Engineering Problems Research Articles

A novel anti-loosening bolt looseness diagnosis of bolt connections using a vision-based technique

Bolt looseness detection is a common problem in engineering. Most vision-based detection techniques focus on diagnosing ordinary bolt looseness, i.e., the methods used for diagnosis are based only on the sidelines of nuts. These methods cannot be used for anti-loosening bolt looseness diagnosis because of the simultaneous rotation of screws and nuts. Therefore, a novel anti-loosening bolt looseness diagnosis method based on a vision-based technique is proposed in this paper. First, a regular hexagonal cap was installed on the screw, which can be used as a reference for the nut. Then, to automatically distinguish the hexagonal borders of the screw cap and nut, a new hexagonal border reconstruction algorithm is proposed. Furthermore, the relative rotation angles of the screw cap and nut hexagons can be determined using the sidelines of the reconstructed hexagonal borders of the screw cap and nut. Finally, a novel anti-loosening bolt looseness diagnosis method is established by using the relative rotation angle of the regular hexagonal borders of the screw cap and nut under initial status and loose status. A prototype flange node of the transmission tower was used for experimental verification. The results show that the proposed method can effectively detect the loosening angle of anti-loosening bolts.

‘5D PRINTING’ -THE NEW ERA IN DENTISTRY! -A NARRATIVE REVIEW

3D printing technology has a wide range of applications in various industries. It is widely used to produce complex 3D structures, but it has some limitations such as a limited amount of material, etc., which have been overcome with the introduction of 4D printing technology. In 4D printing, which involves time as a function and the combination of smart materials, this enables properties such as changing form and function.[1]Five-dimensional (5D) printing is a new branch of additive manufacturing (AM) with great potential to solve problems in engineering, medicine, dentistry and other related fields. It is the latest technological advancement used to produce complex and intricately shaped products, implants and devices with much better physical properties than those obtained by three-dimensional (3D) printing. The concept of 5D printing originated from William Yerazunis of the American University of Mitsubishi Electric Research Laboratories (MERL). In 5D printing, the printing plate also moves with the printing head during the printing process. in 3D printing techniques.[1]four-dimensional (4D) printing is the concept of using a smart material that can change the shape of a printed object over time as the temperature changes. [2] One of the advantages of 5D printing is the use of 25% less material than 3D printing. Five-dimensional printing is all about efficiently manufacturing this complex and curved structure with maximum strength. Using computer-aided design (CAD) data to produce super strong dental implants, orthodontic brackets, crowns, aligners, bridges and appliances. This CAD data is created using the dentist's 3D scanner / different design software.[1] 5D printing of five axes: [1], 1.X-axis ;2. Y-axis;3. Z axis; 4. Movable print head and 5. Movable printing base.5D printing is an advanced manufacturing technology that builds on the concept of 3D printing and adds customization options and features. While 3D printing requires the creation of three-dimensional objects layer by layer, 5D printing allows additional functions or features to be added to a printable object, often dynamically or responsively. its role in dentistry may be limited due to the newness of the technology.

Novel study on active reliable PID controller design based on probability density evolution method and interval-oriented sequential optimization strategy

Vibration active control is a significant problem in practical engineering, but the influence of hybrid uncertainties in practical engineering cannot be ignored. For the uncertainties with adequate data, probability theory can be used while for the uncertainties with insufficient data, probability theory is limited and interval quantification can be used. Taking the widely used proportional-integral-differential (PID) control as an example, a novel study on controller design based on probability density evolution method and interval-oriented sequential optimization strategy considering interval and random uncertainties is proposed. Firstly, under a certain sample point of interval uncertainties, probability density evolution equation is utilized to calculate the probability density function of uncertain response considering only random uncertainties. In this way the probabilistic reliability corresponding to the sample point can be obtained. Then the hybrid reliability is defined as the average probability within the interval uncertainty and the adaptive dichotomy method is utilized to choose proper sample points of interval uncertainties, in which the reliability curve and the interval uncertainty axis is regarded as the judgement of convergence. Next the hybrid reliability is introduced to the optimization as a constraint and the PID gains reliable design optimization is established to balance the energy-consuming and safety. Finally, the sequential optimization strategy is adopted to improve the efficiency of optimization. In every iteration step of the optimization, the constraint of deterministic optimal design is adjusted according to the static reliability at the moment before the passage or reaching the maximum value. Three numerical examples are presented to demonstrate the accuracy and practicability of the proposed framework.

Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

Enhancing Oil–Water Flow Prediction in Heterogeneous Porous Media Using Machine Learning

The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (ConvLSTM) network effectively captures spatiotemporal dynamics, its generalization in predicting complex engineering problems remains limited. Similarly, although the Fourier Neural Operator (FNO) demonstrates adeptness at learning operators for solving partial differential equations (PDEs), it struggles with three-dimensional, long-term prediction. In response to these limitations, we introduce an innovative hybrid model, the Convolutional Long Short-Term Memory-Fourier Neural Operator (CL-FNO), specifically designed for the long-term prediction of three-dimensional two-phase flows. This model integrates a 3D convolutional encoder–decoder structure to extract and generate hierarchical spatial features of the flow fields. It incorporates physical constraints to enhance the model’s forecasts with robustness through the infusion of prior knowledge. Additionally, a temporal function, constructed using gated memory-forgetting mechanisms, augments the model’s capacity to analyze time series data. The efficacy and practicality of the CL-FNO model are validated using a synthetic three-dimensional case study and application to an actual reservoir model.

A novel giant pacific octopus optimizer for real-world engineering problem

A novel giant pacific octopus optimizer for real-world engineering problem

Quantifying residual oil in a household oil tank

On a chilly evening, a practical challenge was posed to 2nd level students taking an extra-curricular math class: Imagine a household cylindrical oil tank lying on its side. How could we determine how much heating oil remains in the tank? In this paper, we ask students to engage in mathematical deduction to devise methods for approximating a solution to this intriguing engineering problem. The only tools that we have access to are measuring tape, and a long clean stick, so resourcefulness becomes key. We establish a mathematical relationship between the parameters that we have access to measure and the volume of oil that remains in the tank. Expanding the scope, we encapsulate this solution within a Jupyter Notebook (Python), allowing for the calculation of oil in any cylindrical tank.

A Generalized 3D-DDM for both Fracture-Type and Asymmetric Closed-Contour Problems: Modified Fully Analytical Approach and Applications

The displacement discontinuity method (DDM) has been widely used for large-scale engineering problems, e.g., hydraulic fracturing related to metal mining and hydrocarbon extraction. However, there is a lack of detailed explanation for the implementation of 3D-DDM in closed-contour boundary problem, and applications to address asymmetric closed-contour boundary problem remains limited. In this paper, we have theoretically derived and elaborated the numerical procedure, identified the caveats of DDM in solving fracture-type and closed-contour boundary problems. Subsequently, we modified the analytical integral and differential expressions for the basic solution on 3D-DDM based on triangular elements and provided a more general suite of expressions. Additionally, implementation details including element traversal criteria and precautions against rigid body movement are introduced to address the asymmetric closed-contour boundary problem. The accuracy of the generalized 3D-DDM were verified through simulations involving a penny-shaped fracture and a spherical cavity. Finally, the generalized 3D-DDM was employed to elucidate the stress redistribution and interference with respect to the tunnel excavation. This study demonstrates that: (1) the modified analytical integral and differential expressions combined with the element traversal criterion and precautions against rigid body movement provide a detailed explanation of how 3D-DDM can be applied to asymmetric closed-contour boundary problem; (2) during tunnel excavation, the arch flanks and floor would suffer from the biggest damage risk; and (3) the interaction effect between adjacent tunnels plays a role when the tunnel spacing is less than eight times the radius of the arch.

Slime Mould Algorithm Based on a Gaussian Mutation for Solving Constrained Optimization Problems

The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value.

Learning search algorithm: framework and comprehensive performance for solving optimization problems

In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics the social learning behavior observed in humans. The LSA algorithm optimizes the search process by integrating historical experience and real-time social information, enabling it to effectively navigate complex problem spaces. By doing so, it enhances its global development capability and provides efficient solutions to challenging optimization tasks. Additionally, the algorithm improves the collective learning capacity by incorporating teaching and active learning behaviors within the population, leading to improved local development capabilities. Furthermore, a dynamic adaptive control factor is utilized to regulate the algorithm’s global exploration and local development abilities. The proposed algorithm is rigorously evaluated using 40 benchmark test functions from IEEE CEC 2014 and CEC 2020, and compared against nine established evolutionary algorithms as well as 11 recently improved algorithms. The experimental results demonstrate the superiority of the LSA algorithm, as it achieves the top rank in the Friedman rank-sum test, highlighting its power and competitiveness. Moreover, the LSA algorithm is successfully applied to solve six real-world engineering problems and 15 UCI datasets of feature selection problems, showcasing its significant advantages and potential for practical applications in engineering problems and feature selection problems.

