Component Tolerances Research Articles

A fault tolerant CSA in QCA technology for IoT devices

According to recent research, with the ever-increasing use of Internet of Things (IoT) devices, there has arisen an ever-growing need for high-performance yet low-power circuits that can efficiently process information. Quantum-dot Cellular Automata (QCA) has emerged as a promising alternative to conventional complementary metal-oxide-semiconductor (CMOS) technology due to its great potential in digital design at nanoscale levels on account of very low power consumption and very high processing speed. However, QCA circuits are inherently prone to faults due to variations in manufacturing processes and due to the influence of environmental factors. These faults degrade the performance of a QCA circuit considerably. Hence, fault tolerance is one of the major factors of consideration while designing a QCA circuit, particularly when the application requires very reliable and continuous operation, say in an IoT system. As such, this work presents a fault tolerant Carry Skip Adder (CSA) for QCA-based circuits. The fault tolerance of basic arithmetic components of IoT nodes performing tasks corresponding to the signal processing, control, and data manipulations is enhanced in the proposed architecture. The area occupied by a fault-tolerant full-adder circuit is 0.06 μm² and a clock cycle is 0.75; its core will be used in the CSA design. It realizes fault-tolerant multiplexers (MUX) and a majority gate, which gives the same result when there is a missing or extra single-cell fault. The most astonishing characteristic of this transistor-based CSA is its 85% tolerance for different types of failures. The CSA with three layers contains 1542 quantum cells, 4.75 clock phases, and occupies an area of 4.59 μm². It is compact and efficient architecture; therefore, it is very suitable for IoT applications where the area constraint and power efficiency are the key issues. The proposed CSA will increase the robustness and reliability of QCA-based digital circuits by integrating fault tolerance into its design such that the circuitry based on QCA can keep their functionality on even in fault-prone environments.

Improving component dimensional accuracy in electron beam powder bed fusion by addressing nonlinear deformations with 3D compensation strategies

ABSTRACT Electron beam powder bed fusion (EB-PBF) is effective for producing complex geometrical components with minimal residual stress because of elevated powder bed temperatures; however, it faces challenges in achieving dimensional accuracy due to nonlinear shrinkage. We systematically investigated the thermal shrinkage behaviour of large-scale EB-PBF components and developed 3D nonlinear compensation strategies. A thermal-mechanical model was developed to simulate residual stress and deformation during printing and cooling, revealing that nonlinear shrinkage is linked to thermal history, stress distribution, and material properties. The proposed method improved dimensional accuracy, reducing maximum errors from 1.81 mm to 0.16 mm, meeting industrial tolerances for components sized 77 × 48 × 326 mm. Hereafter, we developed a comprehensive digital workflow encompassing topology optimisation and compensation, validated through a case study on a topology-optimised satellite component. This approach enhances manufacturing precision and significantly reduces trial-and-error iterations in product design, resulting in substantial time and cost savings.

Investigation on Effect of Nonlinear Parameters in torque accuracy of Permanent Magnet Synchronous Machines

EVs and PHEVs are mainly driven by inverter control topologies which will be driving permanent magnet synchronous machines. PMSM is widely used for high drive performance. However, due to parameter variations high torque accuracy is not achieved. The performance of the PMSM drives is widely affected which is caused mainly due to environmental factors and aging. This would include component tolerance, input signal tolerance, machine parameters, software algorithms, sensor accuracy and calibration. It is important to understand these factors and make the tuning throughout the lifecycle since it may show performance deviations over the time and may lead to field complaints. As a step-by-step procedure, the factors which affects the torque accuracy are identified as part of the literature review. This is given as input to a cause effect relationship model which could demonstrate the deviation points. Simulation is performed by varying the operating points at different regions in the e-drive system modeled in Matlab/Simulink. Simulation results are used with different operating points to find out the influential parameters contributing to the deviation. The outcomes derived from the simulations are analyzed and dominant factors are determined. It is observed that this behavior will vary according to the changes in speed and torque. Correlation coefficient is derived from the statistical analysis. The future scope will be applying the optimization techniques and verification of improvements.

