This research introduces a robust control design for multibody robot systems, incorporating sliding mode control (SMC) for robustness against uncertainties and disturbances. SMC achieves this through directing system states toward a predefined sliding surface for finite-time stability. However, the challenge arises in selecting controller parameters, specifically the switching gain, as it depends on the upper bounds of perturbations, including nonlinearities, uncertainties, and disturbances, impacting the system. Consequently, gain selection becomes challenging when system dynamics are unknown. To address this issue, an extended state observer (ESO) is integrated with SMC, resulting in SMCESO, which treats system dynamics and disturbances as perturbations and estimates them to compensate for their effects on the system response, ensuring robust performance. To further enhance system performance, deep deterministic policy gradient (DDPG) is employed to fine-tune SMCESO, utilizing both actual and estimated states as input states for the DDPG agent and reward selection. This training process enhances both tracking and estimation performance. Furthermore, the proposed method is compared with the optimal-PID, SMC, and $H_{\infty }$ in the presence of external disturbances and parameter variation. MATLAB/Simulink simulations confirm that overall, the SMCESO provides robust performance, especially with parameter variations, where other controllers struggle to converge the tracking error to zero.

The electrical discharge machining (EDM) process is one of the most efficient non-conventional precise material removal processes. It is a smart process used to intricately shape hard metals by creating spark erosion in electroconductive materials. Sparking occurs in the gap between the tool and workpiece. This erosion removes the material from the workpiece by melting and vaporizing the metal in the presence of dielectric fluid. In recent years, EDM has evolved widely on the basis of its electrical and non-electrical parameters. Recent research has sought to investigate the optimal machining parameters for EDM in order to make intricate shapes with greater accuracy and better finishes. Every method employed in the EDM process has intended to enhance the capability of machining performance by adopting better working conditions and developing techniques to machine new materials with more refinement. This new research aims to optimize EDM’s electrical parameters on the basis of multi-shaped electrodes in order to obtain a good surface finish and high dimensional accuracy and to improve the post-machining hardness in order to improve the overall quality of the machined profile. The optimization of electrical parameters, i.e., spark voltage, current, pulse-on time and depth of cut, has been achieved by conducting the experimentation on die steel D2 with a specifically designed multi-shaped copper electrode. An experimental design is generated using a statistical tool, and actual machining is performed to observe the surface roughness, variations on the surface hardness and dimensional stability. A full factorial design of experiment (DOE) approach has been followed (as there are more than two process parameters) to prepare the samples via EDM. Regression analysis and analysis of variance (ANOVA) for the interpretation and optimization of results has been carried out using Minitab as a statistical tool. The validation of experimental findings with statistical ones confirms the significance of each operating parameter on the output parameters. Hence, the most optimized relationships were found and presented in the current study.

High-speed machining is considered to be a promising machining technique due to its advantages, such as high productivity and better product quality. With a paradigm shift towards sustainable machining practices, the energy consumption analysis of high-speed machining is also gaining ever-increasing importance. The current article addresses this issue and presents a detailed analysis of specific cutting energy (SCE) consumption and product surface finish (Ra) during conventional to high-speed machining of Al 6061-T6. A Taguchi-based L16 orthogonal array experimental design was developed for the conventional to high-speed machining range of an Al 6061-T6 alloy. The analysis of the results revealed that SCE consumption and Ra improve when the cutting speed is increased from conventional to high-speed machining. In particular, SCE was observed to reduce linearly in conventional and transitional speed machining, whereas it followed a parabolic trend in high-speed machining. This parabolic trend indicates the existence of an optimal cutting speed that may lead to minimum SCE consumption. Chip morphology was performed to further investigate the parabolic trend of SCE in high-speed machining. Chip morphology revealed that the serration of chips initiates when the cutting speed is increased beyond 1750 m/min at a feed rate of 0.4 mm/rev.

