Energy Consumption Of Machine Research Articles

Algorithm for solving multi-objective hybrid flow shop scheduling problem

Aiming at the multi-objective unrelated parallel machine hybrid flow shop scheduling problem, a multi-objective mathematical model is established with the goal of minimizing the maximum completion time, total machine energy consumption and machine processing cost. An improved multi-objective evolution algorithm based on decomposition (IMOEAD) is proposed. The uniform design table is used to generate the initial weight vector to improve the population diversity. Normal distribution crossover and adaptive Gaussian mutation are used to improve the global and local search capabilities of the algorithm. Individuals are selected in the neighborhood of the weight vector to generate new solutions. The non-dominated rank and crowding distance are used to update the external archive. The inverse generation distance, generation distance and number of non-dominated solutions are used as performance indicators. Through a large number of case simulations, the algorithm is compared with the non-dominated sorting genetic algorithm II and the multi-objective evolution algorithm based on decomposition. The results verify the effectiveness of the algorithm.

A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing

Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. Pioneer researches on multi-objective batch machines scheduling mainly focus on evolutionary algorithms, failing to meet the online scheduling demand. To deal with the challenges confronted by incompatible job families, dynamic job arrivals, capacitated machines and multiple objectives, we propose a clustering-aided multi-agent deep reinforcement learning approach (CA-MADRL) for the scheduling problem. Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. To alleviate time complexity of model training, a parameter sharing strategy is introduced between different reinforcement learning base model. By validating the proposed approach with 16 instances designed based on actual production data from a semiconductor manufacturing company, it has been demonstrated that the approach not only meets the high-frequency scheduling requirements of manufacturing systems for parallel batch processing machines but also effectively reduces the total job tardiness and machine energy consumption.

A Mathematical Programming Model for Minimizing Energy Consumption on a Selective Laser Melting Machine

The scheduling problem in additive manufacturing is receiving increasing attention; however, few have considered the effect of scheduling decisions on machine energy consumption. This research focuses on the nesting and scheduling problem of a single selective laser melting (SLM) machine to reduce total energy consumption. Based on an energy consumption model, a nesting and scheduling problem is formulated, and a mixed integer linear programming model is proposed. This model simultaneously determines part-to-batch assignments, part placement in the batch, and the choice of build orientation to reduce the total energy consumption of the SLM machine. The energy-saving potential of the model is validated through numerical experiments. Additionally, the effect of the number of alternative build orientations on energy consumption is explored.

An energy consumption field model considering motion position errors for energy efficient machining on CNC machines:CNC programming perspective

Motion position errors, which are caused by factors such as machine tool assembly accuracy and thermal effects, often result in deviations in both machining energy consumption and precision, compared to what is expected. Furthermore, the degree of deviations dynamically varies with the machining schemes. These issues hinder the implementation of high-quality energy-saving machining. To address this challenge, an energy consumption field model is proposed, reflecting the impact of motion position errors on energy consumption and precision variations in different machining scenarios. Firstly, by analysing the conversion process from process plans to programming instructions, a dynamic cascading mechanism is revealed, driven by motion position errors that impact tool cutting trajectory offset, machining energy consumption and precision deviation. Subsequently, a dynamic quantification model for the cutting trajectory offset at the programming origin is established by correlating static geometric errors and dynamic spindle rotation errors with the programming origin coordinates. Finally, a transformation function is established by using the cutting trajectory offset as an intermediate variable to correlate motion position error, machining energy consumption and precision deviation. A spatial energy consumption field model is developed. Experimental results indicate that, while meeting precision constraints in the XYZ directions, a 10.96% reduction in energy consumption is achieved through the correction of programming origin coordinates.

Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higher-layer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17 %, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