Research on Cascaded On‐Board DC/DC Converter Based on Three‐Phase Interleaved LLC

ABSTRACTThe on‐board DC/DC converter proposed in this research consists of a Buck converter and a three‐phase interleaved LLC resonant converter. This paper analyzes the average current and output current ripple characteristics of the rear‐stage converter in addition to explaining the topology and working principles of the front‐stage Buck converter and the rear‐stage three‐phase interleaved LLC resonant converter. To establish a single‐phase fundamental equivalent circuit for the rear‐stage converter, apply the fundamental wave analysis method, and it is essential for comprehending the impact of relevant system parameters on voltage gain characteristics and resonant operating regions. Conducting research on the two‐stage on‐board DC/DC converter's control strategy involves establishing small‐signal circuit models for both front and rear stage converters, derivation of parameters for double closed‐loop control, and determination of the control strategy employing phase‐shifted current sharing for the rear‐stage converter in cases where significant tolerance exists in resonant components. The simulation for a two‐stage on‐board DC/DC converter results indicates that under various conditions of resonant component tolerance, the converter is capable of achieving balanced phase currents and demonstrates minimal output current ripple prior to capacitor filtering. A 1.5‐kW experimental prototype has been fabricated, and the experimental findings indicate that the output voltage remains stable at 13.8 V despite fluctuations in input voltage. The highest efficiency of the prototype is about 96.27%, and the utilization of phase‐shifting current balancing control results in a notable reduction in output current ripple. This provides more evidence that the design of a two‐stage on‐board DC/DC converter is correct.

Evaluating sustainability power plant efficiency: Unveiling the impact of power plant load ratio on holding steam ejector performance

Understanding the role of holding ejectors is essential in the broader context of environmental and sustainability research. Through rigorous analysis, optimization of ejector usage is applied to minimize resource consumption and promote cleaner, more sustainable production methods. However, the design and control strategies of the power plant, including the setpoints and tolerances of various components, can also contribute to the power plant load ratio (PPLR). This study explores the impact of PPLR in the power plant cycle on the holding ejectors performance under various condenser temperatures. For comprehensive analysis, the study employs a comprehensive framework encompassing various indicators such as condensing fluid behavior, energy consumption, exergy destruction, air suction in the condenser, and entrainment ratio. The research unveils key findings, including a notable increase in oblique shocks with rising PPLR and a quantifiable rise in the maximum liquid mass fraction in the condensed regime. Additionally, the analysis reveals numerical relationships, such as a 5 % increase in mass flow rate and a 4.9 % rise in energy consumption with a PPLR increase from 0 to +5 %. Negative impacts on performance, such as a 4.6 % reduction in air extraction flow rate from the condenser, are observed with PPLR changes from 0 to −5%.

Improved Implementation of Chua’s Circuit on an Active Inductor and Non-Autonomous System

Chua’s circuit is a well-established model for studying chaotic phenomena and is extensively implemented in fields like encrypted communication. However, a traditional Chua’s circuit has large volume, high component precision requirements and limited adjustable parameter range, which are not conducive to application. In order to solve these problems, we propose an improved implementation of Chua’s circuit on an active inductor and non-autonomous system. First, we adopt the strategy of using active inductors instead of traditional passive inductors, achieving the miniaturization of the circuit and improving the accuracy of inductance. In addition, we present the theory of substituting non-autonomous systems for classical autonomous systems to reduce the requirements for the accuracy of components and improve the robustness of the circuit. Lastly, we connect the extension resistor in parallel with Chua’s diode to optimize circuit structure, thereby increasing the range of the adjustable parameter. Based on the three improvements above, experiments have shown that the average maximum error tolerance of components of our improved design has been increased from 1.88% to 7.38% when generating a single vortex, and from 4.73% to 12.61% when generating a double vortex, compared with the traditional Chua’s circuit. The range of the adjustable parameter has been increased by 195.83% and 36.98%, respectively, when generating a single vortex and double vortex. In summary, our improved circuit is more practical than the traditional Chua’s circuit and has good application value.

