Energy Consumption Of Machine Research Articles

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning

Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek’s F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young’s modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties.

Peculiarities of designing a multi-saw block in the form of a crank saw

The features of the development of sawing equipment with circular translational motion of blades are considered. The proposed solution provides an increase in the productivity and service life of the equipment, reduction of energy consumption and metal consumption of the sawing machine. Keywords: sawing machine, dynamic balancing, circular translational motion. hornet10@yandex.ru

Optimization of Specific Energy Consumption in Turning of GFRP Composites using Particle Swarm Optimization

In this paper, an attempt is made to optimize the control parameters for the minimization of specific energy consumption in turning GFRP composites using particle swarm optimization (PSO).Optimization of specific energy consumption in machining is helpful to evaluate the process energy characteristics and also facilitates choosing the best control parameters for energy saving. The control parameters considered are cutting speed, feed, depth of cut and fiber orientation angle. Experiments are planned and executed according to Taguchi’s L25 orthogonal array in the design of experiments on an all geared lathe with PCD cutting tool insert. A quadratic predictive model was developed for specific energy consumption using RSM and the optimal combinations of control parameters were determined using PSO. The Predicted results from PSO show that there is an improvement in MRR by 46.44% and a reduction in SEC by 33.69%. From the confirmative experimental results, it is observed that PSO algorithm has a powerful global search ability to solve the optimization problem.

Machine Learning-Based Energy System Model for Tissue Paper Machines

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

Energy model-based benchmarking of the drying process in the stenter machine

Stenter machines are used widely in textile dyeing and printing houses for drying and heat setting of fabric. This is one of the most thermal energy-consuming equipment in the textile process. In this paper, the advanced energy model of the stenter machine is developed using primary mass and energy balance equations for ideal conditions as well as standard operating conditions, accounting for various energy losses to understand energy flow. To find out the scope for reduction in energy consumption, energy benchmarking is very important; compared with it, the appropriate energy consumption of a plant can be checked and changed. Furthermore, to eliminate problems such as life, process conditions, and quality of dyeing and printing houses considered during the study, unlike statistical energy benchmarking, in this paper, energy model-based benchmarking is proposed and applied for literature problems. Based on the data given in the literature for conveyor belt, stenter 1 and stenter 2, the specific energy consumption (SEC) for cotton is 4889, 3586, and 4304 kJ/kg of water evaporation, respectively, which is lower than the polywool fabric, having 5185 kJ/kg of water evaporation for another stenter machine. The energy efficiency index (EEI) of conveyor belt dryer, stenter 1, and stenter 2 are 1.56,1.18 and 1.34 for cotton, respectively, and for polywool with another stenter it is 1.92, indicating the polywool process is not energy efficient even polywool do not have bound moisture. Further, Energy flow analysis is carried out for the given stenter and fabric drying process, indicating the highest losses are in the exhaust process even after direct heat recovery by partial recirculation of exhaust gases. By analyzing the energy consumption of stenter machines through energy model based benchmarking and energy efficiency index presents the condition of process and machine from an energy perspective, and energy flow model gives the scope of the energy-saving potential. By using the developed model, the effect of change in atmospheric air temperature and humidity ratio due to season and geographic location on the performance of the stenter machine is investigated and found that there is a variation in performance up to 10% of the total energy required.

Stability of Heat and Energy Characteristics of Refrigerating Units as a Condition of Quality Increase of Agricultural Production Storage

