Purpose The relevance of small and medium enterprises (SMEs) in contributing to the economy and social development is increasingly felt in the current business environment. Focusing on sustainable development, SMEs have also implemented many acting strategies of large-scale enterprises such as lean and green practices. The purpose of this paper is to investigate the linkage between lean manufacturing practices (LMPs) in SMEs and their sustainability performances. Further, this study explores the relationship between the triple bottom line sustainability performances. Design/methodology/approach The study is based on a survey conducted and data collected from 252 manufacturing SMEs in India. The hypothesized relationships are then analyzed with structural equation modeling. Findings The outcome of the analysis shows that LMPs are positively associated with various sustainability performances categorized as economic, environmental, and social performances. Further, this study shows that environmental sustainability is correlated with economic and social sustainability performances. Research limitations/implications The study conducted was limited to a particular state in India. Moreover, the study uses the data from a cross-sectional survey from single respondents. Practical implications The findings of the study become an added advantage for the managers to convince their various stakeholders for implementing LMPs in SMEs. Originality/value The research findings provide theoretical and practical insights to derive the importance of LMPs in maximizing sustainability performances. It gives an enhanced perspective of the importance of LMPs on the sustainability performance of SMEs.

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The aim of this present investigation is to carry out a comparative study of the mechanical properties of AL6061/Albite composites containing albite(NaAlSi 3 O 8) particulates, which are naturally occurring plagioclase feldspar and AL6061/graphite particulate composites containing graphite particles. The reinforcing particulates in the MMC's vary from 0% to 4% by weight. The 'vortex method' of production was employed to fabricate the composites, in which the reinforcements were poured into the vortex created by stirring the molten metal by means of a mechanical agitator. The composites so produced were subjected to a series of tests.

Artificial Neural Network (ANN) approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD). The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed forward back propagation ANN model has been developed with different architectures for ensuring the definiteness of the values. The developed model along with its predicted data was in good agreement with the experimental data, inferring the valuable performance of the optimal model. From the work it was ascertained that, for castings produced by squeeze casting route, the ANN is an alternative method for predicting the mechanical properties and appropriate results can be estimated rather than measured, thereby reducing the testing time and cost. As a secondary objective, quantitative and statistical analysis was performed in order to evaluate the effect of process parameters on the mechanical properties of the castings.