Design of Experiments (DOE) is a powerful statistical technique commonly employed in scientific research and industrial settings to explore the relationships between multiple factors and their impact on a response variable. This method allows researchers to efficiently investigate the influence of various input variables (factors) on an output variable, helping to optimize processes and make informed decisions. One of the key advantages of DOE is its ability to discern which factors are significant contributors to variations in the response variable, providing valuable insights into the underlying mechanisms of a system or process [2] .

The fundamental principle of DOE involves manipulating input factors or variables to observe their impact on the output or response variable. This method allows researchers and engineers to understand the relationships between different factors and identify the optimal combination for achieving desired results. One of the pioneers in the field of DOE is Sir Ronald A. Fisher, whose groundbreaking work laid the foundation for experimental design. Fisher introduced the concept of randomization and replication to ensure the reliability and validity of experimental results. His seminal book, “The Design of Experiments,” published in 1935, remains a cornerstone reference in the field. Fisher’s contributions revolutionized statistical methods and experimental design, making DOE an indispensable tool in scientific research and industrial applications.

Modern applications of DOE extend across diverse industries such as manufacturing, healthcare, and technology. As technology has advanced, computer-aided design and statistical software have become integral in implementing sophisticated experimental designs. Researchers continue to refine and expand the principles of DOE, contributing to its ongoing evolution and widespread adoption in fields where systematic experimentation is crucial for innovation and improvement [3] .

Control and randomization are essential aspects of experimental design. Randomization serves the crucial role of ensuring that extraneous factors do not introduce bias into the results of an experiment. It helps in creating a fair and unbiased distribution of these factors across different experimental conditions.

Replication is another key principle in experimental design, involving the repetition of experiments multiple times. This repetition allows researchers to assess the variability in the results and enhance the reliability of their findings by identifying consistent patterns or trends.

Blocking is a technique that involves grouping similar experiments together to account for known sources of variability. By doing so, researchers can minimize the impact of specific factors and ensure a more accurate evaluation of the experimental variables.

Factorial designs represent an approach where researchers study multiple factors at different levels simultaneously. This enables a more comprehensive understanding of the interactions between various factors and their collective influence on the outcomes of the experiment.

Response surface methodology is a statistical technique used for modeling and optimizing responses over a range of factor levels. This method helps researchers explore the relationship between input variables and the corresponding output responses, allowing for the identification of optimal conditions for the desired outcomes.

Minitab is a comprehensive statistical software package designed to facilitate experimental design and analysis. With its robust capabilities, the software empowers users to efficiently plan and execute experiments, generate data, and conduct statistical analyses. The user-friendly interface of Minitab is tailored to simplify the often complex processes involved in experimental design and analysis.

One of the key strengths of Minitab lies in its ability to create various types of experimental designs. These include factorials, response surface designs, and custom designs. Factorial designs allow users to study the effects of multiple factors simultaneously, providing a holistic understanding of how different variables interact. Response surface designs are useful for exploring the relationship between input variables and response variables, helping users optimize processes or products. Additionally, Minitab allows the creation of custom designs, providing flexibility for specific experimental requirements.

The software goes beyond just design capabilities; it also facilitates the generation and analysis of data. Users can input data into Minitab, and the software provides a range of statistical tools to analyze the data effectively. This analysis is crucial for evaluating the impact of various factors on a chosen response variable. By doing so, Minitab enables users to draw meaningful insights from their experiments, aiding in decision-making and process improvement.

In summary, Minitab is a powerful tool that streamlines the entire process of experimental design and analysis. It empowers users to design experiments, generate data, and perform statistical analyses in a user-friendly environment, ultimately helping researchers, scientists, and analysts gain valuable insights into the factors influencing a given response.

ANOVA is a statistical technique commonly used in Minitab to examine variance in a response variable among different groups or levels of a categorical variable. This technique assists researchers in determining whether or not there are any statistically significant differences between the means of these groups. Minitab has an easy-to-use interface for performing ANOVA, with a variety of tools and features to help with the analysis.

Users often begin by entering their data into Minitab before performing ANOVA. This can be accomplished by manually entering data or by importing data from external sources. After loading the data, users can go to the “Stat” menu, pick “ANOVA,” and then choose the proper ANOVA analysis based on their experimental design. Minitab supports a variety of sorts.

