The European Union is planning to introduce a new tool for evaluating smart solutions in buildings—the Smart Readiness Indicator (SRI). As 54 energy efficiency categories must be evaluated, the triage process can be long and time-intensive. Altogether, 228 data points (or inputs) about the smartness of the buildings are required to complete the evaluation. The present paper proposes an alternative calculation method based on genetic programming (GP) for the calculation of Domains and linear regression (LR) for the calculation of Impact Factors and the total SRI score of the building. This novel calculation requires 20% (Domain ventilation and dynamic building envelope) to 75% (Domain cooling) fewer inputs than the original methodology. The present study evaluated 223 case study buildings, and 7 genetic programming models and 8 linear regression models were generated based on the results. The generated results are precise; the relative deviation from the experimental data for Domain scores (modelled with GP) ranged from 0.9% to 2.9%. The R2 for the LR models was 0.75 for most models (with two exceptions, with one with a value of 0.57 and the other with a value of 0.98). The developed method is scalable and could be used for preliminary and portfolio-level screening at early-stage assessments.

Štore Steel Ltd. is one of the major flat spring steel producers in Europe, producing several hundred steel grades with different chemical compositions. (180 × 180) mm billets are cast using a continuous casting machine with a 9-m radius. The macrostructure of the billets influence the mechanical properties of rolled material. For impact testing, standard test pieces are obtained from the rolled material at a defined location and orientation. Accordingly, the reduction ratio is also essential. Based on 2243 V-notch test pieces obtained from rolled materials produced in several batches between January 2019 and May 2024, models for predicting the absorbed energy of the rolled material at temperatures ranging from –60 °C to 20 °C were developed using linear regression and genetic programming. The models incorporated parameters such as chemical composition (contents of C, Si, Mn, S, Cr, Mo, Ni, Al, and V), casting conditions (average casting temperature, mold water flow, average difference between input and output mold cooling water temperature, average cooling water flow and pressure in three zones of secondary cooling), as well as the width and thickness of the rolled bars and test temperature. The genetic programming model outperformed the linear regression model.

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.

Accurate and efficient evaluation of steel properties is crucial for modern manufacturing. This study presents a novel approach that combines spark imaging and deep learning to predict carbon content in steel. By capturing and analyzing sparks generated during grinding, the method offers a fast and cost-effective alternative to conventional testing. Using convolutional neural networks (CNNs), the proposed models demonstrate high reliability and adaptability across different steel types. Among the tested architectures, MobileNet-v2 achieved the best performance, balancing accuracy and computational efficiency. The findings highlight the potential of machine vision and artificial intelligence in non-destructive steel analysis, providing rapid and precise insights for industrial applications.

ŠTORE STEEL Ltd. is one of the three steel plants in Slovenia. Continuous cast 180 mm × 180 mm billets can undergo cooling to room temperature using a turnover cooling bed. They can also be cooled down under hoods or heat treated to reduce residual stresses. Additional operations of heat treatment from 36 h up to 72 h and cooling of the billets for 24 h, with limited capacities (with only two heat treatment furnaces and only six hoods), drastically influence productivity. Accordingly, the casting must be carefully planned (i.e., the main thing is casting in sequences), while the internal quality of the billets (i.e., the occurrence of inner defects) may be compromised. Also, the stock of billets can increase dramatically. As a result, it was necessary to consider the abandoning of cooling under hoods and heat treatment of billets. Based on the collected scrap data after ultrasonic examination of rolled bars, linear regression and genetic programming were used for prediction of the occurrence of inner defects. Based on modeling results, cooling under hoods and heat treatment of billets were abandoned at the casting of several steel grades. Accordingly, the casting sequences increased, and the stock of billets decreased drastically while the internal quality of the rolled bars remained the same.

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.

Multilayer metal materials are increasingly used by tool shops in the production of transfer tools for sheet-metal forming. In research, it is found that during the machining of these advanced materials, damage to the cutting edge often occurs due to jumps in the cutting forces. The size of the cutting forces when milling a multi-directionally layered metal material is influenced by both the direction of depositing the individual layer and the geometry of the cutting tool. The main goal of the research was to create a mechanistic model of cutting forces for the end-milling of unidirectional and multidirectional 20MnCr5/316L four-layered metal material. The results of the model were compared with experimental data, where a good agreement was found when milling unidirectional layered material and a slightly worse agreement when milling multidirectional material. The maximum observed deviation of the predicted cutting forces is 14.6 % for all the comparative tests.

