Limited non-invasive transhumeral prosthesis control exists due to the absence of signal sources on amputee residual muscles. This paper introduces a hybrid brain-machine interface (hBMI) that integrates surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) signals to overcome the limits of existing myoelectric upper-limb prosthesis. This hybridization aims to improve classification accuracy (CA) to escalate arm movements' control performance for individuals who have transhumeral amputation. To evaluate the effectiveness of this hBMI, fifteen healthy and three transhumeral amputee subjects for six arm motions were participating in the experiment. Myo armband was used to acquire sEMG signals corresponding to four arm motions: elbow extension, elbow flexion, wrist pronation, and wrist supination. Whereas fNIRS brain imaging modality was used to monitor cortical hemodynamics response from the prefrontal cortex region for two hand motions: hand open and hand close. The average accuracy of 94.6 % and 74% was achieved for elbow and wrist motions by sEMG for healthy and amputated subjects, respectively. Simultaneously, the fNIRS modality showed an average accuracy of 96.9% and 94.5% for hand motions of healthy and amputated subjects. This study demonstrates the feasibility of hybridizing sEMG and fNIRS signals to improve the CA for transhumeral amputees, improving the control performances of multifunctional upper-limb prostheses.INDEX TERMS Classification accuracy, fNIRS, hybrid brain-machine interface, sEMG, transhumeral prosthesis.

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.

Laser powder bed fusion (LPBF), also referred to as Selective Laser Melting (SLM), an additive manufacturing (AM) technology, holds significant potential for fabricating three-dimensional metallic components with complex structures. Due to the dynamic application of melting and cooling thermal cycles, maintaining an accurate surface quality and shape in LPBF is extremely challenging. Because the quality of the manufactured products is significantly dependent on the thermal behaviour and temperature distribution inside the melt pool, it is critical to investigate the melt pool's dynamic stability during the LPBF process. Consequently, a FEM model that has been experimentally validated can significantly assist in the precise characterisation of temperature distributions and melt pool dimensions. In this study, the macro-scale modelling approach has been adopted in which a transient model is constructed on a 3D moving Gaussian heat source with varying process variables such as scan velocity, laser power, laser beam radius, hatch spacing, number of layers, and scan angle for each layer in order to investigate their effect on melt pool shape in LPBF of SS316L powder. This research proposed a three-dimensional finite element model for predicting the temperature gradient and melt pool characteristics in the LPBF of SS316L. The method considers the laser penetration depth and its influence on the melt pool characteristics calculated in a multiple-layer (15) and multiple-track (6) FEM model with variable process parameters, i.e., laser power, scan speed, beam radius, and hatch spacing. Literature-based experimental data were utilised to calibrate the proposed heat source model, and the calibrated FEM model was then verified through experiments. The findings of the modelling demonstrated reasonable agreement with the experiments. The interlayer and intertrack effects were investigated. Each track's and layer`s temperature distribution and the melt pool's depth, width, and length were evaluated, and the measurements for each variable were analysed. The average melt pool length, width, and depth were found to have 1.88%, 1,49%, and 2.12% relative errors between the FEM model and experimentally measured dimensions for an optimal range of different process parameters.

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Functional metal parts with complicated geometry and internal features for the aerospace and automotive industries can be created using the laser powder bed fusion additive manufacturing (AM) technique. However, the lack of uniform quality of the produced parts in terms of strength limits its enormous potential for general adoption in industries. Most of the defects in selective laser melting (SLM) parts are associated with a nonuniform melt pool size. The melt pool area may fluctuate in spite of constant SLM processing parameters, like laser power, laser speed, hatching distance, and layer thickness. This is due to heat accumulation in the current track from previously scanned tracks in the current layer. The feedback control strategy is a promising tool for maintaining the melt pool dimensions. In this study, a dynamic model of the melt pool cross-sectional area is considered. The model is based on the energy balance of lumped melt pool parameters. Energy coming from previously scanned tracks is considered a source of disturbance for the current melt pool cross-section area in the control algorithm. To track the reference melt pool area and manage the disturbances and uncertainties, a linear active disturbance rejection control (LADRC) strategy is considered. The LADRC control technique is more successful in terms of rapid reference tracking and disturbance rejection when compared to the conventional PID controller. The simulation study shows that an LADRC control strategy presents a 65% faster time response than the PID, a 97% reduction in the steady state error, and a 98% reduction in overshoot. The integral time absolute error (ITAE) performance index shows 95% improvement for reference tracking of the melt pool area in SLM. In terms of reference tracking and robustness, LADRC outperforms the PID controller and ensures that the melt pool size remains constant.