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This paper aims to deal with the study of interaction between multiple cracks in an aluminum alloy under static loading. Self-similar as well as non-self-similar crack growth has been observed which depends on the relative crack positions defined by crack offset distance and crack tip distance. On the basis of experimental observations, the conditions for crack coalescence, crack shielding, crack interaction, crack initiation, etc. are discussed with respect to crack position parameters. Considering crack tip distance, crack offset distance, crack size and crack inclination with loading axis as input parameter and crack initiation direction as output parameter, an artificial neural network (ANN) model is developed. The model results were then compared with the experimental results. It was observed that the model predicts the crack initiation direction under monotonic loading within a scatter band of ±0.5°.

The study is based on the experimental observations. Growth studies are made from the growth initiation from two cracks in a rectangular aluminium plate under static loading. The present study is focused on the influence of crack position defined by crack offset distance and crack tip distance on growth direction. In addition to this, ANN has been used to predict crack growth direction in multiple crack geometry under static loading. The predicted results have been compared with the experimental data.

The influence of the interaction between multiple cracks on crack extension angle greatly depends on the relative position of cracks defined by crack tip distance S, crack offset distance H and crack inclinations with respect to loading direction. The intensity of the crack interaction can be described according to degree of crack extension angle and relative crack position factors. It is also observed that the progress of the outer and inner crack tip direction is different which mainly depends on the relative crack position.

It is limited to static loading only. Under fatigue loading findings may differ.

It is important to investigate the growth behaviour under multiple cracks and also to know the effect of crack statistics on the growth behaviour to estimate the component life. The study also focused on the development of a high quality predictive method.

The results show trends that vary with crack geometry condition and the ANN and empirical solution provides a possible solution to assess crack initiation angle under multiple crack geometry.

The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.