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In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R 2 ), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R 2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

Now-a-days, the application of hard tuning with CBN tool has been massively increased because the hard turning is a good alternative to grinding process. However, there are some issues that need to be addressed related to the CBN grades and their particular applications in the area of hard turning process. This experimental study investigated the effects of three different grades of CBN insert on the cutting forces and surface roughness. The process of hard turning was made using the AISI H13 die tool steel at containing different hardness (45 HRC, 50 HRC and 55 HRC) levels. The work material were selected on the basis of its application in the die making industries in a range of hardness of 45⁻55 HRC. Optimization by the central composite design approach has been used for design and analysis. The present study reported that the cutting forces and surface roughness are influenced by the alloying elements and percentage of CBN in the cutting tool material. The work material hardness, feed rate and cutting speed are found to be statistically significant on the responses. Furthermore, a comparative performance between the three different grades of CBN inserts has been shown on the cutting forces and surface roughness at different workpiece hardness. To obtain the optimum parameters from multiple responses, desirability approach has been used. The novelty/robustness of the present study is represented by its great contribution to solve practical industrial application when is developed a new process using different CBN grades for hard turning and die makers of workpiece having the hardness between 45 and 55 HRC., Competing Interests: The authors declare no conflict of interest.

Recently, magnesium (Mg) has gained attention as a potential material for orthopedics devices, owing to the combination of its biodegradability and similar mechanical characteristics to those of bones. However, the rapid decay rate of Mg alloy is one of the critical barriers amongst its widespread applications that have provided numerous research scopes to the scientists. In this present, porous Mg-based biodegradable structures have been fabricated through the hybridization of elemental alloying and spark plasma sintering technology. As key alloying elements, the suitable proportions of silicon (Si) and hydroxyapatite (HA) are used to enhance the mechanical, chemical, and geometrical features. It has been found that the addition of HA and Si element results in higher degree of structural porosity with low elastic modulus and hardness of the Mg⁻Zn⁻Mn matrix, respectively. Further, addition of both HA and Si elements has refined the grain structure and improved the hardness of the as-fabricated structures. Moreover, the characterization results validate the formation of various biocompatible phases, which enhances the corrosion performance and biomechanical integrity. Moreover, the fabricated composites show an excellent bioactivity and offer a channel/interface to MG-63 cells for attachment, proliferation and differentiation. The overall results of the present study advocate the usefulness of developed structures for orthopedics applications.

The present paper shows an experimental study on additive manufacturing for obtaining samples of polylactic acid (PLA). The process used for manufacturing these samples was fused deposition modeling (FDM). Little attention to the surface quality obtained in additive manufacturing processes has been paid by the research community. So, this paper aims at filling this gap. The goal of the study is the recognition of critical factors in FDM processes for reducing surface roughness. Two different types of experiments were carried out to analyze five printing parameters. The results were analyzed by means of Analysis of Variance, graphical methods, and non-parametric tests using Spearman's ρ and Kendall's τ correlation coefficients. The results showed how layer height and wall thickness are the most important factors for controlling surface roughness, while printing path, printing speed, and temperature showed no clear influence on surface roughness.