Innovation and Sustainability for Automation, Aerospace, and Robotics

Table of Contents

1. Frontmatter

2. Additive Manufacturing and Biomaterials and Mechanics

   1. Frontmatter

   2. Mechanical and Metallurgical Characterization of Polylactic Acid and Natural Fiber Composite Manufactured by Fused Deposition Modeling

      R. J. Golden Renjith Nimal, R. Sabarish, J. Melvin Jones, D. F. Melvin Jose, R. Sangamaeswaran

      Abstract

      The aim of this study is to analyze the mechanical and metallurgical characteristics of polylactic acid (PLA) composites with the use of natural fibers that are manufactured by fused deposition modeling (FDM), which is a type of additive manufacturing. PLA is a biodegradable polymer sourced from a renewable resource, and it is almost always reinforced with natural fibers like jute, flax, and sisal to make the composite stronger and better for the ecology. The micron-sized tensile strength, flexural modulus, and impact resistance of the composites were assessed and matched against pure PLA. Besides, metallographical evaluation was additionally performed to study the effect of natural fibers on the composite of studied materials and fracture structure. The findings indicated that the addition of natural fibers remarkably enhances the mechanical loading and durability of PLA while still being biodegradable. The most important advantage of this research is the possibility of using PLA–natural fiber composites in the manufacture of products that must be lightweight, durable, and sustainable throughout their life cycles.

   3. Development and Testing of Al 2024/SiC/Ti/Nb Multi-metal Alloy for Fuselage Panels

      Divya Sri Pasala, M. John Iruthaya Raj, K. Anton Savio Lewise, R. Benmoses

      Abstract

      The aerospace industry continually seeks advanced materials to enhance aircraft performance, durability, and efficiency. This study focuses on the development and characterization of a novel multi-metal alloy composed of aluminium 2024, silicon carbide (SiC), titanium (Ti), and niobium (Nb) for potential application in fuselage panels. The alloy’s design aims to achieve a balance between strength, weight reduction, and resistance to environmental factors encountered during flight operations. The fabrication process involves powder metallurgy techniques, casting, and controlled heat treatments to achieve the desired mechanical properties. Subsequent testing encompasses a comprehensive evaluation of mechanical and corrosion resistance properties, utilizing techniques such as mechanical testing and ferric chloride corrosion testing. The results obtained from this research are expected to provide valuable insights into the feasibility and potential advantages of employing the developed multi-metal alloy in aerospace structural components, particularly fuselage panels, paving the way for enhanced aircraft performance and durability in the future aviation systems.

   4. Analysis of Microstructure and Microhardness of 17-4PH Steel by Wire Arc Additive Manufacturing

      C. Rajaravi, M. Selvam, R. Ganapathy Srinivasan, A. Thamarai Selvan

      Abstract

      This research investigates the feasibility of applying the gas tungsten wire arc additive manufacturing (GT-WAAM) process to fabricate components from 17-4PH steel. The study reveals that the process enables accurate fabrication without visible defects, and it evaluates the microstructure and microhardness of the material. Anisotropy is observed in both aspects. The microstructure consists of a nickel-γ matrix, characterized by elongated columnar dendrites and intermetallic phases in the interdendritic regions. Notably, Ni2Mo4C carbide is concentrated in the lower regions due to prolonged heat accumulation, while dislocation density and subgrain boundaries increase from top to bottom. The uniform microhardness across the as-deposited specimens is attributed to the influence of strong directional dendrites on microstructural evolution. This research contributes valuable insights into the relationship between additive manufacturing processes and material properties, paving the way for future advancements in metal additive manufacturing technologies.

   5. Thrust Force and Tool Wear Measurement in Friction Drilling Using Vibration Analysis

      N. Srilatha, V. V. D. Sahithi

      Abstract

      In friction drilling, a revolving conical tool softens and penetrates a thin workpiece using the heat produced by friction to form a bushing without producing chips. This study examines tool wear in friction drilling, a non-conventional method of creating holes. Tool wear is essential in friction drilling since it has an impact on the achievable tolerances. This study examined the tool wear properties of Al alloy and was subjected to experimental friction drilling with a tungsten carbide tool to investigate. Wear of the tool’s changes were measured to quantify the attributes by weight reduction and tool form. This research illustrates a method for tracking the condition of a tool during friction drilling, according to the accelerometer sensors’ vibration data. After the accelerometer sensors captured the signals, the raw data were filtered using a low-pass filter. According to the findings, tool wear in the drilling process is supported by the fact that tool wear and friction have a direct correlation with vibration amplitude. The thrust forces required for friction drilling at different feed rates and speeds are tracked using a drill tool dynamometer. A scanning electron microscope (SEM) is used to analyze the microscopic observations of the drilled holes. The thrust forces gradually increased when feed rates were maintained at a constant speed. In the case of aluminum, the microstructure photographs demonstrated strong workpiece adhesion and material transfer.