Minitab generates thorough output for each group, including crucial statistics such as the F-value, p-value, and means. The F-value is a ratio of variance between groups to variance within groups. A significant p-value (usually less than 0.05) indicates that there are significant differences between the means of at least two groups. Minitab’s output also offers graphical representations of results, such as boxplots and interaction plots, which can help with interpretation. Minitab is designed to handle both balanced and unbalanced data sets, giving it a versatile tool for ANOVA analysis in a variety of research contexts. Additionally, when ANOVA reveals significance, Minitab enables post-hoc tests, such as Tukey’s multiple comparison tests, to discover particular group differences. These follow-up tests are critical.

In general, Minitab streamlines the ANOVA process by providing a simple interface and a variety of options for assessing variation between groups. Minitab’s features can help researchers and analysts investigate the importance of group differences and obtain insights into the causes causing the variability in their data [4] .

The selection of injection molding, extrusion, VARTM (Vacuum Assisted Resin Transfer Molding), and 3D printing among various manufacturing processes is based on their distinct capabilities and suitability for different applications, encompassing a range of material properties, production volumes, and geometric complexities. This combination allows for a versatile and comprehensive approach to meet diverse manufacturing requirements across industries, ensuring optimal efficiency and quality in the fabrication of various products.

Injection molding is a widely used manufacturing process in which molten plastic material is injected into a mold cavity, cooled, and then ejected as a solid product as shown in Figure 1. In the literature, numerous studies have highlighted the importance of optimizing process parameters such as temperature, pressure, and cooling time to achieve desired product quality and minimize defects (Chen et al., 2018; Lee et al., 2019) [5] . Moreover, research has focused on the development of advanced materials for injection molding, including bioplastics and nanocomposites, to enhance product sustainability and performance (Materon et al., 2020; Xiong et al., 2017) [6] [7] . Additionally, efforts have been made to incorporate Industry 4.0 technologies such as IoT and machine learning for real-time monitoring and control of injection molding processes, enabling higher efficiency and reduced waste (Meng et al., 2021; Tao et al., 2018) [8] [9] . The descriptive statistics of the Injection molding parameters are presented in Table 1.

A thorough survcy of the literature on the issue of “extrusion” reveals a plenty of research on many facets of this adaptable manufacturing method. Notably, Karimi-Maleh et al. (2020) outline developments in extrusion technology for the manufacturing of bio-based materials, emphasizing their sustainability and environmental advantages [19] . Kalyon and Ozer (2019) also investigated the mechanical and rheological characteristics of polymers during extrusion, offering insight on the relevance of process factors in obtaining desirable material qualities as shown in Figure 2 [20] . Furthermore, Muthukumar and Vora (2018) investigated the use of computer modeling to improve the extrusion process, stressing its potential for increasing productivity and lowering energy consumption in a variety of sectors [21] . These studies, taken together, highlight the various characters of extrusion and its importance in modern manufacturing. In Table 2, the extrusion parameters’ statistics are presented, showcasing the characteristics as outlined in the paper.

Several major trends and breakthroughs in Vacuum Assisted Resin Transfer Molding (VARTM) have emerged from the literature analysis. Researchers have stressed the importance of VARTM as a flexible and cost-effective composite material production technique (Liu et al., 2017) [31] . Specifically, Wang et al. (2019) have focused on refining the infusion process by exploring several factors such as fiber orientation, resin flow, and tooling design to improve the mechanical characteristics and overall quality of VARTM-produced composites [32] . It involves the infusion of liquid resin into a dry reinforcement preform under a vacuum to create high-performance composite structures.as illustrated in Figure 3. The paper provides the statistics describing the VARTM parameters (See Table 3).

The literature review on the topic of “3D printing” reveals a rapidly evolving field with a wide range of applications. According to Smith et al. (2017), 3D printing technology has gained significant attention in recent years due to its potential to revolutionize manufacturing processes across industries [33] . This sentiment is echoed by Jones and Johnson (2018), who argue that 3D printing has the capacity to disrupt traditional supply chains, reduce production costs,

and enable mass customization [42] . The researchers YP Shaik, and J Schuster (2022) look at the influence of high ambient pressure on layer consolidation during the FDM process, as well as the mechanical characteristics of the layers [43] [44] . To attain high strength qualities for 3D printed components similar to injection-molded specimens, an experimental setup consisting of a 3D printer incorporated within a modified Autoclave was put up [45] . As the technology continues to advance, it presents both opportunities and challenges, from intellectual property concerns to material limitations, necessitating further research to fully harness its potential [46] . The descriptive statistics of the 3D printing process parameters reported in the paper are presented in Table 4. The illustration of a sample 3D printing process is shown in Figure 4.

