InDEx – Industrial Data Excellence

InDEx, the Industrial Data Excellence program, was created to investigate what industrial data can be collected, shared, and utilized for new intelligent services in high-performing, reliable and secure ways, and how to accomplish that in practice in the Finnish manufacturing industry.InDEx produced several insights into data in an industrial environment, collecting data, sharing data in the value chain and in the factory environment, and utilizing and manipulating data with artificial intelligence. Data has an important role in the future in an industrial context, but data sources and utilization mechanisms are more diverse than in cases related to consumer data. Experiences in the InDEx cases showed that there is great potential in data utili zation.Currently, successful business cases built on data sharing are either company-internal or utilize an existing value chain. The data market has not yet matured, and third-party offerings based on public and private data sources are rare. In this program, we tried out a framework that aimed to securely and in a controlled manner share data between organizations. We also worked to improve the contractual framework needed to support new business based on shared data, and we conducted a study of applicable business models. Based on this, we searched for new data-based opportunities within the project consortium. The vision of data as a tradeable good or of sharing with external partners is still to come true, but we believe that we have taken steps in the right direction.The program started in fall 2019 and ended in April 2022. The program faced restrictions caused by COVID-19, which had an effect on the intensity of the work during 2020 and 2021, and the program was extended by one year. Because of meeting restrictions, InDEx collaboration was realized through online meetings. We learned to work and collaborate using digital tools and environments. Despite the mentioned hindrances, and thanks to Business Finland’s flexibility, the extension time made it possible for most of the planned goals to be achieved.This report gives insights in the outcomes of the companies’ work within the InDEx program. DIMECC InDEx is the first finalized program by the members of the Finnish Advanced Manufacturing Network (FAMN, www.famn.fi).</p

Reverse engineering in IOT and CPS using a BLE RGB LED lamp as an example

The objectives of this thesis project are to study industrial revolutions, to foresee further turns of industrial revolution, to suggest necessary skills for that and apply these skills into practice. These objectives were achieved by researching the history of industrial revolutions, following the news of the modern history and inducting current trends to the logical conclusions and applying scientific techniques into practice. As a result was discovered an exponential growth of complexity in technologies both on industrial fields and in everyday tasks. The current technological trend is Cybernation. However, cyber-physical objects are often closed for third-party applications. It may decrease the demand on Cyber Physical Systems. By chance, this challenge can be eliminated by means of reverse engineering. Its methods were studied and applied into practice in this project. Consequently, a light-control application was made. Its main advantage to the original program in the code, which is open source and can be used for new solutions

Data-driven knife sharpness meter for peeling lines

The research focuses on analysing data from a peeling line to understand correlations leading to knife change events. The study starts from identifying knife change events based on downtimes and labelling data by associated knives and peeled meter steps. The primary purpose is to uncover how collected data represents the lifespan of lathe knives and to identify key factors influencing the decision-making process for knife changes. The methodology involves quantitative research, including data gathering, labelling, and group summarization as well as constructive research where trained models were investigated to unravel their attentions. The study successfully processed peeling line data, revealing that models trained on top features from individual data sources outperformed those trained on combined features. Additionally, the research identified inaccuracies in the raw data, emphasizing the necessity of optimizing and validating data collection processes for future investigations. Notably, the study shed light on key factors influencing knife sharpness in peeling lines, offering valuable in-sights for further exploration