4D printing technology combines additive manufacturing with materials that can change shape or properties over time, enabling objects to self-assemble, self-repair, and adapt to their environment. It has potential applications in robotics, autonomous systems, energy and environmental systems, and smart materials for energy storage and distribution. The text discusses the potential of 4D printing technology and its role in shaping the future of wearable technology, robotics, energy, transportation, and fashion industries. It looks at future applications of 4D printing in fashion and design, such as dynamic and customizable clothing and accessories, shape-changing jewelry and wearable art, responsive and interactive fashion shows and events, and sustainable and adaptive fashion manufacturing processes. It emphasizes the importance of continued research and development to unlock the full potential of 4D printing and its transformative impact on various industries.

This chapter explores the integration of IoT and AI technologies to optimize biomass-to-biofuel conversion processes. AI algorithms can be used to optimize process parameters such as temperature, pressure, and enzyme dosage, leading to increased biofuel yields, reduced energy consumption, and improved quality control. Sustainability assessment is also highlighted, with IoT and AI playing a crucial role in monitoring and analyzing sustainability metrics. Companies such as Pacific Ethanol, Renmatix, IOCL, and GranBio have achieved significant improvements in biofuel yield, energy efficiency, quality control, and sustainability by leveraging IoT and AI technologies. These advancements inspire potential applications and strategies in different biomass feedstock scenarios, enabling organizations to drive the transition towards cleaner and more sustainable energy sources while improving operational efficiency and competitiveness.

The current research deals with Taguchi-coupled grey relational analysis (GRA) multiobjective optimization of wire electric discharge machining (WEDM) for better surface roughness (Ra) and the material removal rate (MRR) over magnesium/halloysite nano tube/zirconium (Mg/HNT/Zr) metal matrix composite (MMC). Hybrid composites are created through the powder metallurgy route by varying the weight percentage of reinforcements HNT (5 and 10%) and Zr (0.5 and 1%) to the weight of the base material magnesium. Machining is carried out by varying the factors such as reinforcement’s weight percentage, pulse OFF time, pulse ON time, and wire feed (WF) based on Taguchi’s L27 orthogonal array. The response surface roughness (Ra) and the material removal rate (MRR) were studied through Taguchi-coupled GRA to evaluate the optimized machining parameters. ANOVA results reveal the percentage contribution of certain factors over the machining of composites. The developed regression model proved that the predicted values were merely similar to the experimental values of MRR and Ra. The best parametric combinations obtained from optimization are inline as the minimum weight percentage of reinforcements, and higher Pon, higher WF, and the lowered Poff are used to attain the best rate of MRR during machining and for minimized surface roughness.

The application of ANN in forecasting wear rates during the manufacturing of novel hybrid composites is crucial for advancing material science, particularly in industries where wear resistance is critical, such as aerospace, automotive, and manufacturing. This present study is an investigation to form a hybrid novel metal composite of Aluminium alloy ADC12/BS/TiB₂. The nanoparticles of biosilica and titanium diboride of varying weight percentages were added to the ADC12 matrix alloy, and the hybrid composite was fabricated via a stir-casting. A pin-on-disc wear device was used to determine the hybrid composites' dry sliding wear studies over a range of weights, velocities, and distances. Using ANOVA, the effect of material type on wear rate is determined to be statistically significant. ANN employs the Levenberg-Marquardt, which is aimed to reduce MSE using a back-propagation strategy. ADC12/BS/TiB₂ hybrid composites experimental wearrate has been predicted using ANN models, and it has been determined that these forecasts exhibit excellent agreement with intended values. The use reinforcements have been shown by experimental and ANN technologies to significantly lower wear rates and the results indicated that for the load of 20 N, velocity of 3 m/s, and distance of 2500 m, a minimum wear of 0.00596 mm³/m may be attained.