Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

Šajgalík, Michal; Kušnerová, Milena; Harničárová, Marta; Valíček, Ján; Czán, Andrej; Czánová, Tatiana; Drbúl, Mário; Borzan, Marian; Kmec, Ján

doi:10.3390/app10051788

Cited by 24 publications

(15 citation statements)

References 36 publications

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“…An illustration and description of the individual parts of such a code can be seen in Figure 8. The TIN begins with the text "DOT" (1), which represent that this type of tire meets the safety standards of the Ministry of Transport [27]. Part (2) of the marking is already the identification code itself, where part (3) describes the code of the manufacturer and production plant.…”

Section: Verification Of the Equipment Suitabilitymentioning

confidence: 99%

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

et al. 2022

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The article discusses the possibility of object detector usage in field of automated visual inspection for objects with specific parameters, specifically various types of defects occurring on the surface of a car tire. Due to the insufficient amount of input data, as well as the need to speed up the development process, the Transfer Learning principle was applied in a designed system. In this approach, the already pre-trained convolutional neural network AlexNet was used, subsequently modified in its last three layers, and again trained on a smaller sample of our own data. The detector used in the designed camera inspection system with the above architecture allowed us to achieve the accuracy and versatility needed to detect elements (defects) whose shape, dimensions and location change with each occurrence. The design of a test facility with the application of a 12-megapixel monochrome camera over the rotational table is briefly described, whose task is to ensure optimal conditions during the scanning process. The evaluation of the proposed control system with the quantification of the recognition capabilities in the individual defects is described at the end of the study. The implementation and verification of such an approach together with the proposed methodology of the visual inspection process of car tires to obtain better classification results for six different defect classes can be considered as the main novel feature of the presented research. Subsequent testing of the designed system on a selected batch of sample images (containing all six types of possible defect) proved the functionality of the entire system while the highest values of successful defect detection certainty were achieved from 85.15% to 99.34%.

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Section: Verification Of the Equipment Suitabilitymentioning

confidence: 99%

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

et al. 2022

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“…The amplified charge signal is converted to a voltage signal which is then evaluated. The magnitude of the charge or electrical voltage is proportional to the magnitude of the applied force [24]. A Kistler 9255A stationary piezoelectric dynamometer was used to measure the cutting force components during turning with powered tools.…”

Section: Measurement Of Cutting Forcesmentioning

confidence: 99%

Section: Experimental Partmentioning

confidence: 99%

Experimental Study and Verification of New Monolithic Rotary Cutting Tool for an Active Driven Rotation Machining

et al. 2022

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Forced rotation turning appears to be an effective machining method due to higher tool life, time efficiency and acceptable quality. Several studies have been carried out to investigate the basic characteristics of forced rotation machining. So far, tools are used whose design included several components. However, such tools may generate vibrations, which are undesirable in the process. In engineering practice, most vibration problems are solved by reducing the cutting parameters (cutting speed and feed rate), which reduces machining productivity. For this reason, a new type of monolithic rotary tool has been designed that eliminates the design complexity and high assembly accuracy requirements of current rotary tools. Based on the performed experimental research, it is possible to define the influence of cutting parameters on the cutting force. Next, the equation of the cutting force and the resulting roughness of the machined surface was determined. In the introduction, the results of the analysis of machining parameters with a rotary tool were added. The presented solution fundamentally validates the new monolithic tool for forced rotation technology and defines its application for different machining materials.

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“…In those studies, a relationship between cutting force and machining parameters has been established. The cutting force model shows the relationship between the cutting force with the trochoid step and trochoidal milling when milling EN X38CrMoV5-1 material with UNIFR41010xR1 cutting tool [7]. Relationship between cutting force with spindle speed, feed rate, axial depth of cut, radial depth of cut, and percentage of silicon Engineering carbide in the workpiece material when milling LM6/SiCp with a hard alloy cutting tool has been established in document No.…”

Section: Introductionmentioning

confidence: 99%

Study on model for cutting force when milling SCM440 steel

Thien

Trung

2021

Eureka: PE

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This article presents empirical study results when milling SCM440 steel. The cutting insert to be used was a TiN coated cutting insert with tool tip radius of 0.5 mm. Experimental process was carried out with 18 experiments according to Box-Behnken matrix, in which cutting speed, feed rate and cutting depth were selected as the input parameters of each experiment. In addition, cutting force was selected as the output parameter. Analysis of experimental results has determined the influence of the input parameters as well as the interaction between them on the output parameters. From the experimental results, a regression model showing the relationship between cutting force and input parameters was built. Box-Cox and Johnson data transformations were applied to construct two other models of cutting force. These three regression models were used to predict cutting force and compare with experimental results. Using parameters including coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)) and percentage mean absolute error (% MAE) between the results predicted by the models and the experimental results are the criteria to compare the accuracy of the cutting force models. The results have determined that the two models using two data transformations have higher accuracy than model not using two data transformations. A comparison of the model using the Box-Cox transformation and the model using the Johnson transformation was made with a t-test. The results confirmed that these two models have equal accuracy. Finally, the development direction for the next study is mentioned in this article

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Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

Cited by 24 publications

References 36 publications

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Experimental Study and Verification of New Monolithic Rotary Cutting Tool for an Active Driven Rotation Machining

Study on model for cutting force when milling SCM440 steel

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