As non-renewable conventional fossil fuel sources are depleting day by day, researchers are continually finding new ways of producing and utilizing alternative, renewable, and reliable fuels. Due to conventional technologies, the environment has been degraded seriously, which profoundly impacts life on earth. To reduce the emissions caused by running the compression ignition engines, waste cooking oil (WCO) biodiesel is one of the best alternative fuels locally available in all parts of the world. Different study results are reviewed with a clear focus on combustion, performance, and emission characteristics, and the impact on engine durability. Moreover, the environmental and economic impacts are also reviewed in this study. When determining the combustion characteristics of WCO biodiesel, the cylinder peak pressure value increases and the heat release rate and ignition delay period decreases. In performance characteristics, brake-specific fuel consumption increases while brake-specific energy consumption, brake power, and torque decrease. WCO biodiesel cuts down the emissions value by 85% due to decreased hydrocarbon, SO₂, CO, and smoke emissions in the exhaust that will effectively save the environment. However, CO₂ and NO_x generally increase when compared to diesel. The overall economic impact of production on the utilization of this resource is also elaborated. The results show that the use of WCO biodiesel is technically, economically, environmentally, and tribologically appropriate for any diesel engine.

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

Neurological injuries are one of the main reasons for the loss of functional mobility in humans. Compromised functionalities may include restrictions in the shoulder’s range of motion. Robotic devices, used for the physical therapy of shoulder, have different mechanism designs and control strategies. The aim of this paper is to provide a detailed review of different mechanisms used in shoulder rehabilitation robots as well as their control strategies. Shoulder robot mechanisms are grouped based on their actuating systems, namely, cable-driven shoulder rehabilitation mechanisms and power-driven mechanisms. The actuation systems considered during this study include electrical actuators, pneumatic actuators, series elastic actuators, and pneumatic muscle actuators. Experimental evaluation and validation of previous shoulder rehabilitation robots is also discussed citing their constraints and limitations. Safety aspects of existing shoulder robots have also been discussed. This paper also presents the advantages and disadvantages of previously proposed shoulder rehabilitation robots, their actuation, and control strategies and concludes by presenting future challenges and pathways.

Selective laser melting (SLM), a metal powder fusion additive manufacturing process, has the potential to manufacture complex components for aerospace and biomedical implants. Large-scale adaptation of these technologies is hampered due to the presence of defects such as porosity and part distortion. Nonuniform melt pool size is a major cause of these defects. The melt pool size changes due to heat from the previous powder bed tracks. In this work, the effect of heat sourced from neighbouring tracks was modelled and feedback control was designed. The objective of control is to regulate the melt pool cross-sectional area rejecting the effect of heat from neighbouring tracks within a layer of the powder bed. The SLM process’s thermal model was developed using the energy balance of lumped melt pool volume. The disturbing heat from neighbouring tracks was modelled as the initial temperature of the melt pool. Combining the thermal model with disturbance model resulted in a nonlinear model describing melt pool evolution. The PID, a classical feedback control approach, was used to minimize the effect of intertrack disturbance on the melt pool area. The controller was tuned for the desired melt pool area in a known environment. Simulation results revealed that the proposed controller regulated the desired melt pool area during the scan of multiple tracks of a powder layer within 16 milliseconds and within a length of 0.04 mm reducing laser power by 10% approximately in five tracks. This reduced the chance of pore formation. Hence, it enhances the quality of components manufactured using the SLM process, reducing defects.

Mobile Manipulators (MM) has attracted a lot of researchers for incorporation in the robotics field owning to their multitude of applications in real world. Welding automation has its wide applications in industry like automobile manufacturing and power generation industry involving spherical tanks. The objective of this study is to design workspace and devise a methodology to plan position trajectory of welding tool that produces smooth welding while the mobile platform turns simultaneously. The robot proposed in this paper has the manipulator mounted on a platform moving as a turntable to increase the workspace and enhances the mobility of the manipulator. The earlier produces linear segment of weld while the later produces parabolic segment. The kinematic equations for mobile platform and the mounted manipulator are described in detail. The workspace of the robot is visualized based on computations of transformation matrices and jacobians structured based on kinematic equation. Trajectories for each joint, computed using inverse kinematic equations, are also presented. For spherical trajectories the solution of system equations is combined with constraint values for each manipulator joint, thus allowing computation of desired joint position at any time interval. The efficacy of the proposed methodology for the trajectory planning is tested through a case study. The simulation results of motion transformation, workspace and trajectory show that linear segments of the trajectory combine with parabolic trajectory segments smoothly with zero acceleration within designed reachable workspace. The experimental results verify the efficacy of application of presented kinematic and inverse kinematic models for welding of spherical objects.