To Örebro University

Rotary-percussive drilling is highly susceptible to severe torsional vibrations driven by nonlinear and uncertain bitrock interaction. In particular, the torque–speed characteristics of the bit can introduce an effective negative damping in the torsional degree of freedom, leading to self-excited stick–slip oscillations, increased tool wear, and reduced drilling performance. This paper proposes an interval type-2 fuzzy PID (IT2-FPID) control strategy to actively mitigate torsional vibration by compensating the negative damping effect. Starting from a torsional singledegree-of-freedom drilling model with velocity-dependent bit torque, the nonlinear and uncertain terms-including the apparent negative damping-are grouped into an unknown function. An interval type-2 Takagi–Sugeno fuzzy system is then designed to approximate this function online, while a PID structure shapes the desired effective stiffness and damping. The combined IT2FPID control law is derived such that the fuzzy term cancels the destabilizing nonlinearities and the derivative gain provides additional positive damping. A Lyapunov function that includes both state and parameter estimation errors is constructed, and a set of adaptation laws for the fuzzy parameters is proposed. The resulting stability analysis establishes uniform ultimate boundedness of the closed-loop system and shows that the effective torsional damping is increased in the operating region of interest. Numerical simulations on a representative torsional drilling model illustrate that the IT2-FPID controller substantially attenuates the amplitude of torsional oscillations, improves convergence to the desired operating point, and shifts the equivalent damping from negative to predominantly nonnegative values compared with the uncontrolled case. The results indicate that interval type-2 fuzzy PID control provides a robust and implementable framework for torsional vibration suppression in rotary-percussive drilling under strong nonlinearities and parameter uncertainty.

This paper presents a vibration–based, observer–driven fault detection scheme for rotary–percussive drilling that is both physics–grounded and robust to operating–regime variability. A compact axial–torsional plant with single–cutter bit–rock interaction captures loading/unloading asymmetry and frictional torque coupling. To compensate for salient nonlinearities and premise uncertainty (e.g., intermittent contact, rate effects), we embed the dynamics in an interval type–2 Takagi–Sugeno (IT2–T–S) fuzzy framework with explicitly defined IF–THEN rules and type reduction, yielding a convex blend of local linear models suitable for analysis and synthesis. An adaptive Luenberger observer is then designed to (i) reconstruct the nominal vibration response, (ii) generate a residual sensitive to faults yet tolerant to modelling errors and measurement noise, and (iii) deliver an online estimate of an unknown axial fault input. A Lyapunov function with vertex LMI conditions guarantees exponential convergence in the fault–free case and uniform ultimate boundedness under bounded faults; the fault–estimation update law is derived to ensure closed–loop stability. Simulations with percussion–style axial forcing demonstrate three key outcomes on a short time horizon: residuals remain within a noise–based threshold pre–fault and cross the band at the fault onset; the estimated fault rapidly converges to the true magnitude with negligible steady bias; and the state–error norm decays quickly pre–fault and exhibits a bounded transient post–fault. The results indicate that the proposed IT2–T–S adaptive observer provides an implementation–ready path to reliable, vibration–based fault detection for drilling systems. The paper concludes with recommendations to migrate to higher–order fuzzy consequents (polynomial/type–2) to further reduce approximation error and tighten residuals in strongly impacting regimes.

