Hard turning is a precision machining process used in the manufacturing industry for the finishing of hardened alloy steel, which is known for its high hardness and wear resistance. In this work, an experimental investigation was conducted to predict surface roughness and cutting temperature during the hard turning of AISI 52100 steel using the minimal cutting fluid application (MCFA). The MCFA is a sustainable high-velocity pulsed jet technique that has emerged as an eco-friendly approach for reducing the environmental impact and improving surface integrity in machining processes. The influence of key machining parameters, such as cutting speed, feed rate, and depth of cut, on the performance indicators was modeled using the response surface methodology (RSM) and the artificial neural network (ANN). The RSM was employed for a structured, statistical analysis, while an ANN provided a data-driven approach for capturing complex non-linear relationships. Various network architectures were established and evaluated with a fixed number of cycles. Results showed that the ANN exhibited superior accuracy in predicting both responses. In comparison to the QR model, the ANN exhibited the lowest average error rate in accurately predicting the response. This was further validated through experimental trials, demonstrating that the ANN consistently outperformed the RSM across different parameter settings. Additionally, the use of the MCFA contributed to sustainable manufacturing by minimizing the use of cutting fluids while maintaining machining quality.

Surface roughness is an important parameter for machined components. It affects their wear resistance and fatigue life. High-velocity pulsing jet minimal cutting fluid application (MCFA) is one of the emerging technique that helps to minimise the use of cutting fluid and reduce the adverse effects on human health and the environment. In this paper, an attempt was made to study the effects of cutting speed, feed rate and depth of cut on the surface roughness (Ra) in turning of AISI 52100 alloy steel of hardness 58 HRC with multilayer (TiN/TiCN/Al₂O₃) coated carbide tool insert under MCFA environment. The response surface methodology (RSM) based quadratic regression model for the surface roughness (Ra) was developed with 95% confidence level. The adequacy and validity of the developed model were checked based on P-value, F-value and R² -value. The analysis of variance (ANOVA) was used to determine the relative significance of cutting parameters and their interactions on the surface roughness (Ra). The result showed that the feed rate had significant effect in reducing the surface roughness followed by cutting speed, whereas depth of cut did not show any significant effect. Experimental data were optimised using the desirability function approach. A good agreement was observed between the predicted output and the experimental result.

This paper presents the design, kinematics, and development of a tendon-driven continuum robot for surgical applications. The continuum robot has a flexible and adaptable construction that imitates the movements of natural organisms. The robot’s unique structure comprises disk members, springs, and a continuum backbone member, enabling it to bend, contract, and deform in complex ways. The robot is operated by pulling tendons, giving it the agility and flexibility necessary to bend in confined spaces. This study discusses the main design considerations and challenges in creating a tendon-driven continuum robot, including the kinematics of the four-tendon mechanism. The developed tendon-driven continuum robot is categorized into two modules: the distal end and the proximal end. The distal end consists of the continuum robot structure, whereas the proximal module consists of the actuating unit that actuates the distal end. The experimental results demonstrate the continuum robot’s ability to be used in medical fields and pipe inspections because of the miniaturized design of the distal end, which allows it to enter confined spaces. This paper provides valuable insights into the design, kinematics, and appropriate materials to build a tendon-driven continuum robot; its bending and deformation capabilities can be used in many fields, especially surgical applications and confined space explorations.

Reliability allocation is the process of assigning reliability targets to sub-systems within a system to meet the overall reliability requirements. However, many traditional reliability allocation methods rely on assumptions that are often unrealistic, leading to misleading, unachievable, and costly outcomes. This paper provides a historical review of reliability allocation methods, focusing on the Weighing Factor Method (WFM), with a detailed analysis of its main findings, assumptions, and limitations. Additionally, the review covers methods for reliability optimization, redundancy optimization, and multi-state system optimization, highlighting their strengths and shortcomings. A case study is presented to demonstrate how the assumption of an exponential distribution impacts the reliability allocation process, showing the limitations it imposes on practical implementations. Furthermore, a bibliometric analysis is conducted to assess publication trends in the field of reliability allocation. Through examples, particularly in the context of electronic systems using commercial off-the-shelf (COTS) components, the challenges are discussed, and recommendations for alternative approaches to improve the reliability allocation process are provided.

This paper analyzes the surface quality characteristics, such as arithmetic average roughness (Ra), maximum peak-to-valley height (Rt), and average peak-to-valley height (Rz), in hard turning of AISI 52100 steel using a (TiN/TiCN/Al₂O₃) coated carbide insert under a minimal cutting fluid environment (MCFA). MCFA, a sustainable high-velocity pulsed jet technique, reduces harmful effects on human health and the environment while improving machining performance. Taguchi’s L27 orthogonal array was used to conduct the experiments. The findings showed that surface roughness increases with feed rate, identified as the most influential parameter, while the depth of cut shows a negligible effect. The main effects plot of signal-to-noise (S/N) ratios for the combined response of Ra, Rt, and Rz revealed the optimal cutting conditions: cutting speed of 140 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.3 mm. Feed rate ranked highest in influence, followed by cutting speed and depth of cut. The lower values of surface roughness parameters were observed in the ranges of Ra ≈ 0.248–0.309 µm, Rt ≈ 2.013–2.186 µm, and Rz ≈ 1.566 µm at a feed rate of 0.05–0.07 mm/rev. MCFA-assisted hard turning reduces surface roughness by 35–40% compared to dry hard turning and 10% to 24% when compared to the MQL technique. Moreover, this study emphasizes the significant environmental benefits of MCFA, as it incorporates minimal eco-friendly cutting fluids that minimize ecological impact while enhancing surface finish.