Depth-resolved mechanical behaviour of shot peened 7050-T7451 aluminium surfaces using in-situ synchrotron X-ray diffraction

We have assessed the elasto-plastic (sub)surface behaviour of 7050-T7451 aluminium alloy treated by shot peening using S110 steel shots of 300 μm in diameter and two different intensities, namely Almen intensity (A) of 3.4 (low-intensity) and 10.9 (high-intensity). Shot peening (SP) is a cold work process applied in the aerospace industry to enhance the fatigue tolerance of structural components after machining. The local mechanical response within the SP-induced layer was depth profiled at room temperature by performing an X-ray micro-diffraction experiment in transmission geometry at a synchrotron source, during tensile loading of the specimen to rupture. The presence of a SP-induced layer in the specimen is evidenced by compressive longitudinal lattice strains (∼-540 με for low-intensity and ∼-4200 με for high-intensity at 0.1 mm from the surface for the 311 reflection), parallel with respect to the applied load, i.e. perpendicular to the direction of impact of the steel shots, and also by higher values of the Full-Width-at-Half-Maximum (FWHM) of the diffraction peaks than those measured in the bulk material, due to the local plastic deformation induced by shot peening. High-intensity shot peening produced a higher surface roughness (Sa ∼13 μm), and also two times thicker deformed surface layer (∼0.4 mm), than low-intensity shot peening (Sa ∼2 μm and ∼0.2 mm thick deformed layer). For both shot peening conditions, the local yield stress of the surface layer and bulk material were similar, however the severely affected layers exhibit a non-linear elastic behaviour when applying loads lower than the yield stress of the material. Beyond yielding, the presence of SP-induced layers is mainly evidenced by the relatively higher value of the FWHM near-surface compared to the bulk (∼20 % higher for low-intensity and ∼40 % for high-intensity), due to the initial plastic deformation accumulated during shot peening and additional plasticity during loading of the specimen.

Medium-Term Disability and Long-Term Functional Impairment Persistence in Survivors of Severe COVID-19 ARDS: Clinical and Physiological Insights

BackgroundAlthough the medium- and long-term sequelae of survivor of acute respiratory distress syndrome (ARDS) of any cause have been documented, little is known about the way in which COVID-19-induced ARDS affects functional disability and exercise components. Our aims were to examine the medium-term disability in severe COVID-19-associated ARDS survivors, delineate pathophysiological changes contributing to their exercise intolerance, and explore its utility in predicting long-term functional impairment persistence. MethodsWe studied 108 consecutive subjects with severe COVID-19 ARDS who remained alive 6 months after intensive care unit (ICU) discharge. Lung morphology was assessed with chest non-contrast CT scans and CT angiography. Functional evaluation included spirometry, plethysmography, muscle strength, and diffusion capacity, with assessment of gas exchange components through diffusing capacity of nitric oxide. Disability was assessed through an incremental exercise test, and measurements were repeated 12 and 24 months later in patients with functional impairments. ResultsAt 6 months after ICU discharge, a notable dissociation between morphological and clinical-functional sequelae was identified. Moderate-severe disability was present in 47% of patients and these subjects had greater limitation of ventilatory mechanics and gas exchange, as well as greater symptomatic perception during exercise and a probable associated cardiac limitation. Female sex, hypothyroidism, reduced membrane diffusion component, lower functional residual capacity, and high-attenuation lung volume were independently associated with the presence of moderate-severe functional disability, which in turn was related to higher frequency and greater intensity of dyspnea and worse quality of life. Out of the 71 patients with reduced lung volumes or diffusion capacity at 6 months post-ICU discharge, only 19 maintained a restrictive disorder associated with gas exchange impairment at 24 months post-discharge. In these patients, 6-month values for diffusion membrane component, maximal oxygen uptake, ventilatory equivalent for CO2, and dead space to tidal volume ratio were identified as independent risk factors for persistence of long-term functional sequelae. ConclusionsLess than half of survivors of COVID-19 ARDS have moderate-severe disability in the medium term, identifying several risk factors. In turn, diffusion membrane component and exercise tolerance at 6-month ICU discharge are independently associated with the persistence of long-term functional sequelae.

