Search results

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

This study aims to provide a salt ready-mix to instant fried noodles manufacturers.

Response surface methodology was used to get optimized salt ready-mix based on carbonate salt, disodium phosphate, tripotassium phospahte, sodium hexametaphosphate and sodium chloride. Peak viscosity of flour and yellowness, cooking loss and hardness of noodles were considered as response factors for finding optimized salt formulation.

The results showed that salts have an important role in governing quality of noodles. Optimum levels of five independent variables of salts, namely, carbonate salt (1:1 mixture of sodium to potassium carbonate), disodium phosphate, sodium hexametaphosphate, tripotassium phosphate and sodium chloride were 0.64%, 0.29%, 0.25%, 0.46% and 0.78% on flour weight basis, respectively.

To the best of the authors’ knowledge, this is the first study to assess the effect of different combinations of different salts on the quality of noodles. These findings will also benefit noodle manufacturers, assisting in production of superior quality noodles.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

This study explores how titanium oxide (TiO₂) filler influences the specific wear rate (SWR) in flax fiber-reinforced epoxy composites (FFRCs) through a Taguchi approach. It aims to boost abrasive wear resistance by incorporating TiO₂ filler, promoting sustainable and eco-friendly materials.

This study fabricates epoxy/flax composites with TiO₂ particles (0–8 wt%) using hand layup. Composites were tested for wear following American Society for Testing and Materials (ASTM) G99-05. Statistical analysis used Taguchi design of experiments (DOE), with ANOVA identifying key factors affecting SWR in abrasive sliding conditions.

The study illuminates how integrating TiO2 filler particles into epoxy/flax composites enhances abrasive wear properties. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, wt% of TiO₂ and sliding distance. Grit size has the highest effect at 43.78%, and wt% TiO₂ filler contributes 15.61% to SWR according to ANOVA. Notably, the Taguchi predictive model closely aligns with experimental results, validating its reliability.

This paper integrates TiO₂ filler and flax fibers to form a novel hybrid composite with enhanced tribological properties in epoxy composites. The use of Taguchi DOE and ANOVA offers valuable insights for optimizing control variables, particularly in natural fiber-reinforced composites (NFRCs).

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

The application of indigo dyes in the denim industries has been criticised due to the introduction of non-renewable oxidation products into the environment. Previous studies have investigated that reducing sugars can be used as green alternatives to sodium dithionite in the indigo dyeing of cotton fabric owing to their reduced and stable redox potential in the dye bath. The purpose of this study was to dye denim cotton fabric with indigo dye using various reducing sugars and alkalis. The use of sucrose and potassium hydroxide (KOH) for indigo dyeing has been explored for the first time.

A mixed factorial design with four variables including alkali, pH, number of dips and type of reducing sugar at different levels was studied to identify a significant correlation between the effect of these variables on the colour strength and fastness properties of the dyeings.

Investigations were made to examine the significant factors and interactions of the selected responses in the eco-friendly dyeing method. This process has the potential to reduce the load of sulphite and sulphate generated in the dyebath due to the use of a conventional reducing agent, sodium dithionite. The colour strength of the dyeing reduced with fructose was found to be better than other reducing sugars and significantly influenced by the number of dips, pH levels and the interaction between pH and reducing sugars. Using fructose for indigo dyeing with two dips at a pH of 11.5, using KOH as an alkali, results in higher colour strength values. The fastness properties of the indigo-dyed sample with reducing sugars ranging from fair to good or good to excellent. Specifically, colour change receives a rating of grey scale 3–4, staining 4–5, dry rubbing 4 and light fastness 3–4. These assessments hold true across various factors such as the type of reducing sugar, alkali, pH and the number of dips. The optimised parameters leading to improved colour strength and fastness properties are also discussed.

This dyeing technique is novel and a green alternative to dithionite denim dyeing. This process is found to be useful for indigo dyeing of denim fabric leading to reduced and stable redox potential in the dyebath and acceptable colour strength of the dyed fabric.

The purpose of this study is to determine the effect of laser treatment on disperse dye-uptake and fastness values of polyester fabrics. Furthermore, it was aimed to evaluate colors directly over the photos of fabric samples instead of color measuring with spectrophotometer which is thought to be useful in terms of online digital color assessment.

In this study, 100% polyester (150 denier) single jersey knitted fabrics (weight: 145 g/m2, course density: 15 loops/cm, wale density: 24 loops/cm) were used in the trials. The effect of laser treatments before and after dyeing on color was investigated. Laser treatments were applied to fabrics at different resolutions (20, 25 and 30 dpi) and pixel times (60, 80 and 100 µs) before dyeing. The power of the laser beam was 210 W and the wavelength was 10.6 µm. In order to determine the effect of laser treatment on polyester; FTIR analysis, SEM-EDX analysis and bursting strength tests were applied to untreated and treated fabric samples.

It was found that treatments with laser have a significant effect on disperse dye-uptake of polyester fibers, and for this reason laser-treated fabrics were dyed in darker shade. Furthermore, it was determined that the samples treated at 30 dpi started to melt and the fabric was damaged considerably, but the fabrics treated at 20 and 25 dpi were not affected at all. Another result obtained regarding the use of laser technology in polyester fabrics is that if some areas of fabrics are not treated with laser and some other areas are treated with laser at 20 dpi 60 µs and 25 dpi 60 µs, it will be possible to obtain patterns containing three different shades of the same color on the fabric.

When the literature is examined, it is seen that there are various studies on the dyeability and patterning of polyester fabrics with disperse dyes by laser technology. As it is known, today color measurement is done digitally using a spectrophotometer. However, when we look at a photograph on computer screens, the colors we see are defined by RGB (red-green-blue) values, while in the spectrophotometer they are defined by L*a*b* (L*: lightness-darkness, a*: redness-greenness, b*: yellowness-blueness) values. Especially when it is desired to produce various design products by creating patterns with laser technology, it would be more useful to show the color directly to the customer on the computer screen and to be able to speak over the same values on the color. For this reason, in this study, the color measurement of the fabric samples was not made with a spectrophotometer, instead, the RGB values obtained from the photographs of the samples were converted into L*a*b* values with MATLAB and interpreted, that is, a digital color evaluation was made on the photographs. Therefore, it is believed that this study will contribute to the literature.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.