This article provides information about the importance of stable thermal operating modes of refrigeration machines in the condition of changes in the thermal load on the compressor. We also consider the problem of the minimum energy consumption of refrigeration machines. These two parameters characterize the storage quality of agricultural products in refrigeration machines. It is shown that the temperature stability of the cooling chambers and the minimum energy consumption of the cooling process determine the refrigerator efficiency and, ultimately, the production cost. The article proposes continuous monitoring of the heat and energy indicators of refrigerators. These indicators are recorded and after the set period of operation, control and verification measurements of the control refrigeration machine’s heat and energy indicators are performed. The convergence or divergence of these indicators is used to evaluate the stability of the cooling chambers’ temperature regimes and the specific energy consumption. We also consider the influence of time on the performance of the compression refrigeration machine changes and methods of current technical condition diagnostics. If the deviations are insignificant, the program goes through the cycle to the beginning of monitoring and after a set period of time again automatically performs control measurements and evaluation of deviations. In the case of significant deviations, the decision-making subroutine is activated: either to operate the refrigerator further; perform its maintenance; suspend its operation; or replace it.
 Keywords: refrigeration units, cooling, heat and power characteristics, management controller, storage quality of agricultural production

Optimal production and maintenance scheduling for a degrading multi-failure modes single-machine production environment

Two important aspects of manufacturing systems are production scheduling and maintenance planning. These aspects are interdependent, but in most research work, this dependency is ignored. This paper proposed an integrated mathematical model for joint production scheduling and maintenance planning for a degrading multi-failure single-machine manufacturing system, in which the machine has discrete deterioration states. The machine is subject to different failure modes. The first failure mode is the machine’s full deterioration, which is detected at the end of a job’s processing, and the other failure mode is random breakdowns, which are detected at the time of failure. Two machine deterioration state-based thresholds are considered, and five different maintenance actions may carry out a replacement, preventive, and corrective perfect or imperfect maintenance. Since the machine’s states’ transitions follow an exponential distribution, a closed-form matrix-based mathematical model with probabilistic input parameters is presented. This paper aims to optimize the total system’s cost, including the maintenance cost, machine energy consumption cost as well as the makespan penalty for exceeding a pre-determined threshold. The proposed model determines the optimal jobs’ sequence as well as the machine’s deterioration state-based thresholds. Due to the complexity of the developed model, a genetic algorithm (GA), simulated annealing (SA) algorithm, and a teaching–learning-based optimization (TLBO) algorithm have been used to solve the presented model. The algorithms are validated using a full enumeration technique, and the model is validated by applying different maintenance strategies. Our results demonstrate the superiority of the GA compared to the other algorithms.

Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear

The natural energy crisis and the increasingly serious environmental problems have imposed all industries to reduce energy consumption. During milling process, selecting a correct cutting parameters can not only greatly improve production quality and processing efficiency, but also can reduce energy consumption, in addition, tool wear also has a great impact on them. Therefore, a milling power consumption model of CNC machine tools is established based on modern machining theory is established in this article, unlike traditional energy consumption models, our model takes full account of cutting conditions and tool wear. The surface roughness of parts is one of the important indicators to measure the machining quality of machine tools. Therefore, taking milling process as research object, a multi-objective cutting parameters optimization model that takes the machining surface roughness, material removal rate (MRR) and machining energy consumption as the optimization goals was established. Furthermore, an intelligent optimization algorithm was proposed based on improved Teaching–Learning-Based Optimization (TLBO) to solve the model under various limited milling conditions. Finally, comparing experimental results of optimized parameter and empirical parameters, it shows that goals of reducing energy consumption, improving productivity and machining quality can be achieved by optimizing cutting parameters.

Data-driven based optimization for high-speed dry cutting gear hobbing processing parameters

The formulation of gear hobbing processing parameters relies on extensive professional knowledge and artificial experience, and inappropriate processing parameters will lead to low machining precision and high manufacturing costs. In order to obtain suitable processing parameters in high speed dry hobbing gear process, Gradient boosting regression (GBR) and Generalized Regression Neural Network (GRNN) are adopted to establish a process parameter optimization model targeting gear machining accuracy and machining energy consumption. And then a method based on Differential Evolution (DE) algorithm, parameter adaptive multi-object differential evolution (AMDE), for hobbing processing parameter optimization is proposed. Machining experiment verification shows that the established gear hobbing accuracy prediction model and energy consumption estimation model have high prediction accuracy, and AMDE has strong optimization capabilities and robustness.