Four manufacturing processes are employed in this project: injection molding, extrusion, vartm and 3D printing. Four factors affect the process and four output parameters are chosen for each operation. Each input parameter is varied using the four output parameters. In addition, the relevance of input parameters is evaluated for each production step.

Minitab’s capabilities in injection molding DOE extend to graphical tools that facilitate result interpretation. The software provides insightful graphs, such as main effects plots and interaction plots, which assist in visualizing the impact of different variables on the response variables [21] . Additionally, Minitab offers optimization tools like response surface methodology (RSM) that enable users to fine-tune process settings to achieve desired product quality and minimize defects, enhancing the efficiency and cost-effectiveness of the injection molding process [56] .

Furthermore, Minitab supports the use of robust parameter design (RPD) techniques to make the injection molding process more resilient to variations and external factors. This comprehensive software is widely adopted in the field, allowing practitioners to systematically and statistically optimize their injection molding processes while maintaining high product quality and minimizing waste [57] .

The factors influencing the process taken in this manufacturing process include holding pressure, injection speed, mold temperature, and melt temperature whereas the output parameters are dimensional accuracy, warpage, shrinkage, and cycle time. Each output parameter has a varied significance in terms of the factors that affect the process.

When it comes to dimensional precision, the following factors are important: injection speed, melt temperature, mold temperature, and holding pressure, respectively [58] . In terms of warpage, the order is as follows: injection speed, holding pressure, mold temperature, melt temperature [59] . When it comes to shrinkage, the order of importance is Melt temperature, Injection speed, Mold temperature, and holding pressure [60] . Finally, when it comes to cycle time, the order of importance is melt temperature, injection speed, mold temperature, and holding pressure [61] .

When conducting a DOE for the extrusion process, Minitab, a widely used statistical software, can be a valuable tool. In this context, a three-factor, full-factorial design could be employed to investigate the influence of critical process parameters on product quality. Factors such as extrusion temperature, screw speed, and die design can be considered. By using Minitab, researchers can efficiently generate the experimental design matrix, perform the experiments, and analyze the results. This approach helps in determining the optimal combination of factors that lead to the desired product characteristics, thus reducing defects and improving the efficiency of the extrusion process [24] .

Extrusion temperature, feed rate, screw speed, and l/d ratio are the parameters that influence the extrusion process. Furthermore, dimensional accuracy, tensile strength, elongation, and defects are captured as output characteristics. Each output attribute is significant for the components influencing the extrusion process in a distinct way.

In terms of dimensional accuracy, the following characteristics are significant: Extrusion temperature, l/d ratio, screw speed, and feed rate in that sequence, and if tensile strength is included, the process parameters are extrusion temperature, screw rpm, feed rate, and l/d ratio influenced in order. When elongation is factored under consideration, the process variables governing the Extrusion process contain extrusion temperature, screw rpm, feed rate, and l/d ratio. Finally, when defects are considered, the most essential variables are Extrusion temperature, screw rpm, feed rate, and l/d ratio [62] .

The Vacuum Assisted Resin Transfer Molding (VARTM) process is a popular and innovative method for manufacturing composite materials. It involves the infusion of liquid resin into a dry reinforcement preform under a vacuum to create high-performance composite structures. VARTM offers several advantages, such as cost-effectiveness, reduced emissions, and the ability to produce complex shapes. Jens Schuster, in his research on composites, has highlighted the significance of VARTM in the aerospace and automotive industries, where lightweight and strong materials are essential for improving fuel efficiency and performance. Yousuf Shaik has also contributed to the understanding of VARTM, emphasizing its potential in applications like wind turbine blade manufacturing, as the process allows for the production of large and durable composite structures.

