1. Achieving maximum smart readiness indicator scores: a financial analysis with an in-depth feasibility study in non-ideal market conditionsMitja Beras, Krzysztof Stępień, Miha Kovačič, Uroš Župerl, 2025, original scientific article Abstract: For European competitiveness, energy efficiency must be increased. An important part of energy efficiency depends on an efficient building stock—the sector with the greatest potential for energy savings, as more than a third of all primary energy is consumed in buildings. A new instrument, the smart readiness indicator (SRI), is being prepared to accelerate the implementation of smart solutions in buildings and establish a market that would require and accelerate the implementation of such solutions. In this paper, we examine how the SRI score of a shopping center (with an already relatively advanced automation system) changes if we perform an energy optimization worth approximately 6.6 million EUR. As all the upgrades suggested by the SRI methodology cannot be implemented, this paper is the first of its kind to define the maximum feasible SRI score. The necessary measures are elaborated comprehensively, analyzed, and evaluated both technically and financially (IRR, ROI, and payback time). This type of approach is suitable for less developed EU markets without smart grids, DSM, and predictive functions.
Keywords: smart systems readiness indicator (SRI), methodology, smart systems, real estate energy renovations, energy efficiency, financial analysis, smartness
Published in DKUM: 02.07.2025; Views: 0; Downloads: 8
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2. A cloud-based system for the optical monitoring of tool conditions during milling through the detection of chip surface size and identification of cutting force trendsUroš Župerl, Krzysztof Stępień, Goran Munđar, Miha Kovačič, 2022, original scientific article Abstract: This article presents a cloud-based system for the on-line monitoring of tool conditions in
end milling. The novelty of this research is the developed system that connects the IoT (Internet of
Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical
system for the detection of cutting chip size. The optical system takes care of the acquisition and
transfer of signals regarding chip size to the IoT application, where they are used as an indicator
for the determination of tool conditions. In addition, the novelty of the presented approach is in
the artificial intelligence integrated into the platform, which monitors a tool’s condition through
identification of the current cutting force trend and protects the tool against excessive loading by
correcting process parameters. The practical significance of the research is that it is a new system for
fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of
a more cost-effective sensor, improves machining efficiency and allows remote process monitoring
on mobile devices. A machining test was performed to verify the feasibility of the monitoring
system. The results show that the developed system with an ANN (artificial neural network) for the
recognition of cutting force patterns successfully detects tool damage and stops the process within
35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the
identification of tool breakage, which verifies the effectiveness and practicality of the approach.
Keywords: machining, end milling, tool condition monitoring, chip size detection, cutting force trend identification, visual sensor monitoring, cloud manufacturing technologies
Published in DKUM: 26.03.2025; Views: 0; Downloads: 8
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3. Rapid assessment of steel machinability through spark analysis and data-mining techniquesGoran Munđar, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, Uroš Župerl, 2024, original scientific article Abstract: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting
forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive
and costly. This study presents a novel methodology to rapidly determine steel machinability using
spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including
various low-alloy and high-alloy steels, with most samples being calcium steels known for their
superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic
grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured
during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15
values, which were measured using the standard ISO 3685 test. Our results demonstrate that the
created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While
some samples exhibited high MAPE values, the method overall provided accurate machinability
predictions. Compared to the standard ISO test, which takes several hours to complete, our method is
significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective
and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Keywords: steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Published in DKUM: 12.09.2024; Views: 15; Downloads: 28
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