Energy Consumption Of Machine Research Articles

Control of braking energy recovery of lifting machines

The increase in the energy consumption of machines when operating in unsteady modes is caused by many reasons, the main of which are deviation from the calculated operating modes and braking of the machine with irretrievable energy losses. When driving cars from electric motors, the recovery of braking energy can be carried out by switching them to the electric generator mode and returning the braking energy to the general power supply network. However, the absence of parallel consumers in the general power supply network requires the presence of an electric energy accumulator to recover braking energy. Effective recovery of braking energy can be carried out in a flywheel energy storage device with a high service life and high specific indicators of stored energy per unit weight of the storage device using differential gear mechanisms having two degrees of freedom.

A Data and Model Hybrid Driven Method for Machining Energy Consumption Prediction of Boxy Parts

A Data and Model Hybrid Driven Method for Machining Energy Consumption Prediction of Boxy Parts

Analysis of Available Data and Estimation of Energy Supply of Mechanical Processing

The issue of energy efficiency of machining processes has been the focus of attention for the last 50 years. This is due to the fact that in comparison with other industries, metallurgy and mechanical engineering are characterized by a high level of energy intensity of products. Analysis of available in the literature indicators of energy costs of processing processes and determination of consistent data from these indicators. Establishing an analytical relationship between the grinding energy and the energy required for melting the finishing material. It is determined that the cutting energy of the material during chip for­mation is close to the energy required for melting the metal, and the excess amount of spent grinding energy is spent on friction between the chips and the grinding wheel. It is shown that in the literature there are data on the energy consumption of different treatments: turning - 2 kJ/cm3, milling - 9 kJ/cm3, grinding - 60 kJ/cm3, electrospark treatment - 3000 kJ/cm3. At the same time, the specific energy consumption of steel grinding is 60 kJ/cm3. And the specific heat of fusion of steel is 0.64 kJ/cm3. As a result, 100 times more heat is pumped into the steel during grinding than is needed to melt it. That is, there is a contradiction. To find ways to resolve this contradiction, it is more accurate to estimate the specific energy consumption of diamond-abrasive machining of superhard materials through additional consideration, in addition to productivity and effective machining power, wear of the working layer of the wheel.

A Production Plan Considering Parallel Machines and Deteriorating Effects: Minimizing the Makespan in the Section of Steel Box Girder Processing

With the popularization of energy conservation and emission reduction, amounts of industrial production has taken energy conservation as a goal to achieve. The paper considers an online parallel-batch scheduling problem with deteriorating and incompatible families on identical machines to minimize the makespan, which minimizes the maximum energy consumption of machines. Specifically, the processing time of job [Formula: see text] is defined by an increasing function of its starting time [Formula: see text], i.e., [Formula: see text], where [Formula: see text] is the deterioration rate of job [Formula: see text]. For the problem, we propose an online algorithm with a competitive ratio of [Formula: see text], where [Formula: see text] is the largest deterioration rate in an instance. Furthermore, the paper presents a concise computational study of the numerical experiment to show that our algorithm performs very well in practice of this model.

Dynamics of energy consumption of multi-element traction transport machines

The results of the study of energy costs of multi-element traction transport machines are presented. The method of determining the energy costs of traction transport machines is substantiated. The energy costs of a multi-element agricultural unit are theoretically determined. The results of experimental studies of multi-element traction transport machines on the example of an agricultural unit consisting of a John Deere 8345R tractor, a hopper for John Deere 1910 seed and John Deere 1895 direct seeders. The proposed method for determining energy costs allows estimating the energy consumption of the machine as a whole or its elements. Elements of multi-element traction transport machines in the analysis of energy costs are energy-dynamic models in the form of equations of state space, which allows flexible formation of systems of equations when changing the composition and sequence of elements of the multi-element machine under study. Three ways of connecting energy-dynamic models of elements are considered: parallel and series connection and connection with feedback. It has been established that the energy expenditure by the elements of the agricultural aggregate varies according to the harmonic law with a period of 7.5 s and a transient process at the beginning of movement. The highest energy expenditure occurs in the planter 3.7·106 J with a span of 0.15·106 J. The energy consumption of the tractor and hopper is 2.7·106 J and 3.25·106 J with a span of 0.1·106 J and 0.15·106 J. Experimental studies were carried out using the measuring system of dynamics and energy of mobile machines. The fluctuations in energy consumption on the front wheels are in the same phase and those on the rear wheels are in antiphase and amount to 7.25·105 J with a span of 0.5·105 J for the front left wheel; 7.55·105 J with a span of 0.5·105 J – front right; 6.25·105 J with a span of 0.4·105 J – for the rear wheels. Keywords: energy costs, estimation method, multi-element traction transport machine, energy