Speckle metrology is a powerful optical sensing tool for non-destructive testing (NDT) and advanced surface characterization, enabling ultra-precise measurements of surface deformations and displacements. These capabilities are critical for material analysis and surface assessment in sensing-driven applications. However, traditional correlation methods often struggle to balance resolution and robustness, particularly when simultaneously measuring both small- and large-scale deformations in noisy, high-frequency data environments. In this paper, we present an adaptive resolution approach for speckle displacement measurement that combines grid-based phase correlation with statistical refinement for enhanced accuracy and resolution.Unlike traditional phase correlation techniques that rely on global correlation, our method introduces a flexible grid-based framework with localized correlation and dynamic overlap adjustments. To improve measurement performance, we developed an optimization technique that uses the median absolute deviation of residuals between reference and deformed images, enabling the algorithm to automatically adapt grid sizes based on local deformation characteristics. This allows it to handle both small- and large-scale deformations simultaneously and effectively. The approach resulted in a relative error reduction of up to 14 % compared to the best of the results obtained using a manually fixed grid size.The proposed sensing methodology is validated through a series of numerical simulations and experimental studies, including controlled deformations with a micrometer translation stage and random speckle displacements on water-sprayed surfaces. Results demonstrate that our method can accurately detect both known and unknown deformations with high accuracy and precision, outperforming traditional techniques in terms of adaptability and robustness, particularly for surface deformation analysis.

In this paper we show how relational representations of design structure matrices (DSM), on the one hand, enables to describe domain dependencies and connections as relational composition, and, on the other hand, invites to using a variety of algebraic structures for the sets of qualifications attached with non-binary matrices. Particularly, we use fuzzy sets of higher types to model qualifications in many-valued DSMs where compositional techniques allow for extending the use of fuzzy sets of higher types also in the setting of multidomain matrices (MDM). We further show how clustered domains can be embedded as modelled within powersets of domains, thus providing a further justification for adopting the relational view of DSMs, particularly as the qualification space needs to support folding and unfolding across hierarchies in clustered domains. Our case study is drawn from scenarios involving maintenance of equipment in mineral mining.

The trend towards electrification presents new challenges in bearing design. One such consideration is the occurrence of electrical discharge contact pathways, which can lead to surface damage. The current study presents a novel comprehensive multiphysics model, incorporating bearing dynamics, mechanics of lubricated rolling element-to-races contacts and electrical contact model for both DC and AC voltages. The model also includes both electrical resistance and capacitance effects in the bearing contacts. Key bearing vibration frequencies such as cage frequency and the bearing base natural frequency along with the voltage supply frequency are observed as influential in the electric current discharge. The developed model enables the prediction of rhythmic fluting patterns commonly observed in the failed bearing applications subject to electrical discharge.

This paper explores the 3D printing of poly vinyl alcohol (PVA) using the fused deposition modeling (FDM) process by conducting statistical modeling and optimization. This study focuses on varying the infill percentage (10–50%) and patterns (Cubic, Gyroid, tri-hexagon and triangle, Grid) as input parameters for the response surface methodology (DOE) while measuring modulus, elongation at break, and weight as experimental responses. To determine the optimal parameters, a regression equation analysis was conducted to identify the most significant parameters. The results indicate that both input parameters significantly impact the output responses. The Design Expert software was utilized to create surface and residual plots, and the interaction between the two input parameters shows that increasing the infill percentage (IP) leads to printing heavier samples, while the patterns do not affect the weight of the parts due to close printing structures. On the contrary, the discrepancy between the predicted and actual responses for the optimal samples is below 15%. This level of error is deemed acceptable for the DOE experiments.

An off-axis digital holographic interferometry technique integrated with a Mach–Zehnder interferometer based setup is demonstrated for measuring the temperature and temperature profile of a transparent medium. This technique offers several advantages: it does not require precise optomechanical adjustments or accurate definition of the frequency carrier mask, making it simple and cost-effective. Additionally, high-quality optics are not necessary. The methodology relies on measuring the phase difference between two digitally reconstructed complex wave fields and utilizing the temperature coefficient of the refractive index. In this way, we presented an equation of the temperature as a function of phase changes and the temperature coefficient of refractive index. This approach simplifies the calculation process and avoids the burden of complicated mathematical inversions, such as the inverse Abel transformation. It also eliminates the need for additional work with the Lorentz–Lorentz equation and Gladstone–Dale relation and can be extend for 3D measurements.

The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stabilty analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.

The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential (PID) control with type-2 fuzzy logic (T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator (TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID (T1-F-PID) controller.