Python is one of the most popular programming languages in research. The structure of the language and its object-oriented approach help programmers to write logical and clear code for small and large projects. Python libraries (packages) effectively simplify many important processes such as analysing and visualizing data, retrieving unstructured data from the web, image processing, building machine learning models, and textual information [1-4]. In this article, some of the most important and popular libraries and packages in Python are described. 1-Pandas Pandas is a fast, powerful, flexible, and easy-to-use open source data analysis and manipulation tool built on the Python programming language. Pandas is being used for data wrangling and analysis and provides simple ways for cleaning, manipulating, and transforming data. If you are dealing with a large amount of data, Pandas make it easier to work with them. Top features in Pandas can be categorized as:

The damage of composite structure caused by impact events is one of the most critical damages that have been caused several design problems. Understanding damage and failure of composite materials is critical in order to produce a reliable and cost-effective design. Generally, the character of impact response influences the extent of structural degradation and type of damage. Therefore, it is important to identify the properties and physical parameters that determine the nature of impact response. We need to detect, characterise, size and localise the impact damage. This study presents types of damages in composite materials, impact damage and its classification in composite materials.

One of the most pervasive types of structural problems in aircraft industries is fatigue cracking that can potentially occur without anticipation with catastrophic failures and unexpected downtime. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique, since it offers real time damage detection based on stress waves generated by cracking in the structure. Machine learning techniques have presented great success over the past few years with a large number of applications. This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using two approaches of machine learning, namely, Supervised and Unsupervised learning. A methodology for damage detection and characterization of composite is presented. The result shows that machine learning can predict damages in composite materials. All predictive models and their performance as well as AE parameters had a direct relationship with the applied stress values, suggesting that these correlations are reliable means of predicting fatigue life in a composite material.

a Department of Mechanical and Manufacturing engineering, universiti Putra Malaysia, serdang, Malaysia; b school of engineering and technology (Mechanical engineering), university college of technology sarawak (ucts), sarawak, Malaysia ABSTRACT This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using acoustic emission (AE) parameters. Its aims are to improve understanding of the particular characteristics of AE signals; and also to determine the relationship between AE signals and the failure of the material. Time and frequency domain trends were analysed at four different applied loads (60.97, 67.75, 74.52 and 81.30 MPa) representing 45-60% of the ultimate tensile strength of material. The relevant AE parameters were analysed both in the early stages of the test and as the material neared the fracture zone. The results showed a high degree of correlation between the root mean square and number of hits AE values and the number of cycles to failure, of 92.99 and 92.19%, respectively. This correlation, as well as AE basic parameters, suggests that AE can be a valuable tool to predict the fatigue life and detect the onset of damage in such composite materials.

This paper presents acoustic emission (AE) technique for detecting onset damage of composite materials damage and validate this technique using actual AE data from fatigue crack growth. AE piezoelectric transducer was attached to glass fibre reinforced polyester composite specimen during the fatigue cyclic test. For data collection, AE parameters, i.e., duration, amplitude, and energy near fracture zone were obtained and were correlated to fatigue life. AE signals were obtained at four different applied loads (60.97MPa, 67.75MPa, 74.52MPa, and 81.30 MPa) which were 45% to 60% of ultimate tensile strength (UTS) of material. The results show correlation between AE parameters and the number of cycles to failure. This correlations show that AE can be used to predict the fatigue life and can be tool for detecting damages in composite materials.

The optimization of crashworthiness in aerospace and automotive industries is one of the key research targets of the leading respective industries. In the past few decades; the use of composites in structural applications has been increasingly used in aerospace and advanced transportation industries because of their excellent mechanical properties and better strength-to-weight ratio than metal. In this paper composite materials constituent and their application in aerospace industry and their failure modes has been presented. The effects of the impact damage on the carbon fiber composites as the one of the most common materials in the aerospace industry has been investigated and evaluated by using non-destructive testing methods. This review considered the capabilities of the most common methods of NDT in composite materials. Damage modes interaction, types of velocity, and influence of various factors on impact behavior was investigated and must be considered to predict any failure in composite materials.

The damage of composite structure caused by impact events is one of the most critical damages that have been caused several design problems. Understanding damage and failure of composite materials is critical in order to produce a reliable and cost-effective design. Generally, the character of impact response influences the extent of structural degradation and type of damage. Therefore, it is important to identify the properties and physical parameters that determine the nature of impact response. We need to detect, characterise, size and localise the impact damage. This study presents types of damages in composite materials, impact damage and its classification in composite materials.

During their operation, modern aircraft engine components are subjected to increasingly demanding operating conditions, especially the high pressure turbine (HPT) blades. Such conditions cause these parts to undergo different types of time-dependent degradation, one of which is creep. A model using the finite element method (FEM) was developed, in order to be able to predict the creep behaviour of HPT blades. Flight data records (FDR) for a specific aircraft, provided by a commercial aviation company, were used to obtain thermal and mechanical data for three different flight cycles. In order to create the 3D model needed for the FEM analysis, a HPT blade scrap was scanned, and its chemical composition and material properties were obtained. The data that was gathered was fed into the FEM model and different simulations were run, first with a simplified 3D rectangular block shape, in order to better establish the model, and then with the real 3D mesh obtained from the blade scrap. The overall expected behaviour in terms of displacement was observed, in particular at the trailing edge of the blade. Therefore such a model can be useful in the goal of predicting turbine blade life, given a set of FDR data. Abstract A review provided about non-destructive testing (NDT) methods for the evaluation of composites. The review considers the capabilities of most common methods in composite NDT applications such as Visual Testing (VT or VI), Ultrasonic Testing (UT), Thermography, Radiographic Testing (RT), Electromagnetic Testing (ET), Acoustic Emission (AE), and Shearography Testing with respect to advantages and disadvantages of these methods. Then, methods categorized based on their intrinsic characteristics and their applications.

This study presents the evaluation of overall equipment effectiveness (OEE) at an enterprise crank shaft manufacturing in developing country of Iran. OEE is a measuring tool of equipment in the factories in order to reduce machinery problems and optimize the productivity of production equipment. This study analyzed the failure and repair data of the production line over the period of 1 year. The components availability (A), performance efficiency (PE), and quality rate (QR) of the OEE have been computed. The critical conditions have been identified by failure and repairing data to improve production line. The result shows that OEE is a reliable tool to eliminate the root causes of breakdown losses and speed losses. It can also be used in automobile manufacturing sector and other industrial manufacturers to improve the operation of the production line.

A document by Samira Gholizadeh. Click on the document to view its contents.