Энергосбережение и оценка энергоэффективности угледобывающего предприятия

The article is devoted to consideration of issues related to assessing the energy efficiency of a coal mining enterprise. Coal mining enterprises are characterized by a large volume of energy intensive ore mining operations. The article presents possible ways of energy saving in the mining industry. During the research, a system of energy efficiency indicators for coal mining enterprises is presented. Materials and methods. The article proposes a system of indicators for coal enterprises based on existing methods and approaches to assessing energy efficiency. Energy consumption dependence graphs, benchmarking, and pinch analysis are considered as a starting point. Results. The study presents a system of energy efficiency indicators for coal mining enterprises. The proposed system has several levels. The existing methods and approaches for assessing this important indicator for industrial enterprises in the coal industry as a whole are considered. The author’s approach to formalizing the system of energy efficiency indicators for coal mining enterprises is proposed. The significance of energy audit as a key assessment measure is indicated and its stages are highlighted. The influencing factors determining energy costs and their reduction for a coal mining enterprise are systematized, barriers preventing the practical implementation of energy saving measures are analyzed. Discussion. The proposed metrics system provides a comprehensive framework for assessing the energy efficiency of coal mining enterprises at various levels. Its flexibility allows it to be applied to the analysis of individual equipment units, production processes, and overall enterprise performance. Its multi-level approach provides a detailed understanding of energy consumption, which allows for informed decision-making to optimize operations. At the equipment level, the metrics system evaluates the energy consumption and efficiency of individual machines and devices. At the production process level, the metrics system analyzes the energy consumption and efficiency of various stages of coal mining. At the enterprise level, the metrics system provides a comprehensive assessment of overall energy efficiency. It takes into account energy consumption at all levels and allows enterprises to compare their performance with each other and with industry benchmarks. Conclusion. Energy efficiency assessment of a coal mining enterprise allows to understand how and where energy is used, where its use can be improved, and how to reduce its costs. To conduct an energy efficiency assessment, the article proposes a system of indicators, highlighting the features of an energy audit. The main advantage of the proposed system of indicators is that it covers a wide range of energy consumption indicators, which are carefully selected and located at different levels of detail. In general, a balanced approach to the level of detail and generalization in the system of indicators provides flexibility in adapting indicators to specific research questions and helps to interpret the results in the context of structural differences between energy systems. Resume. Overall, the proposed metrics system offers a comprehensive approach to analyzing the energy efficiency of coal mining enterprises, covering all levels of operations. Its use allows enterprises to improve energy efficiency, reduce operating costs, and contribute to a more sustainable coal mining industry. Suggestions for practical applications and directions for future research. The use and implementation of the developed system at a coal mining company allows it to improve energy efficiency, reduce operating costs and contribute to a more sustainable coal mining industry.

Effects of Failure Criteria on Quantitatively Determining the Machining Mechanics for CFRP with Different Tool Rake Angles

Abstract The machining mechanics of carbon fiber reinforced polymer (CFRP) materials is influenced by the coupled effects of the workpiece anisotropy, tool edge geometry, and cutting parameters. Predicting the chip formation mechanism is crucial for optimizing cutting parameters, reducing tool wear, and improving efficiency and surface quality. This study quantitatively evaluates the effect of main CFRP failure criteria on the chip formation mechanism in modeling the machining mechanics of CFRP. The results show that the Hashin-Puck and Davila criteria excel at capturing chip formation across all fiber orientations because of the incorporation of ‘internal friction’ concept, while others only achieve accurate predictions in specific fiber orientation ranges due to improper shear strength consideration. The sources of the prediction similarities, differences, and limitations of failure criteria are experimentally validated. Sensitivity analyses quantitatively determine the effect of the tool rake angle on the machining energy consumption and cutting forces across the fiber orientation range. This research can be used to select the optimal failure criteria, design proper cutting tool geometry, and inform the cutting parameter choices for CFRP machining operations.

Low-Carbon Flexible Job Shop Scheduling Problem Based on Deep Reinforcement Learning

As the focus on environmental sustainability sharpens, the significance of low-carbon manufacturing and energy conservation continues to rise. While traditional flexible job shop scheduling strategies are primarily concerned with minimizing completion times, they often overlook the energy consumption of machines. To address this gap, this paper introduces a novel solution utilizing deep reinforcement learning. The study begins by defining the Low-carbon Flexible Job Shop Scheduling problem (LC-FJSP) and constructing a disjunctive graph model. A sophisticated representation, based on the Markov Decision Process (MDP), incorporates a low-carbon graph attention network featuring multi-head attention modules and graph pooling techniques, aimed at boosting the model’s generalization capabilities. Additionally, Bayesian optimization is employed to enhance the solution refinement process, and the method is benchmarked against conventional models. The empirical results indicate that our algorithm markedly enhances scheduling efficiency by 5% to 12% and reduces carbon emissions by 3% to 8%. This work not only contributes new insights and methods to the realm of low-carbon manufacturing and green production but also underscores its considerable theoretical and practical implications.