Takım Yolu Ayarlarının Lif Levhanın Yüzey Pürüzlülüğü ve CNC Makinesinin Enerji Tüketimi Üzerine Etkileri

Aim of study: The aim of this study was to determine the effects of cutter parameters on the energy consumption of CNC machine, and surface roughness of medium density fiberboard (MDF). Material and methods: Experiments were conducted using a three-axis CNC router. The energy consumption and surface roughness values were measured using a wattmeter (TT-Technic PM 001) and roughness tester (Mitutoyo SJ 210), respectively. The values of the Ra, Rq, and Rz roughness parameters, and energy consumption were determined. Main results: The surface roughness increased with increases in the step over and feed rate, whereas the energy consumption decreased. In addition, the energy consumption increased when a high spindle speed was used. When the spindle speed is increased from 8000 rpm to 16000 rpm, the energy consumption increases by 154%. When the stepover is increased from 20% to 60%, energy consumption is reduced by 63%. When the feed rate is increased from 3 m/min to 7 m/min, the energy consumption is reduced by 60%. Highlights: The energy consumption increased as the step over and feed rate decreased. The energy consumption increased as the spindle speed increased. The surface roughness increased with the step over and feed rate

Tough-brittle transition mechanism and specific cutting energy analysis during cryogenic machining of Ti–6Al–4V alloy

Reducing the cutting energy consumption in machining is important to improve the sustainability of cryogenic machining. Currently, the analysis of cutting energy for cryogenic machining mostly relies on equipment testing, while less research has been conducted on energy analysis based on material removal mechanisms. In this paper, the chip formation mechanism of cryogenic machining is studied on the basis of liquid nitrogen orthogonal milling experiments with Ti–6Al–4V alloy, and the corresponding specific cutting energy model is established. First, the effects of cooling conditions and process parameters on chip microscopic morphology were analyzed based on orthogonal milling experiments. Next, a specific cutting energy model was established according to the chip formation mechanism. Finally, the reliability of the model was verified by cutting forces. The results show that in cryogenic machining, when the cutting speed is less than 200 m/min, adiabatic shear dominates the formation of serrated chips, and the cutting energy consumption is higher than that of dry cutting. As the cutting speed exceeds 200 m/min, adiabatic shear and periodic fracture together dominate the formation of serrated chips, and the energy consumption of cutting is significantly reduced at this time. This research provides a reference and guidance for the sustainable development of cryogenic machining.

A hybrid meta-heuristic for the flexible flow shop scheduling with blocking

This paper investigates a multi-stage flexible flow shop scheduling problem with blocking on batch processing machines. The objective is to minimize the makespan and the total energy consumption of machines, simultaneously. We first describe the problem and formulate its mathematical model. Based on problem characteristics, we design a hybrid meta-heuristic algorithm based on ant colony optimization and genetic algorithms to solve the problem. In order to solve this problem efficiently, the proposed algorithm consists of two components, which are used to construct batch and overall scheduling, respectively. The search strategy based on ant colony optimization algorithm is designed to construct batches to improve the convergence of the algorithm. Based on this, a global scheduling strategy based on genetic algorithm is proposed to intensify the diversity. Finally, extensive simulation experiments are conducted to verify the effectiveness and efficiency of the proposed algorithm.

High stability multi-objective decision-making approach of dry hobbing parameters

High-speed dry hobbing is an advanced and green manufacturing technology. The use of intelligent algorithm for process parameter decision-making is receiving more and more attention in dry hobbing. The process parameter results of each operation of these methods are inconsistent, that is, the numerical fluctuation of results. To resolve this problem, a multi-objective decision-making approach of process parameters is proposed for high-speed dry hobbing based on K-means clustering, multi-objective Runge Kutta optimizer (MORUN) and Analytic Hierarchy Process (AHP). First, based on the past process cases of dry hobbing, K-means clustering is used to determine the cluster centers and case clusters. For the problem of dry hobbing process parameters to be decided, the distance from each cluster center is calculated and the similar case clusters are found. The numerical range of process parameters is determined. Then, based on the well-established machining parameters model of dry hobbing with the optimization objectives of machining energy consumption, quality and time, MORUN is developed and used to find the optimized process parameters. Finally, regarding the users' emphasis on objectives, AHP is used to sort all the optimized process parameters obtained, and the first ranked process parameter is used for machining. The feasibility of the proposed approach is verified by the actual hobbing. Compared with other well-established methods, the proposed approach can effectively solve the problem of numerical fluctuation of optimization results, has high stability, and select the optimal term from several optimization process parameters.

