Sugarcane must be harvested at the time of maturity, known as the Period of Industrial Utilization (PIU). The PIU analysis is expensive and is performed in a laboratory by measuring an index defined as Total Recoverable Sugar (TRS). The fact is that harvesting is the most expensive stage in sugarcane production and decision-making in this segment depends on TRS level estimates. However, forecasting models aimed at replacing laboratory analyses do not meet the reality regarding the estimation of the TRS index. There is a great demand for tools capable of estimating the index and/or the factors that affect TRS. In this context, this article presents a case study whose objective is to apply statistical models to estimate the TRS index in the sugarcane production of a mill in the interior of São Paulo, Brazil. Variance Analysis (ANOVA) with one classification and Multiple Linear Regression (MLR) models are applied by using Minitab®. These models are based on covariates related to the TRS index to estimate the productivity of 48,151 plots from the 2016/2017 to the 2022/2023 harvests. It is shown that the adjusted models identify the most important covariates (5% significance level) that affect productivity and the TRS index. The accuracy is satisfactory for all covariates of the adjusted MLR model and for the coefficients that measure the proportion of data variability for productivity (76%) and the TRS index (55%). This article brings important contributions to the sugar and ethanol industries worldwide and in Brazil.

In the current decade, a number of industries have moved their attention towards emerging sustainable technologies in order to better support socio-economic and environmental considerations. The present research investigates a unique hybrid composite developed by the amalgamation of natural kenaf-coir fibers, with resin of epoxy, incorporated with titanium carbide (TiC) nanoparticles. This study also presents the development process involved in manufacturing the composites, along with mechanical testing and optimization of these composite samples. The nanofillers of TiC are utilized in wt. percentages of 0%, 3%, 4%, and 5%, while coir and kenaf fibers are incorporated at 0%, 3%, 4%, and 5% by weight, and the thickness of the samples is varied at 2, 3, 4, and 5mm. The mechanical attributes of composites are evaluated using a vacuum bag molding process, with subsequent testing and optimization performed through Taguchi and ANOVA analysis to discover the optimal sample combination. The findings indicate that the most effective composite formulation includes 4% TiC, 5% kenaf, 5% coir, and a thickness of 3 mm, which provides the highest tensile modulus and strength among all tested samples. The integration of kenaf fibers with coir fibers and TiCs as fillers significantly improves the tensile and flexural attributes of the hybrid composite in contrast to composites made with coir or kenaf fibers alone.

Injection moulding (IM) is a processing technique produced from polymeric products. Warpage defect (WD) is the defect that generally occurs during the IM process due to the inappropriate processing parameters of the melt temperature, mould surface temperature, packing pressure, injection pressure, and packing pressure time. This paper investigates the IM parameters that influence product warpage by combining the simulation, analysis of variance, signal-to-noise analysis, and Taguchi method. The simulation process was performed by Moldflow software. The product material is high-density polyethylene. The WD has been predicted and optimized to enhance product quality. Melt temperature and packing pressure time are the factors that acrimoniously influenced the warpage of the product. The results show that the packing pressure time and melt temperature have the highest effects on the WD by the contributions of 48.94% and 37.48%, respectively. The optimal IM parameters are scanned again with the WD abated at about 1.2%. The mathematical formula has been constructed to predict the WD with the reflection of acceptable values of 86.29%. The research hopes that the results have been applied to designing and fabricating the plastic product in the near future.

Recently, there has been an increase in the use of laser beam welding of mild steel in various industries, including petroleum refineries, power plants, pharmaceuticals, and even residential areas. This research paper focuses on studying the effects of laser welding process parameters, such as laser power and welding speed, on the tensile strength of welds. To do this, three types of laser beam oscillations (sinusoidal path, triangular path, and square path) were performed to weld 125mm x 60 and 1.8 thick sheets of ASTM A36 mild steel alloy. The researchers used statistical tools such as ANOVA to generate mathematical models and experimental designs using the Taguchi method. The results indicate that the optimal welded joint has good mechanical properties after laser welding. For ASTM A36 mild steel, the optimal parameters for laser welding are a laser power of 1800 W, a welding speed of 50 mm/s, and triangular welding mode.

The present work deals with some machinability studies on tool wear and surface roughness, in finish hard turning of AISI D2 steel using PCBN, Mixed ceramic and coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters viz., cutting speed, cutting time and tool hardness on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA).The relationship(s) between input variables and the response parameters are determined using a quadratic regression model. The results show that the tool wear was influenced principally by the cutting time and in the second level by the cutting tool hardness. On the other hand, cutting time was the dominant factor affecting workpiece surface roughness followed by cutting speed. Finally, the multiple response optimizations of tool wear and surface roughness were carried out using the desirability function approach (DFA).