Retraction Note: A novel hybrid approach based on principal component analysis and tolerance rough similarity for face identification

Retraction Note: A novel hybrid approach based on principal component analysis and tolerance rough similarity for face identification

Novel Current Source Converter for Integrating Multiple Energy Storage Systems

The increasing penetration of renewable energy sources (RESs) in transmission and distribution systems presents several challenges for grid operators. In particular, the unpredictable behavior of RESs can disrupt the balance between energy production and load demand, potentially affecting the stability of the entire system. Grid-connected energy storage systems (ESSs) offer a possible solution to manage the uncertainty associated with RESs. In fact, ESSs exchange power with the grid through the adoption of suitable energy management strategies, which are typically implemented by power electronics-based grid interfaces. Unlike other current source converter (CSC) solutions described in the literature, which only interface with a single energy storage device, this paper introduces a novel topology for a three-phase delta-type current source converter (D-CSC), which is capable of integrating three independent ESSs using the same number of semiconductors as traditional CSC solutions. Thus, it considerably enhances the flexibility of a power conversion system (PCS) without increasing the number of converter components. In addition, an innovative energy management control strategy is also introduced. This strategy enables the D-CSC to compensate for energy imbalances arising between the three ESSs, which might be caused by several factors, such as different aging characteristics, converter component tolerances, operating conditions, and temperature drifts. Hence, the D-CSC-based interface is capable of proper grid operation even if the three ESSs have different characteristics, thus opening the possibility of employing this converter to integrate both first and second-life devices. First, the topology of the proposed D-CSC is introduced, followed by a detailed mathematical description of its control strategy. The proper grid operation of the D-CSC was tested under different scenarios, considering the grid integration of three independent superconducting magnetic energy storage systems in a marine vessel. The proposed D-CSC is compared to traditional CSC solutions, highlighting the superior performances of the novel converter topology in terms of efficiency, total harmonic distortion of the output currents, and overall cost reduction for the PCS.

An analog circuit fault diagnosis method using improved sparrow search algorithm and support vector machine.

In analog circuits, component tolerances and circuit nonlinearity pose obstacles to fault diagnosis. To solve this problem, a soft fault diagnosis method based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is used. In this study, ISSA is obtained by optimization using four strategies for SSA deficiency. Twenty-three benchmark functions are used for optimization experiments, and ISSA converges faster, more accurately, and with better robustness than other swarm intelligence algorithms. Finally, ISSA is used to optimize the SVM parameters and establish the ISSA-SVM fault diagnosis model. In the Sallen-key test circuit diagnosis experiments, the correct fault diagnosis rates of SSA-SVM and ISSA-SVM are 97.41% and 98.15%, respectively. The results show that the optimized ISSA-SVM model has a good analog circuit fault diagnosis with an increase in diagnostic accuracy.

Fault detection in analog electronic circuits using fuzzy inference systems and particle swarm optimization

Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study.

Component design procedure for LCC‐S wireless power transfer systems based on genetic algorithms and sensitivity analysis