An estimation methodology of energy consumption for the intelligent CNC machining using STEP-NC

An estimation methodology of energy consumption for the intelligent CNC machining using STEP-NC

A service-oriented energy assessment system based on BPMN and machine learning

Increasing energy cost and environmental problems push forward research on energy saving and emission reduction strategy in the manufacturing industry. Energy assessment of machining, as the basis for energy saving and emission reduction, plays an irreplaceable role in engineering service and maintenance for manufacturing enterprises. Due to the complex energy nature and relationships between machine tools, machining parts, and machining processes, there is still a lack of practical energy evaluation methods and tools for manufacturing enterprises. To fill this gap, a serviced-oriented energy assessment system is designed and developed to assist managers in clarifying the energy consumption of machining in this paper. Firstly, the operational requirements of the serviced-oriented energy assessment system are analyzed from the perspective of enterprises. Then, based on the establishment of system architecture, three key technologies, namely data integration, process integration, and energy evaluation, are studied in this paper. In this section, the energy characteristics of machine tools and the energy relationships are studied through the working states of machine tools, machining features of parts and process activities of processes, and the relational database, BPMN 2.0 specification, and machine learning approach are employed to implement the above function respectively. Finally, a case study of machine tool center stand base machining in a manufacturing enterprise was applied to verify the effectiveness and practicality of the proposed approach and system.

Energy-efficient open-shop scheduling with multiple automated guided vehicles and deteriorating jobs

There is growing interest in energy-efficient production scheduling research because of the increasing energy shortage. However, most existing studies along this line of research have not considered the energy consumed by automated guided vehicles (AGVs) used in modern smart factories for production scheduling purposes. In this paper, we study an energy-efficient open-shop scheduling problem with multiple AGVs and deteriorating jobs. A multi-objective model with four objectives is formulated, aiming to simultaneously minimise the maximum ending time of all AGVs, the total idle time of machines and AGVs, the total tardiness of jobs, and the total energy consumption of machines and AGVs. An improved population-based multi-objective differential evolution (IMODE) algorithm is developed to solve the problem. The IMODE makes use of a problem feature-based heuristic and a mean entropy method to enhance the diversity of its initial population. A novel grey entropy parallel analysis-based fitness evaluation mechanism with reference points is adopted to evaluate the candidate solutions. To improve the local search ability of IMODE, a multi-level local search strategy is used. In the experimental study, Taguchi analysis is employed to obtain the best parameter combination. The effects of the main components of IMODE are validated via comprehensive comparison experiments. Extensive experimental results show that the IMODE is preferable to other well-known multi-objective algorithms at solving the problem being considered.

Energy consumption analysis of direct twisting machine

This paper mainly discusses the energy saving measures of the direct twisting machine and its development direction. Through the analysis of the energy consumption mechanism of the direct twisting machine and the theory of yarn balloon, the law that affects the energy consumption during the yarn twisting process is studied. By building an experimental platform, the energy consumption data of the direct twisting machine under various twisting configurations and various yarn deniers are measured, and then the empirical equation of the energy consumption of the direct twisting machine is obtained by the method of data fitting. With the help of this empirical equation, a purposeful and reasonable quantitative adjustment of each parameter can be achieved in the twisting process, which has the effect of energy saving and emission reduction.

An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm

Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To investigate this problem, 9 cases were used. The experiments show that it produces a massive amount of energy when a job is processed fast. Energy consumption is lower when machining at a slower speed. The performance of the GWO algorithm has been compared to that of the Cuckoo Search (CS) algorithm in several experiments. In tests, the Grey Wolf Optimizer (GWO) outperforms the Cuckoo Search (CS) algorithm.

An improved multi-objective evolutionary algorithm based on decomposition for solving re-entrant hybrid flow shop scheduling problem with batch processing machines

Cold-drawn seamless steel pipes play a pivotal role in special industries. However, scheduling plan, as the core of the production, is manually made, which leads to the issue of low production efficiency. The change in processing time or job families when the jobs reenter and the appropriate buffer size of batch processing machines are not considered in the current research. The energy consumption is not considered as an important objective as well. These gaps motivate this study. The production process of cold-drawn seamless steel pipes can be defined as a re-entrant hybrid flow shop scheduling problem with batch processing machines. The model is formulated to minimize the makespan and the energy consumption of the batch processing machines. An improved multi-objective evolutionary algorithm based on decomposition is proposed to address this problem. To make an energy-efficient production scheduling, the buffer size of batch processing machines is discussed and the batching method is developed. Besides, a greedy selection strategy is proposed to choose a machine for cold-drawn operation considering the influence of the long processing time. The results of experiments show that the appropriate buffer size is determined to be the same as the capacity of batch processing machines. The proposed greedy selection strategy can select suitable machines for jobs. The results prove the effectiveness of the batching constraint and the balanced decoding algorithm, and the proposed algorithm can effectively solve the re-entrant hybrid flow shop scheduling problem with batch processing machines.

