In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model's output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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Fused deposition modeling (FDM) is one of the Rapid Prototyping (RP) technologies. The 3D Printer has been widely used in the fabrication of 3D products. One of the main issues has been to obtain a high quality for the finished parts. The present study focuses on the effect of nozzle diameter in terms of pressure drop, geometrical error as well as extrusion time. While using polylactic acid (PLA) as a material, the research was conducted using Finite Element Analysis (FEA) by manipulating the nozzle diameter, and the pressure drop along the liquefier was observed. The geometrical error and printing time were also calculated by using different nozzle diameters. Analysis shows that the diameter of the nozzle significantly affects the pressure drop along the liquefier which influences the consistency of the road width thus affecting the quality of the product’s finish. The vital aspect is minimizing the pressure drop to be as low as possible, which will lead to a good quality final product. The results from the analysis demonstrate that a 0.2 mm nozzle diameter contributes the highest pressure drop, which is not within the optimum range. In this study, by considering several factors including pressure drop, geometrical error and printing time, a 0.3 mm nozzle diameter has been suggested as being in the optimum range for extruding PLA material using open-source 3D printing. The implication of this result is valuable for a better understanding of the melt flow behavior of the PLA material and for choosing the optimum nozzle diameter for 3D printing.