Investigating the Wear Mechanism of TiAlN/TiN PVD-Coated WC Inserts in End Milling of Incoloy 925 Under Synergy of Bio-Degradable Fatty Acids and Nano-Metallic Solid Lubricant

Abstract Rapid tool wear in machining difficult-to-cut material is a significant challenge since it is related to tool cost, surface integrity of machined components, and power consumption. The reduction in tool wear may be accomplished by cutting fluids, especially in modern biodegradable fatty acids. Because of its high lubricity, coconut oil is the best choice for lubricating fatty acids. However, this oil has poor heat transfer properties. Hence, to further improve its properties, copper (Cu) nanoparticles (NPs), which have a high thermal conductivity to cost ratio compared to other nanoparticles in addition to anti-frictional and anti-wear properties (due to self-repairing properties caused by their low shear strength and easy deposition on contacting surfaces), have been dispersed in coconut oil, thus creating a nano-lubricant (nanofluid). It is found that synthesized nano-lubricants perform better in thermo-physics and tribology than pure coconut oil. Further, research has been carried out on the combined effects of coconut oil and 0.3 vol% Cu-NPs on Incoloy 925's end milling performance and tool wear mechanism (viz., coating delamination, adhesion, abrasion, and oxidation). It is found that tool wear-rate is reduced, which in turn increases tool life, due to effective cooling and lubrication when nanofluid-based minimum quantity lubrication (NMQL)-assisted machining is performed rather than dry and pure oil-based minimum quantity lubrication (MQL) conditions. Consequently, energy consumption in machining and surface quality of machined components are improved.

Modeling and optimization of energy consumption in wire cut electrical discharge machining

WEDM (Wire Electrical Discharge Machining) is widely implemented in the manufacture of dies and challenging materials due to its use of spark discharge to shape a workpiece. However, existing research focuses primarily on enhancing processing efficiency, accuracy, and quality, with limited investigation into energy conservation and emissions reduction during processing. This paper constructs an energy consumption model to forecast energy usage during electric machining and proposes a low energy consumption optimization model founded on processing parameters. A case study revealed that the energy consumption model is 98.06 % accurate throughout the processing process and at least 92 % precise in predicting energy consumption for a single process. Upon utilizing the low energy consumption optimization model, the results show a reduction in energy consumption and carbon emissions by 11.5 % and 5.74 %, respectively, compared to traditional methods. The models presented in this paper can enhance EDM machining efficiency while concurrently minimizing energy consumption and carbon emissions, thereby supporting the adoption of energy-saving and efficient practices in electric machining.

An improved MOEA/D for multi-objective flexible job shop scheduling by considering efficiency and cost

To achieve cost reduction and efficiency improvement in the production process of the aerospace industry, this study establishes a mathematical model for the multi-objective flexible job shop scheduling problem (MOFJSP). The model comprehensively considers four optimisation objectives: completion time, tool number, machine load, and machine energy consumption. For solving the MOFJSP, an improved MOEA/D (IMOEA/D) algorithm is proposed. To improve the quality of solutions, the algorithm introduces another global update strategy in addition to the original substitution operation. The population is initialised using four allocation rules, and the neighbourhood is dynamically updated based on the degree of population evolution. The simulation results on a single instance indicate that the simulation time of IMOEA/D has decreased by 10.5% and machine energy consumption has reduced by 9.5%. Moreover, when compared to various classic multi-objective algorithms on 25 benchmark instances, the IMOEA/D algorithm achieved optimal results in 19 instances for inverted generational distance comparisons, 15 instances for hypervolume comparisons and nearly all instances for set coverage comparisons. The results reveal that the proposed IMOEA/D is effective and competitive in solving MOFJSP.

An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop

Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource constrained flexible job shop inverse scheduling problem (RCFJISP) is proposed in this paper, which aims to cope with uncertain events by simultaneously adjusting the machine, worker and process parameters of the original schedule. A multi-objective optimization model is constructed to minimize the makespan, worker cost, machine energy consumption and deviation index. Furthermore, an improved memetic algorithm (IMA) is developed for solving the proposed problem. In IMA, a novel double-layer encoding mechanism is designed to enhance the capacity in exploring new solution’s domains. Three initialization strategies utilizing original scheduling information are designed to improve the quality of initial solutions. An adaptive mutation strategy and a local search mechanism are designed to enhance exploration and exploitation ability of the algorithm. And a crowding operator is proposed to reflect the diversity of the population effectively. In computational experiments, 28 extended benchmarks are constructed, and the effectiveness of the proposed strategy and algorithm is verified by comparing IMA with its 4 variants and other 4 widely used algorithms. Finally, two inverse scheduling problems of a real-world hydraulic cylinder machining workshop under two uncertain situations are studied. The results demonstrate that IMA can effectively solve the actual inverse scheduling problem. With a slight adjustment to the original scheduling, it can reduce the makespan by 11.5%, the worker cost by 8.1% and the machine energy consumption by 27.9% on average.

Efficient low-carbon manufacturing for CFRP composite machining based on deep networks

ABSTRACT The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions.

Numerical and experimental characterization of coffee roasting in a rotary drum type roaster by forced internal convection

The effect of air injection into a coffee roasting chamber of a 2 kg rotary drum roaster is investigated on the temperature profiles and organoleptic properties of a Catuai coffee variety. In the experimental study, 1 kg of coffee beans were used and the air injection velocities were varied for the cases of 0 (standard roasting design), 1, 2 and 3 m/s; testing in triplicate and the two most representative were taken. In the coffee roaster, the temperature was monitored simultaneously in the air inlet duct, roasting chamber and gas outlet duct. The organoleptic properties were examined by a group of professionals from Negociaciones Agroindustrial Arévalo S.A. NARSA in the province of Chanchamayo - Peru, which has a G4 laboratory certified by the Specialty Coffee Association (SCA). In the organoleptic ratings, a maximum score of 83.21/100 was found for the 2 m/s test and a minimum of 80.63/100 for the 1 m/s test. For the numerical characterization, the principle of fluid mechanics such as turbulent regime flow and energy transport were used by the finite volume (FV) method using SolidWorks 2021 Flow Simulation software. The numerical results showed that for the 2 m/s test, the temperature of the bean reaches 195 oC at 500 s, indicating that the whole coffee bean reaches the cooking temperature required for the development time prior to eviction; while for the 1 m/s test, it did not exceed 190 oC. It is shown here that the effect of air injection in the roasting chamber, caused organoleptic changes with respect to the conventional design without air injection, due to a sustained cooking at different times and an adequate development time. However, there is a speed threshold where when exceeded, temperatures inside the chamber remain low, development time is extended (machine energy consumption is extended), and organoleptic properties should not improve. However, as the test speeds are exceeded, temperatures inside the chamber remain low, causing the development time to be extended with higher energy consumption and under these conditions the organoleptic properties are not improved.

Reducing Soil Adhesion to Metal Plates Using Ultrasonic Waves

ABSTRACT One of the most important factors involved with soil-engaging agricultural field operations is soil adhesion. Reducing soil adhesion has significant effects on the operation and energy consumption of agricultural machines. In this study, a laboratory method has been carried out to study the effects of ultrasonic waves on soil adhesion. Normal adhesion tests were performed on two types of soil using three plates with different contact areas. The plates consisted of an iron ring as the main body covered by a stainless steel sheet on the top and an aluminum sheet at the bottom. The internal space of the plates was filled with a fluid that was capable of transmitting the waves produced by piezoelectric crystals attached under the top sheet. When ultrasonic waves reached the metal surface which was in contact with the soil, soil adhesion force was measured while the plate was separating from the soil. Experimental results revealed that ultrasonic waves have a significant effect on soil adhesion in different moisture contents. The results of the analysis of variance (ANOVA) indicated that all the test parameters had significant effects on reducing the soil adhesion to the metal plates. However, multiple effects of frequency and other parameters on the soil adhesion were not significant. An increase in ultrasonic frequency from 0 to 1800 kHz had a significant effect on soil adhesion to metal plates at a 5% level. Results showed that the most effective application time that causes more reduction in soil adhesion is 60 s.

OPTIMIZATION EXPERIMENTAL STUDY OF MACHINING ENERGY CONSUMPTION OF ZIG-ZAG MILLING CUTTING PATH USING RSM.

Machining operations in CNC milling which remove the work material require power and energy to activate the machine components such as spindle motor, table and tool movement in order to withstand the high friction and load between tool and work material. Energy consumption during cutting operation is greatly influenced by the machining condition and parameters. This experimental research aims to investigate how energy responds to changes in the machining parameters such as depth of cut, spindle speed, and feed rate during face milling operation of CNC machine. The high-speed steel (HSS) tool with a 10mm diameter was used to face mill the 40mm x 40mm of Aluminum 6061. The design of experiment technique using Response Surface Methodology (RSM) is utilized to optimize the experimental work. Power usage and machining time were recorded for each machining process, which is then used to determine the machining energy consumption. The interaction between machining parameter and energy is comprehensively visualized using surface and contour plot. Additionally, the ANOVA analysis investigates the feed rate as the most influential parameter to the machining energy. Finally, the regression equation of machining energy is generated with reliability (R) value of 0.88 which can be used as an energy prediction model.