   6. A Review of Innovations in Eco-friendly Biomaterials for Sustainable Food Packaging Solutions

      V. Siva Shankar, G. Velmurugan, K. N. Hanumantharaju, M. Mouresh, E. Christy Ramola

      Abstract

      This review examines innovative green biomaterials for food packaging, emphasizing their critical role in mitigating plastic waste pollution. As awareness of consumers and regulations rises, the industry is seeking for advanced renewable resources. The authors divided primary advancements into natural polymers, polylactic acid (PLA) and other biodegradable plastics, composites, and active/smart packaging. Natural polymers are compostable materials, and thinner polylactic acid (PLA) and polyhydroxyalkanoates (PHA) biodegradable plastics are effective materials for performance. Composite material makes the absorption more active and active, and intelligent packaging devices make food safer for really active packaging. Their analysis of the life cycle assessment (LCA) shows that such new materials have a greater environmental benefit than conventional plastics. Despite these developments, production methods and consumer barriers remain some of the challenges that various stakeholders face. The message is that more research and engagement of relevant players is critical to enable the movement toward the green package. Employing these innovations, however, the food packaging industry will be able to minimize its negative impact on the environment and satisfy the needs of customers at the same time.

   7. Development of Edible Biopolymer Packaging Using Tamarind Seed Starch Incorporating Orange Essential Oil

      G. M. Chaitradeepa, K. N. Hanumantharaju, I. B. Shivaranjini, V. Siva Shankar, Chennappa Gurikar, A. C. Lokesh

      Abstract

      The study investigates the development and refinement of edible films using tamarind seed starch (TSS), glycerol, and orange essential oil (OEO) as sustainable alternatives to traditional plastic packaging. Different formulations with different quantities of glycerol (3, 4, and 5 ml), OEO (1, 2, and 3 ml), and TSS (4, 5, and 6%) were investigated. After a thorough analysis, the ideal composition—which had favorable mechanical and physical properties—was found to be 5% TSS, 4% glycerol, and 2% OEO. The resulting films had thicknesses between 0.16 and 0.24 mm and had effective moisture barrier properties with water vapor transmission rates (WVTR) between 0.0012 and 0.0017 g/h·m². Additionally, water solubility varied between 80.30 and 96.80%, indicating potential biodegradability, whereas tensile strength ranged from 0.01 to 0.1 MPa, suggesting moderate mechanical resilience. These results highlight the economic importance of using natural resources to design environmentally friendly packaging solutions, solving issues with plastic waste and promoting eco-friendly packaging industry practices.

   8. Novel Analytical Approach to Nonlinear Biodegradation Equations of n-Butanol in Biofilters Using Taylor’s Method

      V. Sreelatha Devi, K. Saranya

      Abstract

      In the present study, an innovative analytical technique for solving nonlinear biodegradation equations of n-butanol in biofilters is introduced, which uses Taylor's method. The purpose is to produce results that are accurate and efficient. Finally, the main purpose in creating this model is to predict the n-butanol concentration level in both phases such as biofilm and gas, which is important to optimize the biofiltration processes. The methodology used encompasses the application of Taylor series expansion in approximating the solutions of these complex nonlinear equations, which provide a means of an iterative solution that leads to a closed form. This method is indeed very important because it is reducing the complexity in terms of computation while providing the necessary reliable predictions, thereby making it very essential in designing and scaling biofilters. Results from this approach were found to be in very great correlation with existing numerical methods, thus validating correctness of the approach. This discussion shows the superiority of Taylor's method for nonlinear biodegradation processes, but it also opens avenues into adapting this for other types of volatile organic compounds and for various biofilter configurations. The work sets the way forward for further research to be done on biofiltration methods for volatile pollutants.