Harmony-driven technique for solving optimization and engineering problems

Harmony-driven technique for solving optimization and engineering problems

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.

Multi-objective generalized normal distribution optimization: a novel algorithm for multi-objective problems

AbstractThis study introduces the Multi-objective Generalized Normal Distribution Optimization (MOGNDO) algorithm, an advancement of the Generalized Normal Distribution Optimization (GNDO) algorithm, now adapted for multi-objective optimization tasks. The GNDO algorithm, previously known for its effectiveness in single-objective optimization, has been enhanced with two key features for multi-objective optimization. The first is the addition of an archival mechanism to store non-dominated Pareto optimal solutions, ensuring a detailed record of the best outcomes. The second enhancement is a new leader selection mechanism, designed to strategically identify and select the best solutions from the archive to guide the optimization process. This enhancement positions MOGNDO as a cutting-edge solution in multi-objective optimization, setting a new benchmark for evaluating its performance against leading algorithms in the field. The algorithm's effectiveness is rigorously tested across 35 varied case studies, encompassing both mathematical and engineering challenges, and benchmarked against prominent algorithms like MOPSO, MOGWO, MOHHO, MSSA, MOALO, MOMVO, and MOAOS. Utilizing metrics such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the study underscores MOGNDO's ability to produce Pareto fronts of high quality, marked by exceptional precision and diversity. The results affirm MOGNDO's superior performance and versatility, not only in theoretical tests but also in addressing complex real-world engineering problems, showcasing its high convergence and coverage capabilities. The source codes of the MOGNDO algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.

Photothermal superhydrophobic composite coatings based on n-tetradecane@CaCO3/TiN microcapsules for anti-/deicing

Ice accumulation on equipment and building surfaces is a scientific and modern engineering problem to be solved urgently. Compared to the energy-intensive and expensive active anti-/deicing approaches, passive energy-free technology based on advanced materials and surface engineering has received more attention. Herein, we successfully encapsulated n-tetradecane in calcium carbonate shell by in situ precipitation method, and the microcapsules reveal good energy-storage capability (99.9 %), latent heat (108.1 J g−1), and long-term durability. Combining the microcapsule with low-cost titanium nitride and low-surface-energy PDMS (polydimethylsiloxane) materials, the multifunctional superhydrophobic composite coating has been successfully prepared. The composite coating surface possesses strong absorption in UV–vis-NIR bands with high photothermal conversion and storage capability. The results indicate that the composite coating successfully prolongs the icing time compared to traditional photothermal superhydrophobic surfaces, and accelerates the melting of ice droplets under light illumination, which demonstrates great potential in both anti-icing and deicing applications.

Dynamic Analysis of an FGM Beam Undergoing Large Overall Motion in Temperature Field

The dynamic analysis of a functionally gradient material (FGM) beam undergoing large overall motion based on meshless methods is studied in the variable temperature field environment. Considering the high-order terms of nonlinear coupling deformation which were often ignored in previous studies, the high-order rigid–flexible coupled (HOC) dynamic model of a rotating FGM beam with a lumped mass in a temperature field is established by employing Lagrange’s equations of the second kind. Compared with the first-order approximate coupled (FOAC) dynamic model, the HOC dynamic model is more accurate and has a wider scope of application. The validity of the point interpolation (PIM) method and radial point interpolation method (RPIM) in this paper is verified by comparison with simulation results of the assumed mode method (AMM) and the finite element method (FEM). On this basis, the factors affecting the dynamic characteristics of the FGM beam such as the form of temperature field, the functionally gradient index, and the location of added lumped mass are discussed. In this paper, the meshless method with higher computational efficiency and the HOC dynamic model with more accurate results are adopted. This study will provide guidance and reference for dealing with problems in engineering, machinery, aerospace, and other fields in complex environment.

