HAZARD ANALYSIS AND RISK ASSESSMENT FOR SPINNING YARN PRODUCTION PROCESS BY INTEGRATED FTA-FMEA APPROCH

The hazard analysis and management is vital in textile industry to avoid accidents and wasting resources caused by the failures in production systems. Risk analysis is also very significant to decrease possible hazards and to avoid possible damage in manpower & production systems. In this study, an approach based on Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) is proposed to analyse the ring spinning yarn production process in a textile industry. First, the possible hazards in the production line, yarn production system, in an integrated company operating in the textile sector are analysed by FTA method. Then, FMEA is applied to ring spinning yarn production process in a textile industry to rank all possible risks corresponding to hazards in descending order with respect to both occupational health and safety. It is very important to remove all possible hazards in textile industry to decrease the number of risks related to occupational health and safety. Therefore, in total of 57 hazard root causes are determined in the yarn production department. Subsequently, the faults related to the hazard root causes are examined by FTA and then risk corresponding to these hazards are prioritized by FMEA. The results obtained from the proposed FTA-FMEA approach show that decision makers and engineers can easily decrease the number of hazards and risks with respect to both occupational health and safety in practic

Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques

Agricultural waste materials are increasingly being used as partial replacements for cement in concrete. Several experimental studies are available to evaluate the mechanical properties of plastic waste reinforced concrete but there are limited evaluations on agricultural waste material. In this study, an attempt is made to investigate the compressive strength of Corn Cob Ash (CCA) concrete at different replacement levels by implementing an Artificial Neural Network (ANN). As the percentage of CCA increases, workability, density and compressive strength decreases, hence the developed ANN model consists of 3 input parameters (cement content, CCA content, and curing ages) in the input layer, 4 hidden neurons in the hidden layer and 3 output parameters (slump, density, and compressive strength) in the output layer. Training is done by adopting Levenberg-Marquardt back-propagation algorithm by considering 80% of experimental data with log-sigmoid activation function for both hidden and output layers. The developed model has a high correlation coefficient of 0.999 for both the training and testing data sets. It has low MSE and MAPE values of 2.2768x10-5 and 1.25 for training data respectively and 3.0463x10-5 and 1.37 for testing data respectively. Hence, it is concluded that the developed model predicts the output at an average rate of 98% accuracy. The predicted 2.5% replaced CCA concrete shows the best performance at all curing ages. Therefore, this percentage level is considered as an optimum replacement level which does not much affect the hardened properties of concrete

Bio-mediated Synthesis of Nanomaterials for Packaging Applications

Change in lifestyle of humans in this present generation with huge dependence on packaging materials has encouraged several studies on development of new variety of packaging materials. Emphasis on replacement of existing non-biodegradable packaging materials with biodegradable materials paved the way for the use of biopolymers. Lack of properties, such as thermal stability and mechanical strength in biopolymers led to the development of bio-polymer nano-composites by adding metal/metal oxide nanoparticles as fillers into the biopolymers. Metal/metal oxide nano-particles improve mechanical/tensile strength, thermal stability as well as antimicrobial properties of the binding and receiving polymer matrix. Bio-mediated synthesis of metal/metal oxide nanoparticles result to development of novel packaging materials at a low cost and without releasing hazardous wastes into the environments. Novel packaging materials with metal/metal oxide nanoparticles as additives are capable of increasing the shelf life of food stuffs, in certain cases they act as indicators of quality food inside the package. Summarily, this present chapter focuses on bio-mediated synthesis of various metal/metal oxide nano-particles and their applications in food packaging.Peer reviewe

Review of advanced techniques for manufacturing biocomposites: non-destructive evaluation and artificial intelligence assisted modeling

Natural fiber reinforced polymer composites (NFRPCs) are being widely used in aerospace, marine, automotive, and healthcare applications due to their sustainability, low cost and ecofriendly nature. The NFRPCs manufactured through conventional, and computer controlled intelligent manufacturing techniques may contain internal and external defects. Traditionally, the microstructure of NFRPCs at different stages of manufacturing was obtained using destructive techniques which have stringent sample size restrictions and may cause decrease in residual properties of composites due to destructive scanning. However, these complications can be overcome by using non-destructive evaluation (NDE) and artificial intelligence (AI) techniques. This review highlights the impact of NDE and AI on the improvement of emerging manufacturing systems. We have discussed the classification of biocomposites, their manufacturing techniques, recyclability and strategies to improve mechanical properties. Further, the use of different types of contact and non-contact NDE techniques in understanding the microstructural variations during manufacturing, machining and the parameters that effects the mechanical performance of NFRPCs are discussed. The use of NDE images in developing the geometrical and computational models of NFRPCs are presented. We have highlighted the importance of AI technology in enhancing the quality of NDE images, improving the microstructural information before post-processing the data, and minimizing the analysis time, and identifying the defects and damages in NFRPCs. In the end, we presented the application of NDE techniques and AI technology in efficient generation of digital material twins of NFRPCs, which will be useful to design next generation biocomposites