Characterization of the operation variables in an industrial process using statistical methods (original) (raw)
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Procedia CIRP, 2012
Since machining is a prevalent process in product manufacture this study reviews the accuracy of a specific energy characterization model to predict the electrical energy consumed by a 3-axis milling machine tool during processing. The energy characterization model had an accuracy of 97.4% for the part manufactured under varied material removal rate conditions and highlighted the potential for energy reduction using higher cutting speeds. Interviews with cutting tool manufacturers and end users showed that there is a genuine potential for energy reduction during milling operations due to the extensive use of uncoated cutting tools in industry.
A Reduced Model for Energy Consumption Analysis in Milling
Procedia CIRP, 2014
General awareness on energy consumption is globally growing, becoming a significant performance parameter also in the manufacturing field. The current work outlines a reduced model for the analysis of the energy consumption of a machine tool during face milling operations. The model is characterized by a minimum set of significant parameters describing the product, the process and the machine. The influence of these parameters on energy consumption is represented by a feed forward neural network with 20 inputs, two hidden layers and one output. The input parameters are identified a priori by means of simplifications based on physical and technological considerations, while their relevance is evaluated by a sensitivity analysis using the neural network. The network has been trained and validated on 800 experiments consisting of runs of a continuous-time simulator that estimates the energy consumption of a machine tool during part program execution.
Optimization Issues of a Hammer Mill Working Process Using Statistical Modelling
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Our paper presents the hammer mill working process optimization problem destined for milling energetic biomass (MiscanthusGiganteus and Salix Viminalis). For the study, functional and constructive parameters of the hammer mill were taken into consideration in order to reduce the specific energy consumption. The energy consumption dependency on the mill rotor spinning frequency and on the sieve orifices in use, as well as on the material feeding flow, in correlation with the vegetal biomass milling degree was the focus of the analysis. For obtaining this the hammer mill was successively equipped with 4 different types of hammers that grind the energetic biomass, which had a certain humidity content and an initial degree of reduction ratio of the material. In order to start the optimization process of hammer mill working process, 12 parameters were defined. The objective functions which minimize hammer mill energy consumption and maximize the milled material percentage with a certain ...
Energy Efficiency Analyses of a Milling Process Based on Toolpath Strategies
2018
This paper presents an approach to analytically determine the most energy efficient toolpath strategy in mechanical machining. This was achieved by evaluating the electrical energy requirement of the NC codes generated for the zag, zigzag, and rectangular contour toolpath strategies. The analytical method was validated by performing pocket milling on AISI 1018 steel with the considered toolpaths using a 3-axis Takisawa Mac-V3 milling machine. The rectangular contour toolpath was the most efficient in terms of the electrical energy demand of the feed axes and cycle time. Pocket milling with the zigzag toolpath strategy resulted in higher electrical energy demand of the feed axes and cycle time by 2% due to acceleration and deceleration characteristics of the machine tool feed axes execution at corners of the toolpath strategy adopted. Also, the electrical energy demand of the feed axes and cycle time for the zag toolpath were higher by 14% and 8%, respectively, due to the number of t...
Advanced Powder Technology, 2020
In this study, considering different operational parameters for stirred media mill, change in specific energy was compared to the change in R x values, i.e. the cumulative weight of the material undersized to a specific sieve. R values, namely R 38 , R 75 , R 106 , were measured before and after grinding in stirred mill. The change in R x (DR x , %) values were calculated and they were used to evaluate the certain unit of effectual energy (Ecb). This abovementioned calculation is performed by proportioning the Specific Energy (SE) to DR x values. The effectual part of SE is considered to be the ratio of the energy needed only for size reduction in grinding and it should be related to the DR x. The relative Ecb ratios of different grinding conditions give the relative specific energy efficiency ratio (SEe). The relative specific energy efficiency ratio is inversely proportional to specific grinding parameters and ground product particle sizes. The relative specific energy efficiency can be considered as the relative amount of energy for various grinding conditions. The variation between relative energy amount and the previously specified particle size provides a realistic comparison of different grinding parameters. The abovementioned variation could be employed to understand the resistance particle size which is a new concept to describe the particle size at which the maximum effectual SE is directly used. In the context of this study, it was aimed to figure out the interrelation between specific energy efficiency and PSD variation along with the resistance particle size.
The International Journal of Advanced Manufacturing Technology, 2019
The complex structure and large number of energy-consuming components in a machine tool provide a constant challenge to the researchers to characterize and model the energy consumption during a machining process. Recently, Therblig-based energy model in conjunction with value stream mapping has been used to identify and reduce the energy waste in a turning process. However, this model does not depict the information of energy consumption and carbon emissions throughout the process. Hence, it is difficult to estimate how much energy consumption and carbon emissions are caused by each activity. This paper presents an improved micro analysis of the energy and carbon emissions for each activity of a machining process on a value stream map. A case study of milling process is provided to illustrate the proposed methodology. The case study shows the improvement in energy efficiency, time efficiency, and carbon emissions. The energy and carbon emissions of each activity provide better transparency of energy flow and carbon emissions information throughout the machining process. The proposed methodology can not only be used to reduce the peak load at the factory level but also help to develop potential energy and carbon emission reduction strategies during the process planning stage.
Methodology for Process-independent Energetic Assessment of Machine Tools
Procedia Manufacturing, 2017
Energy-labels draw users' attention to energy efficiency and thus they force the manufacturers to reduce the energy consumption of their products. This paper focuses on the large variety of metal-cutting machine tools, representing a challenge to find an evaluation basis for a unique energy-label. For this reason, the process-independent energy consumption has been compared to machine tool features and properties. The database contains measured values and has been extended by values from literature. Concerning a large number of various machining centers for milling operations, the statistical analysis shows the influence of the different machine tool features and properties on energy consumption. Finally, an evaluation basis is presented and the applicability on energy-labels is discussed.
Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use
Since machine tools are used extensively throughout their functional life and consequently consuming valuable natural resources and emitting harmful pollutants during this time, this study reviews strategies for characterizing and reducing the energy consumption of milling machine tools during their use. The power demanded by a micromachining center while cutting low carbon steel under varied material removal rates was measured to model the specific energy of the machine tool. Thereafter the power demanded was studied for cutting aluminum and polycarbonate work pieces for the purpose of comparing the difference in cutting power demand relative to that of steel.