At & Scaron;tore Steel Ltd., continuously cast billets (180 mm x 180 mm) are reheated and rolled after cooling to room temperature. Hot-rolled bars are controlled as they cool to room temperature in specially designed cooling chambers, minimizing residual stresses and the development of pre-existing surface and internal defects. The bar ends can be additionally covered with insulating material. The cooled, rolled bars undergo examination using automated control lines to detect surface and internal defects, which primarily originate from the casting process. Internal defects are identified using ultrasonic testing. Between January 2022 and June 2023, 1550.0 tons of 61SiCr7 rolled bars, with diameters ranging from 53 mm to 72 mm and lengths from 7010 mm to 7955 mm, were examined using ultrasonic testing. The scrap was 109.6 tons (7.07 %). After collecting data on chemical composition (C, Si, Mn, Cr, Mo, Ni content), the casting process (casting temperature, cooling water pressure and flow in the first, second, and third zones of secondary cooling, as well as the temperature difference between input and output mould cooling water), and rolled bar geometry (diameter, length), scrap modelling after ultrasonic testing was carried using genetic programming. The genetic programming model suggested reducing the length of the rolled bar. Due to length multiplication, it was possible to reduce the rolled bar length from the initial lengths of 7010-7955 mm to the current lengths of 4558-6720 mm in June 2023. Based on this adjustment, a new production of rolled bars was established. By August 2024, 1251.9 tons of 61SiCr7 rolled bars were produced with the mentioned length adjustments. These rolled bars were subsequently examined using ultrasonic testing. The scrap was reduced by nearly 14 times, amounting to only 8.1 tons (0.64 %).

In today's rapidly evolving business landscape, the strategic adoption of virtual manufacturing methods has emerged as a key driver for companies seeking to streamline operations and expedite product launches in a cost-effective manner. This progressive approach involves the creation of a synthetic and interconnected environment, empowered by advanced software tools and systems, including Virtual Reality and Simulation technologies, tailored to optimize industrial processes. Our methodology employs a unique combination of two simulation software tools: Factory I/O for process development and MATLAB for control program implementation. Furthermore, we explore the use of the Modbus TCP/IP communication protocol as the framework for seamless interaction between these software tools during simulation. This research presents practical insights into the transformative potential of virtual manufacturing, showcasing its real-world application in enhancing operational efficiency and agility within industrial settings.

When machining difficult-to-cut metal materials often used to make sheet metal forming tools, excessive cutting force jumps often break the cutting edge. Therefore, this research developed a system of three neural network models to accurately predict the maximal cutting forces on the cutting edge in helical end milling of layered metal material. The model considers the different machinability of individual layers of a multilayer metal material. Comparing the neural force system with a linear regression model and experimental data shows that the system accurately predicts the cutting force when milling layered metal materials for a combination of specific cutting parameters. The predicted values of the cutting forces agree well with the measured values. The maximum error of the predicted cutting forces is 5.85% for all performed comparative tests. The obtained model accuracy is 98.65%.

In January 2022 final electromagnetic stirrers were installed to improve billets' macrostructure and to reduce defects in STORE STEEL, Slovenia. From January to September 2022 the material from 1121 batches, out of 2284 batches produced, was examined using ultrasonic testing. Among these 1121 batches, 270 batches were detected with defects larger than 2.5 mm. Based on various metallurgical reports made from samples taken after an ultrasonic inspection, the root causes were found to be shrinkage porosity and segregations. Based on the gathered parameters from this period (chemical composition, casting parameters, cooling or heating parameters of billets), the occurrence of inner defects was predicted using logistic regression and genetic programming. The genetic programming proved to be better than logistic regression. Based on modeling results, optimization of the final electromagnetic stirrers position for 51CrV4 spring steel was conducted. Consequently, out of 41 examined batches, only 2 were detected with defects larger than 2.5 mm.

Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.