VARTM operates by drawing resin through the dry reinforcement using a pressure differential, facilitated by a vacuum. The method is environmentally friendly, as it minimizes the release of volatile organic compounds compared to traditional methods like open molding. Jens Schuster’s work underscores VARTM’s ability to produce composite parts with exceptional mechanical properties, including high strength-to-weight ratios and resistance to corrosion. Yousuf Shaik’s research complements this by demonstrating the process’s adaptability for creating composite structures in various industries, ensuring that VARTM remains a valuable technique for achieving lightweight, durable, and environmentally sustainable materials in the modern world of advanced manufacturing [63] .

The criteria influencing the manufacturing process in this case are resin viscosity, resin gel time, fibre orientation, and fibre volume fraction. Tensile strength, resin saturation, surface finish, and curing time are the output parameters examined. In terms of the elements that influence the process, each output parameter has varying importance.

Tensile strength can be impacted by the following parameters: resin viscosity, resin gel time, fibre orientation, and fibre volume fraction in order. The resin saturation influences the procedure in a specific order: Resin viscosity, Fibre volume fraction, Resin gel time, and Fibre Orientation. If Surface finish is taken into account, the parameters influence is Resin viscosity, Resin gel time, Fibre Orientation, and Fibre volume fraction respectively. Involving curing time, the order of importance is Resin Viscosity, Resin Gel Time, Fibre Orientation, and Fibre Volume Fraction [32] .

In this context, Minitab, a statistical software package, can be employed to conduct an Analysis of Variance (ANOVA) to analyze the effects of various factors on the 3D printing process and identify the optimal printing conditions. For example, a three-factor DOE might consider factors such as print temperature, layer height, and print speed. By varying these factors systematically and using Minitab for ANOVA, one can determine the significant factors and their interactions, enabling the identification of optimal parameter settings for improved print quality and efficiency [64] .

Furthermore, Minitab’s ANOVA capabilities allow for the investigation of interactions between these factors, helping to unveil complex relationships in the 3D printing process. By utilizing Minitab, researchers and engineers can analyze experimental data to identify which factors have the most significant impact on outcomes like print strength, layer adhesion, or print time [65] . This knowledge can inform process improvements and lead to more consistent and reliable 3D prints [66] .

In summary, the combination of Design of Experiments and Minitab ANOVA offers a systematic approach to optimize 3D printing processes. Through carefully designed experiments and statistical analysis, it is possible to identify the key factors and interactions that influence 3D print quality and efficiency, leading to more robust and effective additive manufacturing processes [67] .

Layer thickness, printing temperature, printing speed, nozzle diameter are the factors taken impacting the 3D printing process, and the output characteristics include mechanical strength, dimensional accuracy, print defects, and print time. The relevance of the affecting elements varies for each output metric.

To begin, while considering mechanical strength, the characteristics in order of importance are nozzle diameter, layer thickness, printing speed, and printing temperature [68] [69] [70] . When dimensional precision is taken into consideration, the following features have an impact: printing speed, layer thickness, printing temperature, and nozzle diameter [71] [72] . When it comes to print defects, the parameters influencing the process are, in order, layer thickness, printing speed, printing temperature, and nozzle diameter [73] [74] . Finally, Considering the printing time, the variables that influence 3D printing are layer thickness, printing speed, nozzle diameter, and printing temperature respectively [75] [76] [77] .

In this manufacturing process, the order of relevance of the factors impacting injection molding is illustrated in the DOE software tool MINITAB (See Figure 5). The ANOVA one-way analysis is conducted in the minitab software. Significance is given a value of 1 - 10 on the Y-axis, and parameters which are affecting the process are indicated on the X-axis.

The order of importance of the parameters controlling extrusion in this manufacturing process is illustrated in the DOE software tool MINITAB. The Minitab software is used for the ANOVA one-way analysis (Figure 6). On the Y-axis, significance is scaled from 1 to 10, and parameters are displayed on the X-axis.

In this manufacturing process, the order of relevance of the factors impacting the VARTM is shown in the DOE software tool MINITAB (Turn to Figure 7). The ANOVA one-way analysis is carried out using the minitab program. The Y-axis displays significance with a scale of 1-10, and the X-axis displays parameters.

In this manufacturing process, the order of importance of the parameters affecting 3D Printing is represented in the DOE software tool MINITAB. The Minitab program is used for the ANOVA one-way analysis (Refer to Figure 8). On the Y-axis, significance is represented as a score from 1 to 10, while parameters are listed on the X-axis.