Optimal coordination of directional overcurrent protection relays using hybrid optimizer

In order to minimize or maximize an objective function, electrical engineering problems might benefit from optimization methodologies inspired by nature. This study employs a hybrid particle swarm optimizer (HPSO) to get rid of local optimum solutions and find the best global one for directional overcurrent protection relays (DOPR) protection. DOPR coordination is a highly constrained, mixed-integral nonlinear optimization problem. An adaptive defense method has been created to counteract the intentionally challenging design. Therefore, design variables such as time multiplier setting (TMS) and plug setting (PS) are utilized for each relay in the circuit. The goal function is to reduce the amount of time spent to trace the fault by all the relays. Reducing the time required for each relay to trace the fault is the aim of function. After analyzing and comparing two test studies of the IEEE benchmark using various optimization strategies, we found that the results for the IEEE 15-bus system has minimum time of 8.9 s, which is improved by 22.92% to 82.89%, and those for the IEEE 30-bus system minimum time of 22.45 s that is improved by 4.41% to 39.6%. In terms of overall DOPR operation and computational time needed to get the global optimum solution, the acquired results are demonstrably superior to those obtained using conventional and state-of-the-art methods.

Chaotic-Based Mountain Gazelle Optimizer for Solving Optimization Problems

The Mountain Gazelle Optimizer (MGO) algorithm has become one of the most prominent swarm-inspired meta-heuristic algorithms because of its outstanding rapid convergence and excellent accuracy. However, the MGO still faces premature convergence, making it challenging to leave the local optima if early-best solutions neglect the relevant search domain. Therefore, in this study, a newly developed Chaotic-based Mountain Gazelle Optimizer (CMGO) is proposed with numerous chaotic maps to overcome the above-mentioned flaws. Moreover, the ten distinct chaotic maps were simultaneously incorporated into MGO to determine the optimal values and enhance the exploitation of the most promising solutions. The performance of CMGO has been evaluated using CEC2005 and CEC2019 benchmark functions, along with four engineering problems. Statistical tests like the t-test and Wilcoxon rank-sum test provide further evidence that the proposed CMGO outperforms the existing eminent algorithms. Hence, the experimental outcomes demonstrate that the CMGO produces successful and auspicious results.

Experimental Investigation of Flow Drilling and Flow Tapping of Thin-Walled Square and Circular Hollow Sections

One of the most important problems of automotive engineering is joining metal sheets, thin-walled tubes or profiles simply, efficiently, and economically. After conventional drilling and tapping in thin-walled materials, the strength remains low due to the small number of teeth and the connection can be easily unfastened. For increasing the strength there are several solutions such as using welded nuts, tapped rivets and welding extra nuts. Since nut welding cannot be done on the inner surfaces, these solutions are inadequate for square and circular tubes. In this study, holes of various diameters were drilled on 1.5 mm thick AISI 304 stainless steel and EN AW-6060 square and circular profiles by flow drilling at various rotational speeds, and then flow tapping was applied to the holes. The same processes were repeated with conventional drilling method to compare bushing heights and clamping strengths of the parts as well as the hardness values and capillary crack formations around the holes. According to the results obtained, the strength in flow drilling and tapping increases by 50-55% compared to the classical drilling method. The reason for this is that as the hole diameter increases, the amount of material plastered and the number of threads required for screwing increases approximately 2.5-3 times. Capillary cracks, which are observed in holes drilled with the traditional method as the hole diameter increases, are not observed with this method and thus increasing the strength of the connection.

A Bayesian Spatio-Temporal Modeling Approach to the Inverse Heat Conduction Problem

Abstract A wide range of science and engineering problems involve inferring the system behavior and physical models from indirect observations. These are known as inverse problems. Unlike forward problems (predictive models), inverse problems are difficult to solve due to ill-posedness arising from one or more of non-existence, non-uniqueness (existence of many solutions), or instability (small perturbations in the measurements emerging from noise could lead to large changes in the solution). Therefore, direct inversion of the inverse problem in complex real-world systems is impractical. Several modeling approaches have been developed and adopted in the literature. However, the existing approaches do not consider spatio-temporal relationships. Meanwhile, various real systems and processes exist whose inverse problems can be comfortably modeled using spatio-temporal modeling techniques. We present a spatio-temporal inverse model considering interaction effects for modeling this type of inverse problem. We adopted a Bayesian inference framework for the subsequent parameter estimation problem and developed a Gibbs sampling algorithm to sample from the posterior distribution of the parameter of interest. Finally, we evaluate the proposed methodology through case studies on a 1D heat simulation and pool boiling experiment, using multi-sensor thermocouple measurements to reconstruct heat flux. Compared to the conventional methods, the proposed method performs better and can be adopted for solving inverse heat conduction problems. Moreover, incorporating the spatio-temporal interaction term notably improved the model's performance.

An improved manta ray foraging optimization algorithm

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm’s ability to escape from local optimal. To verify IMRFO’s performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms.