optimized ANN based on the proposed advanced algorithm (PSOICA) represents precise estimation and faster convergence. The validity assessment confirms that the proposed methodology performs better in varied conditions and complex shapes.

This research fills an important gap in surface roughness distribution estimation modelling by using a test part designed for that purpose and optimized ANN models which uses purely empirical data. The novel PSOICA combination enhances the ability of the ANN to perform more accurately and quickly. The advantage in using actual surface roughness values is that all factors resulting in the creation of a rough surface are included, which is impossible if other methods are used.

This paper is motivated by the need for a generic approach to evaluate the volumetric accuracy of rapid prototyping (RP) machines. The approach presented in this paper is inspired in large part by the techniques developed over the years for the parametric evaluation of coordinate measuring machine (CMM) errors. In CMM metrology, the parametric error functions for the machine are determined by actual measurement of a master reference artifact with known characteristics. In our approach, the RP machine is used to produce a generic artifact, which is then measured by a master CMM, and measurement results are used to infer the RP machine's parametric error functions. The results presented demonstrate the feasibility of such an approach on a two‐dimensional model.

The major concern technologies during the processing through three-dimensional printing (3DP) are the mechanical and boundary properties of sand models. The parameters such as activator content, resolution X, layer thickness and re-coater speed play a vital role in 3DP sand components. The purpose of this paper is to recommend the optimal process parameters for the best sand mold properties.

In this paper, taking the parameters of the activator content, resolution X, layer thickness and re-coater speed as the influence factors, an orthogonal test of L16(44) was designed to discuss the influences of those parameters on the mechanical and boundary properties. Three-point bending (3PB) test was used to characterize the actual bending strength, and the boundary accuracy was assessed by the deviation of the three-point bending samples compared with its design scale.

The experimental results showed that the resolution X and layer thickness are the main parameters affecting sand mold properties. The strength will attain its maximum when the resolution X and layer thickness are the minimum. The optimal parameters were screened and verified by the confirmation test. The optimal process parameters for best strength and less gas evolution are the activator of 0.19%, resolution X of 0.1 mm, layer thickness of 0.28 mm and re-coater speed of 210 mm/s.

The novelty of this paper is the select of significant parameters on 3D-printed sand model properties. A mathematical model was built to analyze the effect of these parameters. The optimal process parameters for the best properties were got.

Binder jetting (BJ) process is an additive manufacturing (AM) process in which powder materials are selectively joined by binder materials. Products can be manufactured layer-by-layer directly from three-dimensional model data. The quality properties of the products fabricated by the BJ AM process are significantly affected by the process parameters. To improve the product quality, the optimal process parameters need to be identified and controlled. This research works with the 420 stainless steel powder material.

This study focuses on four key printing parameters and two end-product quality properties. Sixteen groups of orthogonal experiment designed by the Taguchi method are conducted, and then the results are converted to signal-to-noise ratios and analyzed by analysis of variance.

Five sets of optimal parameters are concluded and verified by four group confirmation tests. Finally, by taking the optimal parameters, the end-product quality properties are significantly improved.

These optimal parameters can be used as a guideline for selecting proper printing parameters in BJ to achieve the desired properties and help to improve the entire BJ process ability.

This paper aims to develop a new slicing method for fused deposition modelling (FDM), the curved layer adaptive slicing (CLAS), combining adaptive flat layer and curved layer slicing together.

This research begins with a review of current curved layer and adaptive slicing algorithms employed in the FDM and further improvement of the same, where possible. The two approaches are then integrated to develop the adaptive curved layer slicing based on the three-plane intersection method for curved layer offsetting and consideration of facet angles together with the residual heights for adaptive slicing. A practical implementation showed that curved layer adaptive layers respond in similar lines to the flat layer counterparts in terms of the mechanical behaviour of FDM parts.

CLAS is effective in capturing sharply varying surface profiles and other finer part details, apart from imparting fibre continuity. Three-point bending tests on light curved parts made of curved layers of varying thicknesses prove thicker curved layers to result in better mechanical properties.

The algorithms developed in this research can handle relatively simple shapes to develop adaptive curved slices, but further developments are necessary for more complex shapes. The test facilities also need further improvements, to be able to programmatically implement adaptive curved layer slicing over a wide range of thicknesses.

When fully developed and implemented, CLAS will allow for better FDM part construction with lesser build times.

This research fills a gap in terms of integrating both curved layer and adaptive slicing techniques to better slice and build a part of given geometry using FDM.