The prospects of using of flywheel energy accumulators in the lifting and transport industry

The increase in the energy consumption of lifting and transport machines in transient operating mode requires the creation of mathematical models and, on their basis, the development of recommendations for increasing economy. Important reasons for reducing the economy of lifting machines are changes in speed and load, deviations from the optimal value and causes an increase in energy dissipation. Another reason for the increase in lost energy is the process of machines with a loss of kinetic energy during their shutting-off. Increasing the economic showings of machines is possible by reducing its nominal power, which leads to a deterioration in dynamic qualities. A promising method of increasing the economy of machines is using of braking energy recovery, its accumulation and using during further acceleration. In this case, the increase of efficiency occurs without reducing the dynamic qualities and showings of the machine.

An experimental investigation and optimization of energy consumption and surface defects in wire cut electric discharge machining

Optimizing the energy consumption and minimization of surface defects in wire cut electric discharge machining (WEDM) at the experimental design effectively saves energy and improves quality of machining. The present study is aimed to reduce energy consumption and improve machine performance related to kerf width, metal removal rate and surface quality in WEDM of Al-Si metal matrix composite. Current, pulse on time, pulse off time, voltage and wire tension are considered as controllable parameters and optimized using graph theory and utility concept (GTUC) and teaching learning based optimization (TLBO) algorithm considering criteria for the performance characteristics. It is observed that 26% of the additional energy is consumed in GTUC method while 40% less energy is consumed in TLBO method; the kerf width is found to be 8.4% more in GTUC and 2.8% less in TLBO respectively; the MRR is found to be 40.2% and 43.2% less in GTUC and TLBO methods respectively; 72.8% of the excess surface roughness is found in GTUC method while 1.2% less surface roughness is found in TLBO method. Effect of process parameters on the performance characteristics is also analyzed and the current is found to be dominant parameter. At TLBO optimized working condition, surface defects around kerf are found minimum.

Hybrid virtual metering points – a low-cost, near real-time energy and resource flow monitoring approach for production machines without PLC data connection

Transparent energy flows within a factory are the prerequisite for energetic improvements of the involved production machines. With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. Virtual energy metering points predict the power consumption of a regarded entity and can therefore enable a cost-effective increase in energy transparency on machine level. However, many machines, especially in small and medium-sized enterprises (SMEs), have no external data connection, which prevents the use of data-based energy prediction models. In this paper, a near real-time deployable approach to predict the current energy consumption of production machines without a programmable logic controller (PLC) data connection is presented. By using a Raspberry Pi as low-cost edge analytics device, its integrated camera films the optical signals from light-emitting diodes (LEDs) of different PLC modules, which display the switching state signals of various machine sub-units. In a next step, the filmed PLC information is translated into state signals, which are correlated with temporarily measured electric energy data of the production machine as well as its principal sub-units. After an automated model training and hyperparameter optimization process, the empirical black box model is deployed in a near real-time environment on the Raspberry Pi. Thus, a hybrid virtual energy and resource flow metering point of the production machine as well as its sub-units is generated. In addition, challenges like model training for predicting different production processes as well as the necessary data set size for VMP model generation are addressed. The approach is tested and validated for a metal cutting machine tool and a cleaning machine of the ETA Research Factory at the Technical University of Darmstadt.

Modelling of Variable Energy Consumption for CNC Machine Tools

Machining is a prevalent process in manufacturing industries and consumes a considerable amount of energy which causes adverse environmental effects. Establishing an accurate energy evaluation model is essential for a sustainable machining process. An extensive amount of research work is conducted to model energy consumption for the constant material-removal rate machining processes such as turning and milling. However, no significant attempt is made to model energy consumed during variable material-removal rate machining processes like end face turning, grooving etc., which results in a substantial amount of error for the prediction of the total energy required for machining a product. In this work, experiments are performed on a computerized numerical control machine tool to acquire the material-removal energy consumption of end face turning process. The fluke 435 power analyzer is used to measure energy consumption. An empirical model is established between cutting parameters and energy consumed during the end face turning process. The coefficient of determination is used to evaluate the fitness of the model. The results indicate that the model can predict the end face turning energy consumption data accurately. The developed model can be further used to estimate the total energy consumption for machining of a product beforehand in early design stages and to identify the most suitable sustainable machining options.

