A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Machine Learning for IoT Applications and Digital Twins

The Internet of Things (IoT) stands as one of the most transformative technologies of our era, significantly enhancing the living conditions and operational efficiencies across various domains [...].

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

BACKGROUND

The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.

OBJECTIVES

To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT.

DESIGN

Systematic review.

DATA SOURCES

Unrestricted search of indexed databases and reference lists of included studies.

DATA SELECTION

Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included.

DATA EXTRACTION

Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively.

DATA SYNTHESIS

Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB.

LIMITATIONS

Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment.

CONCLUSION

AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT.

REGISTRATION

PROSPERO CRD42022366864.

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design's performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.

Physics-informed shape optimization using coordinate projection

The rapid growth of artificial intelligence is revolutionizing classical engineering society, offering novel approaches to material and structural design and analysis. Among various scientific machine learning techniques, physics-informed neural network (PINN) has been one of the most researched subjects, for its ability to incorporate physics prior knowledge into model training. However, the intrinsic continuity requirement of PINN demands the adoption of domain decomposition when multiple materials with distinct properties exist. This greatly complicates the gradient computation of design features, restricting the application of PINN to structural shape optimization. To address this, we present a novel framework that employs neural network coordinate projection for shape optimization within PINN. This technique allows for direct mapping from a standard shape to its optimal counterpart, optimizing the design objective without the need for traditional transition functions or the definition of intermediate material properties. Our method demonstrates a high degree of adaptability, allowing the incorporation of diverse constraints and objectives directly as training penalties. The proposed approach is tested on magnetostatic problems for iron core shape optimization, a scenario typically plagued by the high permeability contrast between materials. Validation with finite-element analysis confirms the accuracy and efficiency of our approach. The results highlight the framework's capability as a viable tool for shape optimization in complex material design tasks.

Theoretical study of effect of the geometrical parameters on the dynamic properties of the elastic rings of an air journal bearing

The paper investigates theoretically the effect of the geometry of the elastic rings of an air journal bearing on the elastic rings dynamic coefficients. The physical finite element method (FEM) model used to obtain the dynamic coefficients of the rings is discussed. A theoretical model is implemented to predict the effect of the geometrical parameters on the dynamic coefficients of the elastic rings. The effect of the geometrical parameters on the dynamic coefficients at different frequencies is studied using FEM. The elastic geometry that result in desired dynamic coefficients is demonstrated. Since predicting the dynamic coefficients for all possible ring geometries using FEM would be computationally expensive. A neural network (NN) is trained to predict the dynamic coefficients for all possible ring geometries generated by the different ring geometrical parameters within a given input domain. The NN results are compared to the experimentally verified FEM results and the results are in good agreement.

Numerical and Experimental Mechanical Analysis of Additively Manufactured Ankle-Foot Orthoses

Growing age and different conditions often require the replacement of orthoses, and FDM-based 3D printing can produce them quickly with less investment. In today's market for orthotics, these characteristics are highly desired. Therefore, this study is fully focused on the optimization and strength analysis of FDM 3D-printed ankle-foot orthoses (AFO) fabricated using PLA and PLA reinforced with carbon fiber (PLA-C). An increase in ankle plantar-flexor force can be achieved by reinforcing thermoplastic AFOs with CFs. Specially designed mechanical strength tests were conducted at the UTM to generate force-displacement curves for stored elastic energy and fracture studies. The mechanical behavior of both AFOs was predicted with the help of an FEA. The model predictions were validated by comparing them with mechanical strength testing conducted under the same loading and boundary conditions as the FEA. In both the prediction and experimental analysis, the PLA-C-based AFOs were stiffer and could withstand greater loads than the PLA-based AFOs. An area of high stress in the simulation and a fracture point in experimentation were both found at the same location. Furthermore, these highly accurate models will allow the fabrication of AFOs to be improved without investing time and resources on trials.

Smart Active Vibration Control System of a Rotary Structure Using Piezoelectric Materials

A smart active vibration control (AVC) system containing piezoelectric (PZT) actuators, jointly with a linear quadratic regulator (LQR) controller, is proposed in this article to control transverse deflections of a wind turbine (WT) blade. In order to apply controlling rules to the WT blade, a state-of-the-art semi-analytical solution is developed to obtain WT blade lateral displacement under external loadings. The proposed method maps the WT blade to a Euler-Bernoulli beam under the same conditions to find the blade's vibration and dynamic responses by solving analytical vibration solutions of the Euler-Bernoulli beam. The governing equations of the beam with PZT patches are derived by integrating the PZT transducer vibration equations into the vibration equations of the Euler-Bernoulli beam structure. A finite element model of the WT blade with PZT patches is developed. Next, a unique transfer function matrix is derived by exciting the structures and achieving responses. The beam structure is projected to the blade using the transfer function matrix. The results obtained from the mapping method are compared with the counter of the blade's finite element model. A satisfying agreement is observed between the results. The results showed that the method's accuracy decreased as the sensors' distance from the base of the wind turbine increased. In the designing process of the LQR controller, various weighting factors are used to tune control actions of the AVC system. LQR optimal control gain is obtained by using the state-feedback control law. The PZT actuators are located at the same distance from each other an this effort to prevent neutralizing their actuating effects. The LQR shows significant performance by diminishing the weights on the control input in the cost function. The obtained results indicate that the proposed smart control system efficiently suppresses the vibration peaks along the WT blade and the maximum flap-wise displacement belonging to the tip of the structure is successfully controlled.

Machine Learning-Based Surrogate Model for Press Hardening Process of 22MnB5 Sheet Steel Simulation in Industry 4.0

The digitalization of manufacturing processes offers great potential in quality control, traceability, and the planning and setup of production. In this regard, process simulation is a well-known technology and a key step in the design of manufacturing processes. However, process simulations are computationally and time-expensive, typically beyond the manufacturing-cycle time, severely limiting their usefulness in real-time process control. Machine Learning-based surrogate models can overcome these drawbacks, and offer the possibility to achieve a soft real-time response, which can be potentially developed into full close-loop manufacturing systems, at a computational cost that can be realistically implemented in an industrial setting. This paper explores the novel concept of using a surrogate model to analyze the case of the press hardening of a steel sheet of 22MnB5. This hot sheet metal forming process involves a crucial heat treatment step, directly related to the final part quality. Given its common use in high-responsibility automobile parts, this process is an interesting candidate for digitalization in order to ensure production quality and traceability. A comparison of different data and model training strategies is presented. Finite element simulations for a transient heat transfer analysis are performed with ABAQUS software and they are used for the training data generation to effectively implement a ML-based surrogate model capable of predicting key process outputs for entire batch productions. The resulting final surrogate predicts the behavior and evolution of the most important temperature variables of the process in a wide range of scenarios, with a mean absolute error around 3 °C, but reducing the time four orders of magnitude with respect to the simulations. Moreover, the methodology presented is not only relevant for manufacturing purposes, but can be a technology enabler for advanced systems, such as digital twins and autonomous process control.