Sustainable disassembly line balancing model based on triple bottom line

End of life (EOL) phase of a product is receiving more attention due to increase in environmental concerns, and many studies have been conducted for value creation in EOL, focusing on concepts as remanufacturing, reuse and recycling in sustainable production manner. This study especially focuses on one of global problem, e-waste. To minimise the amount of wastes and maximise recovered materials from EOL, disassembly is one of the most important concept, associated with reuse, and balancing disassembly line in an optimal way is essential for organisations. In disassembly line balancing (DLB), not only precedence of tasks, but also risk criteria related to environment and human safety should be considered for sustainability. The aim of this study is to propose a model based on triple bottom line (TBL) dimensions, i.e. human safety, environmental safety and business criteria. To achieve sustainability in DLB, and for risk assessment in sustainable DLB, it had been decided to use a multi-criteria method, i.e. TODIM, acronym in Portuguese of ‘Tomada de Decisão Iterativa Multicritério’. The proposed model included 22 disassembly criteria categorised under TBL dimensions, which are derived from the literature. Implementation of the study was conducted for computer disassembly processes, and as a result of the study approximately 12% an improvement in cycle time was succeeded. In the long run, the integration of sustainability in disassembly operations may contribute to the competitive advantage of the company in terms of differentiation and corporate image by achieving business, environment and human targets simultaneously.     

Modeling and prediction of weld shear strength in friction stir spot welding using design of experiments and neural network

Friction Stir Spot Welding (FSSW) is a kind of the friction stir welding (FSW) process, creates a spot, lap‐weld without bulk melting work materials. The tensile shear strength of the FSSW welded joints mainly depends on the pin height, tool rotation and welding time. In the present study, two of the techniques, namely factorial design and neural network (NN) were used for modeling and predicting the tensile shear strength of EN AW 5005 aluminum alloy. Tensile shear strength was taken as a response variable measured after welding pin height, tool rotation and welding speed were taken as input parameters. Relationships between tensile shear strength and welding parameters have been investigated. The level of importance of the FSSW parameters on the tensile shear strength was determined by using the analysis of variance method (ANOVA). The mathematical relation between the tensile shear strength and FSSW welding parameters were established by regression analysis method. This mathematical model may be used in estimating the tensile shear strength of FSSW joints without performing any experiments. Finally, predicted values of tensile shear strength by techniques, NN and regression analysis, were compared with the experimental results and their nearness with the experimental values assessed. Results show that, NN is a good alternative to empirical modeling based on full factorial design.     

Risk assessment for sustainability in e-waste recycling in circular economy

Clean Technologies and Environmental Policy - E-waste is a hazardous concept for human health, environment and businesses, and therefore, risk assessment is essential to eliminate or reduce the...