   9. Neural Network Model for Prediction of Mechanical Properties of Biodegradable Mg-Al-Zn-Ca Alloys

      A. Thamarai Selvan, R. Ganapathy Srinivasan, M. Selvam, C. Rajaravi

      Abstract

      In this paper, two models ANN and ANN + PSO were proposed to predict the mechanical properties of Magnesium (Mg) alloys, and the results were compared. Now-a-days, machine learning algorithms are used in various engineering applications in prediction of parameters and properties of materials, machining, and manufacturing. Twenty samples of as-cast Mg alloys with varying weight percentages of its constituents—Aluminum (Al), Zinc (Zn), Manganese (Mn) and Calcium (Ca)—were taken along with their yield strength (YS), ultimate tensile strength (UTS), and ductility (%) to build an artificial neural network (ANN) model for prediction of mechanical properties of Mg-Al-Zn-Ca alloys which play a vital role in making biodegradable implants for orthopedic applications. The weight percentages of Mg, Al, Zn, Mn, and Ca were taken as input data, and YS, UTS, and ductility were considered as target data in training the ANN model. The hidden layer with 2–10 neurons was considered to observe the performance of the model in predicting these properties. Levenberg–Marquart (LM) algorithm was used to train the input data, and transigma function was used as a learning function. The mean square error (MSE) and correlation coefficient (R²) were used to validate the optimality of the results. The weights of the neurons were optimized using particle swam optimization (PSO) to enrich the predicted results from ANN. The results obtained from ANN and ANN + PSO model show that the later performs better in prediction of mechanical properties of Mg-Al-Zn-Ca alloys.

   10. Integration of Voronoi Tessellation and Structure Optimization for Advanced Additive Manufacturing Applications

       K. Karthik, R. Ramesh Kumar, S. Balaguru, P. Jagadeesh, S. P. Gowtham

       Abstract

       Additive Manufacturing (AM) and Voronoi tessellation are a new dimension in material design and manufacturing, exploiting computational geometry to extract maximum structural performance from the material. This is done by the Voronoi decomposition of the design space into separate cellular regions based on proximity principles to create complex lightweight structures that exhibit enhanced mechanical properties. By strategically placing materials into interconnected polygonal cells via this technique, one may control the porosity, density, and mechanical response at microscopic scales. The relevant applications of this method are in the aerospace, biomedical, and advanced engineering fields where weight savings and very specific characteristic properties of material are crucial. Voronoi-based AM can generate metamaterials with customized mechanical responses, irregular geometric complexities, and optimum performance qualities, which cannot be manufactured by classical methods, through precision computational algorithms and sophisticated three-dimensional printing technology.

3. Automation and Smart Systems

   1. Frontmatter

   2. Enhancing Sustainability in Intelligent Transportation Systems with Machine Learning-Based Misbehavior Detection

      Azra Nazir, Faisal Rasheed Lone, Ashfaq Ahmad, Suhaib Ahmed, Arhum Qureshi

      Abstract

      Vehicular ad hoc networks (VANETs) are vital to enhance traffic management and safety in Intelligent Transportation Systems (ITS). However, misbehaving nodes can compromise these benefits by introducing and spreading false information, creating safety risks such as accidents and phantom traffic jams. This paper explores machine learning techniques for detecting misbehavior in VANETs, focusing on speed and position-related attacks. The proposed model demonstrates robust detection capabilities against speed and position offset attacks, utilizing features derived from basic safety messages (BSMs). Evaluated on the VeReMi dataset, optimized tree-based models achieve a detection accuracy of 91%, with a 36% reduction in training time. This dual emphasis on security and environmental sustainability highlights the role of misbehavior detection in creating more safer, efficient, and eco-friendly transportation systems.

   3. Deep Learning for Network Intrusion Detection: Attention-Based Bidirectional Modified Gated Recurrent Unit

      M. Karthigha, L. Latha, K. Sripriyan

      Abstract

      Network intrusion detection plays a crucial role in safeguarding computer systems and networks against malicious activities. With the increasing complexity and sophistication of cyber threats, there is a growing demand for advanced intrusion detection systems capable of effectively identifying and mitigating various forms of attacks. In this paper, the Bidirectional Modified Gated Recurrent Unit (M-GRU) with Attention Mechanism is another variant of the original GRU architecture that has been modified to improve its performance in classifying network intrusions. It combines the benefits of bidirectional processing, modified GRU cells, and attention mechanisms for enhanced performance. In experiments, the M-GRU with attention mechanism has shown improved performance over AI models for network intrusion classification.