Three-dimensional printed plastic products developed through fused deposition modeling (FDM) endure long-term loading in most of the applications. The tensile creep behavior of such products is one of the imperative benchmarks to ensure dimensional stability under cyclic and dynamic loads. This research dealt with the optimization of the tensile creep behavior of 3D printed parts produced through fused deposition modeling (FDM) using polylactic acid (PLA) material. The geometry of creep test specimens follows the American Society for Testing and Materials (ASTM D2990) standards. Three-dimensional printing is performed on an open-source MakerBot desktop 3D printer. The Response Surface Methodology (RSM) is employed to predict the creep rate and rupture time by undertaking the layer height, infill percentage, and infill pattern type (linear, hexagonal, and diamond) as input process parameters. A total of 39 experimental runs were planned by means of a categorical central composite design. The analysis of variance (ANOVA) results revealed that the most influencing factors for creep rate were layer height, infill percentage, and infill patterns, whereas, for rupture time, infill pattern was found significant. The optimized levels obtained for both responses for hexagonal pattern were 0.1 mm layer height and 100% infill percentage. Some verification tests were performed to evaluate the effectiveness of the adopted RSM technique. The implemented research is believed to be a comprehensive guide for the additive manufacturing users to determine the optimum process parameters of FDM which influence the product creep rate and rupture time.

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In the design stage of construction projects, determining the soil permeability coefficient is one of the most important steps in assessing groundwater, infiltration, runoff, and drainage. In this study, various kernel-function-based Gaussian process regression models were developed to estimate the soil permeability coefficient, based on six input parameters such as liquid limit, plastic limit, clay content, void ratio, natural water content, and specific density. In this study, a total of 84 soil samples data reported in the literature from the detailed design-stage investigations of the Da Nang–Quang Ngai national road project in Vietnam were used for developing and validating the models. The models’ performance was evaluated and compared using statistical error indicators such as root mean square error and mean absolute error, as well as the determination coefficient and correlation coefficient. The analysis of performance measures demonstrates that the Gaussian process regression model based on Pearson universal kernel achieved comparatively better and reliable results and, thus, should be encouraged in further research.

Improving the productivity of a manufacturing firm is very imperative in order to obtain high profits in such a competitive market. Equipment is an important part of a manufacturing system and its efficiency directly affects the quality and cost of the product contributing to overall productivity of a manufacturing plant. This paper focuses on calculating the overall equipment effectiveness (OEE) at a local pharmaceutical industry and investigates the key factors which influence the overall equipment effectiveness. OEE is an important tool which identifies areas that may have bottleneck in the production line, covering three major aspects such as the availability, performance, and quality rate of the output of equipment. On the basis of OEE, six significant losses were identified. The preliminary results of the analysis showed that OEE for the dry suspension (D/S) section is only 23% which is substantially below the benchmark of OEE level i.e. 85%. The results also presented that prime cause involves a high difference between the performance level of world class and dry suspension (D/S) process which is due to unavailability of machines for a longer period of time and malfunctioned machines producing products in large quantities that need to be

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