AbstractThis paper introduces a novel approach for designing a Wireless Power Transfer (WPT) system with LCC‐S compensation. Since WPT systems operate under resonant conditions, even small deviations of the components from the nominal values can result in a significant reduction of the power transferred to the load, and in an increment of the circulating currents, reducing the system efficiency. The design techniques available today in the literature provide a unique combination of passive components capable of transferring a certain power to the load. This is a limitation, because, in practice, there are several combinations that allow reaching the desired output power, but they are usually neglected because they are extremely difficult to compute analytically. For this reason, in this paper, the authors present an innovative design procedure that enables, through a Genetic Algorithm, the identification of multiple feasible combinations of the LCC‐S components capable of achieving the desired output power. Moreover, the authors evaluate the effects of the component tolerances on the output power to determine which combinations are more robust to component variations. This task is performed by calculating the probability that a particular combination yields the desired output power, once the tolerances have been considered, following a Monte Carlo approach. This information is utilized to decide whether it is possible to reduce the component quality (worsening the tolerance) without affecting the performance. Finally, an optimal solution granting both low‐cost and robustness against component tolerances can be individuated. The proposed design procedure is applied to a case study and validated experimentally.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

Reverse total shoulder arthroplasty with proximal bone loss: a biomechanical comparison of partially vs. fully cemented humeral stems

BackgroundThe appropriate amount of cementation at the time of reverse total shoulder arthroplasty with significant proximal bone loss or resection is unknown. Extensive cementation of a humeral prosthesis makes eventual revision arthroplasty more challenging, increasing the risk of periprosthetic fracture. We analyzed the degree of subsidence and torque tolerance of humeral components undergoing standard cementation technique versus our reduced polymethyl methacrylate (PMMA) protocol. Reduced cementation may provide sufficient biomechanical stability to resist physiologically relevant loads, while still permitting a clinically attainable torque for de-bonding the prosthesis. MethodsA total of 12 cadaveric humeri (6 matched pairs) underwent resection of 5 cm of bone distal to the greater tuberosity. Each pair of humeri underwent standard humeral arthroplasty preparation followed by either cementation using a 1.5 cm PMMA sphere at a location 3 cm inferior to the porous coating or standard full stem cementation. A 6-degrees-of-freedom (DOF) robot was used to perform all testing. Each humeral sample underwent 200 cycles of abduction, adduction, and forward elevation while being subjected to a physiologic compression force. Next, the samples were fixed in place and subjected to an increasing torque until implant-cement separation or failure occurred. Paired t-tests were used to compare mean implant subsidence versus a predetermined 5 mm threshold, as well as removal torque in matched samples. ResultsFully and partially cemented implants subsided 0.49mm (95% CI, 0.23 to 0.76 mm) and 1.85mm (95% CI, 0.41 to 3.29 mm), respectively, which were significantly less than the predetermined 5mm threshold (p<0.001 and p<0.01, respectively). Removal torque between fully cemented stems was 45.22 Nm (95% CI, 21.86 to 68.57 Nm), versus 9.26 Nm (95% CI, 2.59 to 15.93 Nm) for partially cemented samples (p=0.021). Every fully cemented humerus fractured during implant removal versus only one in the reduced cementation group. The mean donor age in our study was 76 years (range, 65 to 80 years). Only one matched pair of humeri belonged to a female donor with comorbid osteoporosis. The fractured humerus in the partially cemented group belonged to that donor. ConclusionPartially and fully cemented humeral prostheses had subsidence that was significantly less than 5 mm. Partially cemented stems required less removal torque for de-bonding of the component from the cement mantle. In all cases, removal of fully cemented stems resulted in humeral fracture. Reduced cementation of humeral prostheses may provide both sufficient biomechanical stability and ease of future component removal.

Three-Dimensional Tolerance Analysis and Allocation for Valve Gap of Engine

The engine is a typical complex machine with many mechanisms and high precision. The tolerance of the valve gap is an accumulative result of component tolerances on the valve mechanism, which is very critical to engine performance. The current tolerance analysis is carried out using the traditional linear dimension chain, and it is difficult to consider a large number of geometric tolerances. The analysis result is not consistent with reality. Based on an innovative three-dimensional tolerance model, i.e., the unified Jacobian-Torsor model, tolerance analysis and allocation for valve gap were presented in this paper, where large geometric and partial parallel connections were taken into consideration. For the special conical structure in the valve mechanism, the expression and variation of the conical torsor were deduced. At last, a real engine was selected as an example of the analysis process. Sufficient comparison and discussion were made for the results, which demonstrated the effectiveness of the method.

