Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

The use of bodyweight unloading force control on a treadmill with therapist manual assistance for gait training imposes constraints on natural walking. It influences the patient’s training effect for a full range of natural walks. This study presents a prototype and a safety controller for a mobile rehabilitation robot (MRR). The prototype integrates an autonomous mobile bodyweight support system (AMBSS) with a lower-limb exoskeleton system (LES) to simultaneously achieve natural over-ground gait training and motion relearning. Human-centered rehabilitation robots must guarantee the safety of patients in the presence of significant tracking errors. It is difficult for traditional stiff controllers to ensure safety and excellent tracking accuracy concurrently, because they cannot explicitly guarantee smooth, safe, and overdamped motions without overshoot. This paper integrated a linear extended state observer (LESO) into proxy-based sliding mode control (ILESO-PSMC) to overcome this problem. The LESO was used to observe the system’s unknown states and total disturbance simultaneously, ensuring that the “proxy” tracks the reference target accurately and avoids the unsafe control of the MRR. Based on the Lyapunov theorem to prove the closed-loop system stability, the proposed safety control strategy has three advantages: (1) it provides an accurate and safe control without worsening tracking performance during regular operation, (2) it guarantees safe recoveries and overdamped properties after abnormal events, and (3) it need not identify the system model and measure unknown system states as well as external disturbance, which is quite difficult for human–robot interaction (HRI) systems. The results demonstrate the feasibility of the proposed ILESO-PSMC for MRR. The experimental comparison also indicates better safety performance for the ILESO-PSMC than for the conventional proportional–integral–derivative (PID) control.

An innovative wearable upper-limb power-assist exoskeleton system (UPES) was designed for laborers to improve work efficiency and reduce the risk of musculoskeletal disorders. This novel wearable UPES consists of four joints, each comprising a single actuated pneumatic muscle actuator (PMA) and a torsion spring module driven via a steel cable. Unlike most single-joint applications, where dual-PMAs are driven by antagonism, this design aims to combine a torsion spring module with a single-PMA via a steel cable for a 1-degree of freedom (1-DOF) joint controlled by a proportional-pressure regulator. The proposed four driving degrees of freedom wearable UPES is suitable for power assistance in work and characterizes a simple structure, safety, and compliance with the motion of an upper limb. However, due to the hysteresis, time-varying characteristics of the PMA, and non-linear movement between joint flexion and extension, the model parameters are difficult to identify accurately, resulting in unmeasurable uncertainties and disturbances of the wearable UPES. To address this issue, we propose an improved proxy-based sliding mode controller integrated with a linear extended state observer (IPSMC-LESO) to achieve accurate power-assisted control for the upper limb and ensure safe interaction between the UPES and the wearer. This control method can slow the underdamped dynamic recovery motion to tend the target trajectory without overshoots from large tracking errors that result in actuator saturation, and without deteriorating the power assist effect during regular operation. The experimental results show that IPSMC-LESO can effectively control a 4-DOF wearable UPES, observe the unknown states and total disturbance online of the system, and adapt to the external environment and load changes to improve system control performance. The results prove that the joint torsion spring module combining the single-PMA can reduce the number of PMAs and proportional-pressure regulators by half and obtain a control response similar to that of the dual-PMA structure.

This paper proposed a novel design using a torsion spring mechanism with a single fluidic muscle actuator (FMA) to drive a joint with one degree-of-freedom (DOF) through a steel wire and a proportional pressure regulating valve (PRV). We developed a 4-DOF wearable upper-limb rehabilitation assistance exoskeleton system (WURAES) that is suitable for assisting in the rehabilitation of patients with upper-limb injuries. This system is safe, has a simple mechanism, and exhibits upper-limb motion compliance. The developed WURAES enables patients with upper-limb musculoskeletal injuries and neurological disorders to engage in rehabilitation exercises. Controlling the joint is difficult because of the time-varying hysteresis properties of the FMA and the nonlinear motion between standard extension and flexion. To solve this problem, a proxy-based output feedback sliding mode control (POFSC) was developed to provide appropriate rehabilitation assistance power for the upper-limb exoskeleton and to maintain smooth and safe contact between the WURAES and the patient. The POFSC enables the overdamped dynamic of the WURAES to recover motion to be aligned with the target trajectory without a significant error overshoot caused by actuator saturation. The experimental results indicate that the proposed POFSC can control the designed WURAES effectively. The POFSC can monitor the exoskeleton system’s total disturbance and unknown state online and adapt to the exterior environment to enhance the control capability of the designed system. The results indicate that a single FMA with a torsion spring module exhibits a control response similar to a dual FMA configuration.

Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent susceptibilities, underscoring the pressing need for the deployment of resilient preservation frameworks. This study delves into the transformative potential offered by distributed ledger technology (DLT), with a specific focus on IOTA, within the expansive landscape of the Internet of Things (IoT). Through a meticulous examination of the intricacies inherent to data transmission protocols, we present a novel paradigm aimed at fortifying data security. Our approach advocates for the strategic placement of IOTA nodes on lower-level devices, thereby streamlining the transmission pathway and curtailing vulnerabilities. This concerted effort ensures the seamless preservation of data confidentiality and integrity from inception to storage, bolstering trust in the convergence of IoT and DLT technologies. By embracing proactive measures, organizations can navigate the labyrinthine terrain of data management, effectively mitigate risks, and cultivate an environment conducive to innovation and progress.