Metal matrix composites (MMCs) have wide applications due to being lightweight, their high strength, and immense resistance to wear. To explore new generation materials like aluminum-based metal matrix composites (AMCs) for wide engineering applications, the present work aimed at investigating the effect of changes in composition, sintering time, and temperature on the hardness and surface roughness of AMCs containing SiC and ZrSiO₄ in wt % of 5, 20, 30, and 40 binary and hybrid sample pallets. The samples have been prepared by powder metallurgy (PM) method under 1000 psi pressure. After compaction, the above pallets sintered at different temperatures ranging from 500 °C to 1100 °C with an increment of 200 °C and 15 min intervals for four levels of temperature and time, respectively. Afterwards, sensitivity analysis has been done by investigating the effect of chemical composition, sintering time, and sintering temperature of the binary and hybrid composites on hardness and surface roughness. Morphological studies on the composites were carried out using field emission scanning electron microscope (FESEM) with energy dispersive spectroscopy (EDS). It has been observed that hardness is increased by increasing the sintering temperature in the case of SiC, whereas surface roughness did not change much by changing the composition. Additionally, a rise in temperature lead to liquid-state sintering. SEM images obtained during the elemental analysis showed that porosity is generated within the samples after sintering due to the higher melting point of reinforcements compared to a base metal, i.e., aluminum. Mathematical equations have also been developed via regression analysis using Minitab and excel for the confirmation and validation of experimental data. Analysis of Variance (ANOVA) has also been done, and its tables are shown and discussed in the paper. Hence, the most optimized findings relating the changes in the composition of reinforcements, sintering temperature, and sintering time (input variables) with porosity, hardness, and surface roughness have been presented in the current study.

Synchromodality is the key to finding sustainable solutions for logistics, especially across larger networks. The era of the COVID-19 pandemic has brought special attention to the disruptions in demand and supply across the world and has accentuated the need for sustainable transportation networks to handle such anomalies in supply chains. The proposed research develops a mathematical model for an intermodal transportation network and investigates the model on one of the largest and most widely discussed supply chain projects of the One-Belt-One-Road (OBOR) initiative. The proposed bi-objective model focuses on time and cost functions with rail, roads, and ships as modes of transportation. A detailed analysis was performed on various mode alternatives and links to evaluate their performance. The study provides an insightful understanding of the network with several suggestions. In contrast to roads and trains, container ships depict a fourfold increase in fuel consumption for an average ship weighing 4500 TEUs with the increase in shipping speed. It was concluded that increasing port capacity and reducing custom clearance time can have a major impact on lead times, and this is directly influenced by a country’s ease of doing business. Moreover, with its several branches, the OBOR initiative can provide a robust supply chain with increased logistical capacity.

Aluminum metal matrix composites (AMMCs) have become increasingly ubiquitous in the fields of aerospace and automobile businesses due to their lightweight properties. Their machining is a challenging task because of the presence of supplemented particles, also called reinforcements. As the wt% of the supplemented particles changes, the morphological and machining behaviors of the AMMCs change. The present work is focused on exploring the thermo-mechanical properties of AMMCs which would help in AMMC applications in the aerospace industry with a new collection of composites containing silicon carbide (SiC) and zircon/zirconium silicate (ZrSiO₄) as supplements in wt% of 5%, 20%, 30%, and 40%. Uniform binary and hybrid sample pallets are prepared by powder metallurgy (PM). The said samples are sintered and then machined using wire electric discharge machining (WEDM) employing brass wire with a feed rate of 2 to 3 mm/min. Also, analysis of porosity and recast layer formation is performed via the microstructure, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). Some interesting and useful findings are obtained which can extend the applications of AMMCs in automobiles and the aerospace industry. The results reveal that temperature and wt% are playing their significant roles in the changes in the thermo-mechanical properties of AMMCs. Mathematical equations via regression analysis using Minitab 17 and Excel are developed for the congruence of experimental data. Analysis of Variance (ANOVA) is also performed. Hence, the most optimized relationships for the best machining output are established and presented in this proposed study.