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It is founded on DataBases (DB), Expert Systems (ES) created by heuristic logical programming methods using an artificial intelligence approach. There are also Hybrid Expert Systems (HES) which have "pure" Expert System parts and calculation modules (developed by traditional methods of software creating).

In this paper, methods of identifying the structural features of fibers and cracks in GDL images based on artificial intelligence are proposed. The block probabilistic Hough transform and the quadric voting based on the weighted K-means algorithm are programmed to realize the fiber feature extraction, and the crack feature extraction is realized by the regional connectivity algorithm and the geometric feature calculation based on the circumscribed graph of the crack region. ...The image processing technology based on artificial intelligence can capture the microstructural features of GDL images and extract feature parameters, which provides a reliable tool for GDL image analysis and has guiding significance for further research on GDL.

The detection and evaluation of damage in composite materials components is one of the main concerns for automotive engineers. It is acknowledged that defects appeared in the manufacturing stage or due to the impact and/or fatigue loads can develop along the vehicle riding. To avoid an unexpected failure of structural components, engineers ask for cheap methodologies assessing the health state of composite parts by means of continuous monitoring. Non Destructive Technique (NDT) for the damage assessment of composite structures are nowadays common and accurate, but an on-line monitoring requires properties as low cost, small size and low power that do not belong to common NDT. The presence of a damage in composite materials, either due to fatigue cycling or low-energy impact, leads to progressive degradation of elastic moduli and strengths.

Since the aircraft industry has a particularly high requirement for defect-free production of structural components, this paper presents a study on the online-detection of tool wear in automated drilling processes using a combination of external sensor technology and Artificial Intelligence methods. For this reason, a laboratory setup to conduct automatic drilling operations in fuselage material is introduced.

This development aims to improve the resistance to dirt and staining on artificial leather applied in seat cover with light colors. Comparative dirt and staining trials were conducted with soil, coffee and indigo jeans through abrasion testing by Crocking, followed by clean fabric removal. ...This improvement to protect surfaces and prevent it from staining on the artificial leather does not influence the mechanical properties of the product throughout service life and results in a better soft touch of materials.

This paper deals with the effects of artificial aging on two aluminum alloys, A356 and A319, which have been often used for engine cylinder heads. ...The aluminum alloys were artificially aged under several different conditions after T6 heat treatment. The alloys were tested for such mechanical properties as pure tension, cyclic loading resistance and thermo-mechanical fatigue failure.

In this article, CFRP and metal body vehicles were tested under artificial lightning. The electric discharging caused by the artificial lightning in the vehicles was investigated under different grounding conditions. ...A CFRP roof plate and a CFRP box mimicking vehicle cabin were also examined with artificial lightning to study generic cases, which did not depend on vehicle body shapes. The comparative study showed no significant difference between the CFRP and metal vehicles in lighting-strike performance.

The objective of this study is to investigate and develop an Artificial Neural Network approach based on vibration and AE signals for the detection, and characterization of wear, damage, and malfunction of an experimental gearbox. ...The objective of this study is to investigate and develop an Artificial Neural Network approach based on vibration and AE signals for the detection, and characterization of wear, damage, and malfunction of an experimental gearbox. Five artificial defects were introduced to the gearbox and these are; (1) tooth face wear, (2) full tooth breakage (missing tooth), (3) clearance or backlash, (5) axial gear looseness, and (5) single internal bearing race wear.

In the present paper, to predict the process relation between laser-assisted machining parameters and machinability characteristics, statistical models are formulated by employing surface response methodology along with artificial neural network. Machining parameters such as speed of cut; the rate of feed; along with the power of laser are taken as model input variables. ...Furthermore, artificial neural network method has been done to model the laser-assisted machining process. Then, both the models (RSM and ANN) are compared for accuracy regarding root mean square error (RMSE); model predicted error (MPE) along with the coefficient of determination (R2).

In this paper, we will discuss the development of an artificial neural network model and an adaptive neuro-fuzzy inference system that can be used to predict the future performance of Wire Electrical Discharge Machining (WEDM).

Abstract Previous artificial lightning strike direct effect research has examined a broad range of specimen design parameters. ...Thermal-electric finite element (FE) modelling is used and laboratory scale (<1 m long) and aircraft scale (>1 m long) models are generated in which laminated ply current dissipation is predicted, considering a fixed artificial lightning current waveform. The simulation results establish a positive correlation between the current exiting the specimen from a given ply and the amount of thermal damage in that ply.

Abstract Previous works have established strategies to model artificial test lightning plasma with specific waveform parameters and use the predicted plasma behavior to estimate test specimen damage.

This research paper presents the development of an Artificial Neural Network (ANN) model for Computer Numerical Control (CNC) drilling of AA6061 aluminium alloy with a coated textured tool.

Proposed approach allows to solve unformalized problems of material choice for friction units by methods of Artificial Intelligence. Algorithms and software for tribodesigning were developed. It could be used either as a separate design tool or as a part of some existing CAD/CAE system.

Statistical and computational intelligence techniques were employed for informatics based design of nano friction modifiers added bio-lubricant. ...The experimental data were used to develop data driven models using statistical techniques, artificial neural network and fuzzy inference systems. The simulation studies which were based on the model predictions were used to design the nano-lubricant with multi-walled carbon nanotubes as the friction modifiers.

A new artificial intelligence (model order reduction) / finite element coupled approach will be presented for the risk assessment of battery fire during a car crash event.

In the development and construction of such a system, it is advisable to use artificial intelligence methods in particular Adaptive Network-based Fuzzy Inference System networks (ANFIS), for solving optimization processing and forecasting MPP results.

This approach can easily be extended to crankcase lubricants, in which case major blend constituents are base oils, additive packages, and viscosity index improvers. Artificial intelligence (AI) tools allow accurate predictions of the basic physicochemical properties of such blends.

This work presents a novel approach for different parameter- based fatigue failure (rig testing failure) characterization using artificial intelligence (AI). The deep learning algorithm is trained on carefully collected physical testing data (historical data), which helps in predicting the new product development testing failure cycles based on basic design parameters available at the start of the program such as loading, component dimensions, distances, and inclination angle, etc.

This study looked at the effects of cold deformation, sample orientation, and artificial aging (paint cure simulation) after forming on the tensile properties of 6009 aluminum sheet of two thicknesses, 0.035 in (0.889 mm) and 0.071 in (1.80 mm). ...Test results indicated that the post-formed tensile properties were strongly influenced by deformation mode, sample orientation, sheet thickness, and artificial aging. Die compression produced tensile strengths of 67± 4 ksi (462± 28 MPa); by contrast cold rolled samples tested parallel to the rolling direction had tensile strengths of 31± 3 ksi (213± 21 MPa). ...Die compression produced tensile strengths of 67± 4 ksi (462± 28 MPa); by contrast cold rolled samples tested parallel to the rolling direction had tensile strengths of 31± 3 ksi (213± 21 MPa). Artificial aging increased strength in all deformation modes and exacerbated the deformation mode anisotropy of strength values.