Methodology of a hierarchical and automated failure analysis and its advantages

&lt;p&gt;Several industries, particularly the automotive sector, are increasingly incorporating more electronics into their products. As a result, these products are becoming more complex and difficult to analyze. This complexity poses a significant challenge for manufacturers in proving the functional safety of their products. Not only do random faults present risks, but component tolerances can also lead to unexpected safety hazards. Current methods are struggling to keep pace with these challenges. We have identified key issues with existing methods and introduce a new approach that leverages computer automation and a model-based framework to enhance the process. We explain how this new method not only improves upon existing practices but also introduces additional capabilities.&lt;/p&gt;&lt;p&gt;In this paper, we examine methods for proving the functional safety of electronic systems. We begin by identifying the challenges associated with current established methods. Next, we introduce our new approach, which relies heavily on computer assistance and offers novel techniques for conducting broader and more in-depth analyses of these systems. We then explain a new workflow that utilizes this approach. To illustrate its application, we provide a demonstrative example. Our conclusion summarizes our findings and results, and we share our thoughts on potential future developments.&lt;/p&gt;

Overview of mathematical concepts for tolerance analysis

This paper's goal is to give an overview to tolerance analysis. The cost and quality of a product can be significantly impacted by decisions about tolerating. Increased focus on tolerance design is required if high-precision assemblies are to be produced at cheaper costs. After a component's tolerance has been determined, tolerance analysis looks to propagate the manufacturing imprecisions (geometrical defects) and to validate the value of the functional requirements. The mathematical modelling is absolutely necessary for this validation. Models for tolerance analysis are discussed in a lot of literature. There are summaries of the current state of the art, the most recent advancements, and the upcoming trends in tolerancing research. From a mathematical perspective, this paper provides a taxonomy of mathematical concepts for tolerance analysis.

TOLERANCE ANALYSIS OF BOOST DC-DC CONVERTER

This paper provides a detailed and comprehensive tolerance analysis of a boost DC-DC converter, emphasizing the effects of component tolerances on the overall performance and reliability of the converter. Boost converters are extensively used in modern power electronics systems to increase voltage levels in applications such as renewable energy systems, electric vehicles, and industrial power supplies. These systems often demand high efficiency and precise regulation, making the accuracy and tolerance of key components—such as inductors, capacitors, resistors, and semiconductors—critical. This analysis explores the impact of component tolerances on several key performance metrics, including output voltage stability, power efficiency, ripple, and transient response. Furthermore, the paper discusses strategies to mitigate the negative impacts of component variations, providing insights into improving the robustness and reliability of boost converters for real-world applications.

A flexible soft error mitigation framework leveraging dynamic partial reconfiguration technology

Fault tolerance is crucial for mission-critical FPGA-based systems in radiation environments. Soft-core processors in these systems, performing both control and computational tasks, require efficient soft error mitigation techniques to enhance their fault tolerance. In this paper, we investigate the variation trends in the fault tolerance of various processor components as the software workload changes. Based on our investigation, we propose a reconfigurable soft error mitigation framework that dynamically switches the hardening strategies for the processor according to the running workloads. The innovative application of dynamic partial reconfiguration technique in FPGAs enables time-multiplexing of the same circuit region to accommodate various hardening schemes, consequently achieving high area efficiency. We apply the framework to an open-source dual-issue superscalar RISC-V processor on a Xilinx ZCU102 FPGA board. Compared to traditional spatial redundancy-based hardening techniques, the proposed framework achieves a 20.84 % reduction in LUT utilization and a 20.53 % reduction in flip-flop utilization. The effectiveness of our framework has been evaluated through fault injection campaigns. The results indicate that, on average, only 0.05 % of faults would lead to system failure after hardening. Therefore, our work offers a new perspective on dynamic soft error mitigation in reconfigurable systems.