Soil Bio-Impact Effectiveness for the Optimal Multicriterial Environmental Sustainability in Crop Production

Different bio-impacts affect the various properties and composition of soil, plant residues, harvests, and technological processes, as well as the interactions between different parts of the soil, working machine tools, energy consumption and environmental pollution with harmful gases. To summarize the wide-coverage investigations of various aspects of different bio-impact parameters, a multicriteria evaluation was conducted. Experimental research shows that different bioeffects such as those of agricultural practices can be oriented towards a reduction in fuel consumption, followed by reductions in CO2 emissions from machinery and changes in soil properties, dynamics of composition, yield and other parameters. A multicriteria assessment of the essential parameters would give farmers new opportunities to choose one optimal decision for reducing fuel consumption and increasing agricultural production, thereby reducing the negative environmental impact of soil cultivation processes, increasing yields and improving soil. Of all the properties investigated, from a practical point of view, the selection of the most important of all the essential links, such as reducing energy and expenditure, reducing environmental pollution, improving soil, and increasing yields and productivity, is reasonable. The evaluation of the bio-impact effects in agriculture by accounting for many criteria from several aspects was the main objective of the multicriteria assessment using the analytic hierarchy process. Based on the results of a multivariable research of fuel consumption—C1, C2, yield—C3, CO2 from soil—C4, density—C5, total porosity—C6, humus—C7, soil stability—C8, and soil moisture content—C9, the evaluation used experimental research data and the Simple Additive Weighting (SAW) mathematical method to find the best-case scenario. Multicriteria effectiveness was most pronounced after the first and third soil bio-impacts by using a solution of essential oils of plants, 40 species of various herbs extracts, marine algae extracts, mineral oils, Azospirillum sp. (N), Frateuria aurentia (K), Bacillus megaterium (P), seaweed extract. The most important goal was to achieve the best soil bio-impact effectiveness—minimized energy consumption from ploughing and disc harrowing operations, parallelly minimized harmful emissions from agricultural machinery, minimized CO2 from soil, soil density, maximized soil total porosity, soil humus, soil stability, yield and optimized soil moisture.

Formulation and Solution Methodology for Reducing Energy Consumption in Two-Machine Bernoulli Serial Lines

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In energy-intensive manufacturing enterprises, reducing the energy consumption in their production systems is urgent and challenging. In this article, an energy consumption optimization problem in a simple model of production systems (i.e., in the two-machine Bernoulli line) is investigated. Machine efficiencies are optimized so that the total energy consumption of machines is minimized, while a required production rate is ensured. The reported results enable a novel managerial paradigm for production systems, especially for energy-intensive systems. Although the model of the two-machine Bernoulli line is simple, as a cornerstone, this research will be extended, in the future, to long Bernoulli lines and more practical production systems, e.g., lines with geometric, exponential, and non-exponential machine reliability models.

Cutting Path-associated Energy Consumption of Milling Machining Process

Manufacturing industries face numerous challenges, such as reducing their energy consumption. Minimising energy usage is one of the initiatives towards sustainable manufacturing. Many researches have studied the surface quality of machined part, but studies on energy consumption during machining process are limited. Different cutting paths operations may influence energy consumption during machining. The objective of this research was to investigate the effect of cutting tool paths and parameters on energy consumption during machining. Aluminium 6061 alloy was face-milled using high-speed steel tool with different tool paths, namely, morphed spiral, parallel and spiral paths. The design of experiment technique was applied to optimise the experimental work, and response surface methodology was used to analyse the experimental result. Results showed that feed rate is the most influential parameter on the energy consumption of machining. Machining energy models were also generated for morphed spiral, parallel and spiral cutting paths. The R 2 of each model was higher than 0.92, which indicates that the model equations are applicable in predicting the energy consumption of a milling operation.