Minimizing the energy consumption of hole machining integrating the optimization of tool path and cutting parameters on CNC machines

Minimizing the energy consumption of hole machining integrating the optimization of tool path and cutting parameters on CNC machines

Analysis of the Twisting Tension in the Direct-Twisting Machine and the Fitting Model Based on the Experimental Data

The tension of the balloon yarn in the direct-twisting machine affects the yarn breakage and balloon shape, which in turn affects the energy consumption and yarn productivity of the direct-twisting machine. At present, research on the balloon tension of an industrial yarn direct-twisting machine is very rare, both in China and abroad. In this regard, this paper establishes a theoretical model for balloon yarn tension during the yarn twisting process of an industrial yarn direct-twisting machine based on yarn balloon kinematics. The experiment of the yarn balloon tension of the direct-twisting machine under different yarn fineness and different spindle angular speed is carried out. The influence of the angular speed of the spindle, the yarn fineness, and other factors on the tension of the balloon yarn in the direct-twisting machine is investigated. By using mathematical statistics and regression analysis methods, the fitting equations of the yarn balloon tension of the direct-twisting machine are established. The research results show that the relationship between the balloon yarn tension of the direct-twisting machine, the angular velocity of the spindle, and the yarn fineness can be fitted by a quadratic polynomial. The predicted value of balloon yarn tension based on the fitted equation has a small relative error compared to the measured value, and the prediction result is more reliable and accurate. This paper contributes specifically by adding to the understanding of how to model the yarn tension for the specific machine and the range of the test. It also contributes to the generical methodology on how to develop such a model.

Mechanisms for leveling the carbon footprint in the production of grain products

In this study, an attempt was made to determine the mechanisms for leveling the carbon footprint in the production of grain based on the development of the concept of state regulation. The article considers the impact of grain production on the formation of a carbon footprint - greenhouse gas emissions (carbon dioxide, methane, nitrogen), which were produced both with human participation and without. The most important determinants of carbon emissions are nitrogen fertilizers (8-49%), straw combustion (0-70%), machine energy consumption (6-40%), irrigation energy consumption (0-44%) and CH4 emissions. from the fields (15-73%). The mechanisms for regulating the carbon footprint are described, an assessment of its economic impact on domestic agricultural production is given, the threats to the carbon footprint and ways to reduce greenhouse gas emissions are identified.

QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment.

This paper deals with the challenging problem of scheduling users’ tasks, while taking into consideration users’ quality of service (QoS) requirements, with the objective of reducing the energy consumption of physical machines. This paper presents a model to analyze the current state of the running tasks according to the results of the QoS prediction assigned by an ARIMA prediction model optimized with Kalman filter. Then, we calculate a scheduling policy with a combined particle swarm optimization (PSO) and gravitational search algorithm (GSA) algorithms according to the QoS status analysis. Experimental results show that the proposed HPSO algorithm reduces resources consumption 16.51% more than the original hybrid algorithm, and the violation of service-level agreement (SLA) is 0.053% less when the optimized prediction model is used.

Deep learning analysis for energy consumption of shield tunneling machine drive system

Inaccurate estimation of energy from the shield driving system may result in serious energy loss and low tunneling efficiency. A deep learning network is developed in this study to optimize the energy consumption of cutterhead drives in a shield tunneling system. The proposed model, KCNN-LSTM, cooperates convolutional neural network (CNN) and long short-term memory predictor (LSTM) based on clustering algorithm to realize accurate energy consumption forecasts. First, geological conditions, shield operational parameters, and geometry are characterized and identified using a feature extraction strategy. The proposed model is used to decompose the energy consumption values and extract the sequential and implicit features of nonlinear interactions. The prediction is then applied to a real tunnel field case study. The modeling results are evaluated by comparing its performance with the reproduced Naïve predictor and other proven deep learning networks including recurrent neural network (RNN), CNN, and LSTM. The comparisons reveal that the proposed model outperforms the other conventional techniques in predictive accuracy. The KCNN-LSTM model provides an accurate and feasible tool for adjusting the energy consumption of cutterhead drives in shield tunneling.