THE EFFECT OF ECONOMIZER APPLICATION ON ENERGY ECONOMY IN STENTER MACHINES USED

This study investigates the impact of economizer application on reducing energy consumption in the textile industry. It starts by analyzing the influence of traditional drying methods on both energy usage and operational costs. Next, it explains the operation of ramie machines and economizer technology. The primary focus is on the contributions of economizer application to energy efficiency. This includes highlighting the reduction in energy consumption and cost savings in operations. The study also underscores the importance of economizer application for environmental sustainability by helping to reduce greenhouse gas emissions. Furthermore, it discusses the investment cost and payback period of economizers, emphasizing how they can enhance a company's economic competitiveness. Real-world examples and success stories are provided to demonstrate the practicality of implementing economizers. In conclusion, this research underscores the significance and potential of economizer applications in improving energy economics within the textile sector. This technology not only enables energy savings but also contributes to environmental sustainability, supporting the creation of a more sustainable future in the textile industry. It also compares the energy consumption of a single-pass ramie machine with and without economizer use, assuming a 24-hour daily operation for 300 days, while thoroughly evaluating the associated advantages and disadvantages.

Research of energy consumption of the vibration machine for surface restoration of working bodies of tillage units

Research of energy consumption of the vibration machine for surface restoration of working bodies of tillage units

A Large-Diameter Earth–Air Heat Exchanger (EAHX) Built for Standalone Office Room Cooling: Monitoring Results for Hot and Dry Summer Conditions

Earth–air heat exchangers (EAHX) use the soil thermal capacity to dampen the amplitude of outdoor air temperature oscillations. This effect can be used in hot and dry climates for room cooling, and depending on the EAHX design, this cooling can be achieved with very few resources other than those used during EAHX construction. This is an obvious advantage compared to the significant energy consumption and operational costs of refrigeration machines traditionally used in room cooling. Despite the large number of papers on EAHXs available in the scientific literature, very few deal with large-diameter EAHXs (with pipe diameters larger than 0.30 m), and even fewer present monitoring data gathered from a built and functional large-diameter EAHX. The present paper uses monitoring data and provides a detailed quantitative analysis of the performance of a large-diameter EAHX built for standalone cooling of an existing office building. The field monitoring was carried out during a characteristic hot and dry summer period of the south of Portugal. Results show that outdoor air to EAHX exit air temperature gradients reach 9 K and cooling capacities exceed 27 kW. Moreover, the studied EAHX is capable of standalone cooling for outdoor air temperatures up to 33 °C and meets more than 50% of the room design cooling demand for outdoor air temperatures as high as 37 °C. This evidences that large-diameter EAHXs have the potential to achieve significant reductions in CO2 emissions and in energy consumption associated with building cooling in hot and dry climates.

Greenhouse gas emission in the whole process of forest fire including rescue: a case of forest fire in Beibei District of Chongqing.

In the context of global high temperature, the harm of greenhouse gases (GHG) emissions caused by frequent forest fires to the environment cannot be ignored. Existing research only calculates the GHG generated by the burning of forest vegetation, ignoring the GHG generated by the fire-driven social rescue activities. Taking the forest fire in Beibei District, Chongqing City, China, as an example, this paper studies and establishes the GHG emission accounting method for the whole process of forest fire from ignition to fire extinguishing through three processes: vegetation burning, rescue transportation, and on-site fire extinguishing. It covers three GHG calculation types: biomass burning, traffic activity level comprehensive energy consumption, and machine energy consumption. Among them, the CO2 produced by the burning of coniferous forest, the support transportation of rescue teams in Yunnan province, and the motorcycle transportation at the fire extinguishing site accounted for a relatively high proportion in the corresponding processes, reaching 12,761.445 t, 118.750 t, and 1056.980 t, respectively. Finally, through data analysis, suggestions on GHG emission reduction related to forest tree regulation and optimization of rescue and fire extinguishing management are put forward, which provides a direction for future research on carbon reduction in the whole process of forest fire events.