Energy efficiency improvement in farming equipment, for agricultural holdings

The use of depreciated and outdated agricultural equipment reflects both, on the increased cost of production (due to the higher energy component), and the greater amount of emissions produced during the operation of that equipment. The presented paper deals with energy and emission analysis of the replacement of existing farming equipment in a holding, with a new one. The analysis considers the baseline tractor and harvester and their specific agricultural operations, related to the processing of the soil and the harvesting of the production. The analysis is based on the existing energy consumption of the old machines and accounting information on the operation of the new equipment, one year after its commissioning. The study shows that the estimated energy savings from the measures of 17.2%, with a similar carbon footprint reduction. Based on the technical characteristics, CO, HC, HC + NOx and NOx emissions were also analyzed. The percentage of estimated carbon emission savings is higher than 43.5%.

A new tailored solution to predict blow efficiency and energy consumption of hammer-forging machines

Energy saving has become a real issue in process management and has led to the analysis of the energy consumption of forging machines, for the purpose of optimization. This study focuses on the amount of energy lost due to machine behaviour during the forming process. A spring-mass-damping model of the machine and tools system is associated with a billet model to describe hammer-forming operations only during the forging phase. The model parameters are identified with experimental measurements of process variables during a stroke, providing parameters specifically adapted to the machine-tools system. Then, model predictions are validated by the experimental upsetting of steel and aluminium billets. The model accurately predicts forging process variables for consecutive blows with different materials. The decrease in process efficiency and the evolution from inelastic to elastic blows after several strokes on the same billet are also predicted by the model. This methodology provides a new, tailored solution that enables forging manufacturers to predict the forging energy consumption of their own machines. The approach developed in this work concerns gravity drop hammers but is also transferable to other energy-driven machines.

An energy-aware scheduling algorithm under maximum power consumption constraints

This research investigates the production scheduling problems under maximum power consumption constraints. Probabilistic models are developed to model dispatching-dependent and stochastic machine energy consumption. A multi-objective scheduling algorithm called the energy-aware scheduling optimization method is proposed in this study to enhance both production and energy efficiency. The explicit consideration of the probabilistic energy consumption constraint and the following factors makes this work distinct from other existing studies in the literature: 1) dispatching-dependent energy consumption of machines, 2) stochastic energy consumption of machines, 3) parallel machines with different production rates and energy consumption pattern, and 4) maximum power consumption constraints. The proposed three-stage algorithm can quickly generate near-optimal solutions and outperforms other algorithms in terms of energy efficiency, makespan, and computation time. While minimizing the total energy consumption in the first and second stages, the proposed algorithm generates a detailed production schedule under the probabilistic constraint of peak energy consumption in the third stage. Numerical results show the superiority of the scheduling solution with regard to quality and computational time in real problems instances from manufacturing industry. While the scheduling solution is optimal in total energy consumption, the makespan is within 0.6 % of the optimal on average.

Experimental and statistical investigation of the effect of cutting parameters on surface roughness, vibration and energy consumption in machining of titanium 6Al-4V ELI (grade 5) alloy

This study aims to increase energy efficiency in turning operation. For this purpose, titanium 6Al-4V ELI (grade 5) alloy is turned to obtain optimum surface quality with minimum energy. For this reason, in order to determine the appropriate cutting conditions, cutting speed, feed rate and depth of cut have been chosen according to the cutting tool suitable for titanium material. In the turning process, surface roughness, vibration and energy consumption were measured. In Taguchi analysis, S/N ratios, S/N graphics, S/N response table, ANOVA results, pareto analysis results, it is determined that the effective parameter in surface roughness, vibration and energy consumption is feed rate. It has been observed that the surface roughness and energy consumption increase as the vibration value increases. The created Taguchi models have made estimations with a high percentage of accuracy (Surface roughness is about 82%; vibration is about 89%; energy consumption is about 98%).