This work deals with the problem the measuring of form deviations of rotating parts with the use of coordinate measuring machines equipped with a scanning probehead. The authors investigated the influence of the measurement speed and the number of sampling points on the results of the roundness deviation assessment. The experiments were carried out on a Prismo Navigator coordinate measuring machine (Zeiss) equipped with a VAST GOLD XXL head for a scanning speed range of 10 to 60 mm/s and for three different values of sampling points on the roundness profile. The results were compared with reference values obtained from the Taylor Hobson Talyrond 365 instrument. The measurements results were compared both quantitatively (by means of roundness parameter compliance analysis) and qualitatively (by visual analysis of profiles obtained at different scanning speeds). The research showed that the scanning speed can significantly affect the result of the roundness deviation measurement with the use of CMMs.

The paper presents a model for predicting the roll wear in the hot rolling process. It includes all indicators from the entire continuous rolling line that best predict the roll wear in the hot rolling process. Data for model development were obtained from annual production on the first rolling stand of the continuous roll mill. The main goal of the research was to determine significant parameters that affect the wear of the roll in the process of hot rolling. It has been found that the amount of rolled material before the re-machining of the roll surface has the greatest impact on the life of the roll contour. Therefore, the amount of material rolled before re-machining of the roll was used to estimate the wear of the roll. An artificial neural network was used to predict this amount of rolled material and was validated using data from one-year production.

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.

During the continuous casting of steel billets, several geometrical, inner and surface defects can occur due to the thermomechanical behavior during solidification. One of them is rhombic distortion (i.e. rhomboidity), which can lead to the occurrence of off-corner cracks and twisting of cast billets during further plastic deformation (i.e. rolling). Based on data of 2088 cast batches (64 different hypoeutectoid steel grades), 109,514 billets, produced from January 2022 to September 2022 in Štore Steel Ltd. (Slovenia), chemical composition (content of C, Si, Mn, S, Cr, Mo, Ni and V), casting parameters (average casting temperature, average difference between input and output cooling water, melt level, average cooling water flow and pressure in the first and second zone of secondary cooling) the linear regression and genetic programming were used in order to predict rhomboidity of continuously cast billets. The rhomboidity, in our case defined as relative diagonal difference, was determined using in-house developed computer vision system for measuring of rhomboidity. Based on the modelling results 9 batches (419 billets) of 42CrMos4 were cast in September 2022 with a 10 % higher water pressure in the first zone of secondary cooling (from 2.41 bar to 2.67 bar). The rhomboidity of continuously cast billets improved by 18.18 % (from 1.43 % to 1.21).

Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. The main motive for this study was to study the influences of non-metallic inclusions on mechanical properties obtained by tensile testing. From January 2016 to December 2021, all available tensile strength data (472 cases–472 test pieces) of 17 low alloy steel grades, which were ordered and used by the final user in rolled condition, were gathered. Based on the geometry of rolled bars, selected chemical composition, and average size of worst fields non-metallic inclusions (sulfur, silicate, aluminium and globular oxides), determined based on ASTM E45, several models for tensile strength, yield strength, percentage elongation, and percentage reduction area were obtained using linear regression and genetic programming. Based on modeling results in the period from January 2022 to April 2022, five successively cast batches of 30MnVS6 were produced with a statistically significant reduction of content of silicon (t-test, p < 0.05). The content of silicate type of inclusions, yield, and tensile strength also changed statistically significantly (t-test, p < 0.05). The average yield and tensile strength increased from 458.5 MPa to 525.4 MPa and from 672.7 MPa to 754.0 MPa, respectively. It is necessary to emphasize that there were no statistically significant changes in other monitored parameters.

This article presents a universal simulator of instantaneous loads on the cutting edges of ball-end mills for the processing of layered metal materials.The ANFIS-mechanistic simulator of cutting edge loads is designed for all typical geometries of ball-end mills and for layered materials with different machinability of individual layers.Two ANFIS (Adaptive Neuro Fuzzy Inference System) models are included in the simulator, which, based on the instantaneous radial thickness of the chip and the geometry of the spherical part of the ball-end mill, predict the material coefficients for the material layers.Comparison of the predicted tool loads with experimental data shows that the simulator accurately predicts the direction and magnitude of the instantaneous loads of the ball-end mill cutting edges when milling 3-layer metal material with different machinability of individual layers.The largest observed deviation of simulated load magnitudes for all verification machining tests is 21.7 %.