Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Natural fiber composites (NFCs) are an evolving area in polymer sciences. Fibers extracted from natural sources hold a wide set of advantages such as negligible cost, significant mechanical characteristics, low density, high strength-to-weight ratio, environmental friendliness, recyclability, etc. Luffa cylindrica, also termed luffa gourd or luffa sponge, is a natural fiber that has a solid potential to replace synthetic fibers in composite materials in diverse applications like vibration isolation, sound absorption, packaging, etc. Recently, many researches have involved luffa fibers as a reinforcement in the development of NFC, aiming to investigate their performance in selected matrices as well as the behavior of the end NFC. This paper presents a review on recent developments in luffa natural fiber composites. Physical, morphological, mechanical, thermal, electrical, and acoustic properties of luffa NFCs are investigated, categorized, and compared, taking into consideration selected matrices as well as the size, volume fraction, and treatments of fibers. Although luffa natural fiber composites have revealed promising properties, the addition of these natural fibers increases water absorption. Moreover, chemical treatments with different agents such as sodium hydroxide (NaOH) and benzoyl can remarkably enhance the surface area of luffa fibers, remove undesirable impurities, and reduce water uptake, thereby improving their overall characteristics. Hybridization of luffa NFC with other natural or synthetic fibers, e.g., glass, carbon, ceramic, flax, jute, etc., can enhance the properties of the end composite material. However, luffa fibers have exhibited a profuse compatibility with epoxy matrix.

Recently, researchers have proposed various maturity models (MMs) for assessing Industry 4.0 (I4.0) adoption; however, few have proposed a readiness framework (F/W) integrated with technology forecasting (TF) to evaluate the growth of I4.0 adoption and consequently provide a roadmap for the implementation of I4.0 for smart manufacturing enterprises. The aims of this study were (1) to review the research related to existing I4.0 MMs and F/Ws; (2) to propose a modular MM with four dimensions, five levels, 60 second-level dimensions, and 246 sub-dimensions, and a generic F/W with four layers and seven hierarchy levels; and (3) to conduct a survey-based case study of an automobile parts manufacturing enterprise by applying the MM and F/W to assess the I4.0 adoption level and TF model to anticipate the growth of I4.0. MM and F/W integrated with TF provides insight into the current situation and growth of the enterprise regarding I4.0 adoption, by identifying the gap areas, and provide a foundation for I4.0 integration. Case study findings show that the enterprise’s overall maturity score is 2.73 out of 5.00, and the forecasted year of full integration of I4.0 is between 2031 and 2034 depending upon the policy decisions.