Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components - Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.

This article presents a dataset of face-milling experiments for smart tool condition monitoring (TCM) performed under varying cutting conditions in the High-Perfomance Machining laboratory of Mondragon Unibertsitatea (MU). The experiments collected raw internal signals from the machine. Cutting forces, vibration signals, and acoustic emission signals were collected with external sensors. Tool wear was measured before each experiment and annotated accordingly, providing tool wear progression throughout the dataset. The dataset was technically validated using Python scripts to ensure the quality and reproducibility of the dataset. The resulting MU-TCM face-milling dataset offers a reproducible research design of experiments and associated data to carry out and advance smart TCM of milling processes. The dataset supports applications such as training machine learning and deep learning for TCM, enables sensor fusion research with diverse signal combinations, and facilitates the development of TCM solutions using only internal CNC signals for industrial environments. By supporting these applications, the dataset is expected to help reduce the gap between research and industry in smart TCM applications.

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.