Variation exists in all processes. There is not even a single process that is completely true. Measuring the trueness of the process is itself a process which can also imitate the process variation. Therefore, measurement system should be strong enough to wager on the trueness of the process. This paper is an attempt to indicate the true method and substantiate the use of measurement system analysis (MSA) by using it in two different environments i.e. in manufacturing as well as process industry. Also, a comparison among various analyzing techniques has been drawn for authenticating the candid method followed by an evaluation using fuzzy TOPSIS for authenticating the results of comparison. The organization’s type, also, strongly influences the performance of MSA as revealed in the conclusion of the article. The results calculated by various methods and in both environments were discussed and as a result ANOVA comes out to be the best method. The application of correct MSA is highly required which ultimately results in increased organizations’ performance. The study is one of its type and will motivate the researchers and industrialists to use and explore the new and efficient ways of MSA.

This paper suggests an advanced hybrid multi output optimization technique by applying weighted principal component analysis (WPCA) incorporated with response surface methodology (RSM). This investigation has been carried out through a case study in CNC turning of Aluminum alloy 63400 for surface roughness (Ra) and tool vibration (db) optimization. Primarily, input parameters such as spindle speed (N), feed rate (S) and depth of cut (t) are designed for experiment by using RSM Box-Behnken methodology. The aluminum alloy workpieces are machined by using coated carbide tool (inserts) in dry environment. Secondly, the empirical model for the responses as the functions of cutting parameters are obtained through RSM technique and the adequacy of the models have been checked using analysis of variance (ANOVA). Finally, the process parameters are optimized using WPCA technique. The confirmatory experiment has been performed using optimized result and it reveals that multiple response performance index (MPI) value was increased by 0.2908 from initial setting. The increases in MPI value indicates that the aforesaid optimization methodology is suitably acceptable for multi response optimization for turning process.

The present work concerns an experimental study of turning with coated cermet tools with TiCN-TiN coating layer of AISI 52100 bearing steel. The main objectives are firstly focused on the effect of cutting parameters and coating material on the performances of cutting tools. Secondly, to perform a Multi-objective optimization for minimizing surface roughness (Ra) and maximizing material removal rate by desirability approach. A mathematical model was developed based on the Response Surface Methodology (RSM). ANOVA method was used to quantify the cutting parameters effects on the machining surface quality and the material removal rate. The results analysis shows that the feed rate has the most effect on the surface quality. The effect of coating layers on the surface quality is also studied. It is observed that a lower surface roughness is obtained when using PVD (TiCN-TiN) coated insert when compared with uncoated tool. The values of root mean square deviation and coefficient of correlation between the theoretical and experimental data are also given in this work where the maximum calculated error is 2.65 %.

This paper describes a comparison of surface roughness between ceramics and cubic boron nitride (CBN7020) cutting tools when machining of AISI H11 hot work steels treated at 50 HRC. Plan is designed according to Taguchi’s L18 (21×32) orthogonal array. The response surface methodology (RSM) and analysis of variance (ANOVA) were used to check the validity of multiple linear regression models and to determine the effects, contribution, significance and optimal machine settings of process parameters, namely, cutting speed, feed rate and depth of cut on machining parameters on the Ra and Rt. The results of this research work showed that, the feed rate was found to be a dominant factor on the surface roughness, followed by the cutting speed, lastly the depth of cut. The CBN7020 cutting tool showed the better performance than that of ceramic based cutting tool. In addition, the combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness.

There is an increase in awareness about sustainable manufacturing process. Manufacturing industries are backbone of a country’s economy. Although it is important but there is a great concern about consumption of resources and waste creation. The primary aim of this study was to explore sustainability concern in turning process in an Indian machining industry. The effect of cutting parameters, Speed/Feed/Depth of Cut, the machining environment, Dry/MQL/Wet, and the type of cutting tool on sustainability factors under study were observed. Analysis of Variance (ANOVA) was used to analyse the data obtained from experimentation in a small scale machining industry. The process is modelled mathematically using response surface methodology (RSM).The economic and environmental aspect like surface roughness, material removal rate and energy consumption were considered as sustainability factors. The model helps to understand the effect of the cutting parameters and conditions on surface finish, energy consumption, and material removal rate. The process was optimized for minimum power consumption considering environmental concern as prime importance. Studies suggest that the cutting environment and tool type influenced on the power consumption during turning process. Extended form of the proposed model could be useful to predict the environmental impact due to machining process, which would bring environmental concern into conventional machining.