Energy Consumption Of Machine Research Articles

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.

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.

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.

Integrated optimization of process planning and scheduling problems based on complex networks

Industrial information integration can promote modern manufacturing enterprises to achieve efficient information processing and data integration in manufacturing systems, providing a good framework for solving process planning and workshop scheduling integration problems. To explore optimization issues in an intelligent manufacturing environment, find a better way to solve the integration of process planning and scheduling, and analyze basic features of manufacturing task-driven workshop scheduling network. The paper constructs a multi-process relational network model based on processes and machines in accordance with the complex network theory. Moreover, this paper proposes a multi-objective integrated model that minimizes maximum makespan, minimum machine load, and minimum machine energy consumption with the constraints of the workpiece, process, and machine. This paper uses the NSGA-II algorithm strategy to analyze the proposed model and issue. The feasibility of the model and algorithm proposed is demonstrated by solving various optimization objectives in actual workshop manufacturing tasks. Then, it analyses and evaluates the network structure characteristic index of the production and manufacturing scheduling process, which provides a decision basis for the task processing process design and scheduling planning in the intelligent manufacturing.

Life Cycle Assessment of Cold Spray Additive Manufacturing and Conventional Machining of Aluminum Alloy Flange

Cold spray additive manufacturing (CSAM) is generally used to repair worn components and build complex on-demand parts by depositing metal powder layer-wise using compressed air. Previous studies on CSAM were focused on printing parameters, materials properties, and printed part mechanical performance. However, the energy consumption and environmental impacts of CSAM processes have not yet been investigated, which are essential factors for sustainable manufacturing. This study aims to investigate the carbon footprint of the CSAM process and compare it with conventional machining processes and other additive manufacturing. The life cycle assessment methodology was followed to calculate the carbon footprint of a pipe flange, considering rod or tube as a feedstock. Results revealed that the machined flange from the tube had the lowest CO2-eq emissions of 31 kg CO2-eq due to low rough machining energy consumption and scrap production, compared to the machined flange from a rod and a printed flange from powder. Moreover, the life cycle carbon emissions increased by 8% and 19% in case of the printed and machined flanges, with uncertainties of 4% and 9%, respectively, when changing feedstock CO2 emissions. From a regional perspective, the CSAM process was responsible for the lowest CO2-eq emissions in Tasmania and South Australia.

Energy-saving and noise-reducing integrated task allocation model for machining systems and its application

Abstract In this paper, firstly, based on the application model of optimal scheduling of machining system for green manufacturing, two application models of the energy-saving scheduling model and energy-saving and noise-reducing scheduling model in a multi-model framework are combined, and the resource environment coefficient matrix of the two application models is established as well as the solution process is studied with the parallel machine problem. Then the system's architecture is constructed, and its basic operation flow, functional modules, etc., are designed and conceived. The application of the system is studied in conjunction with a gear machining workshop of a machine tool factory, and the machining system's energy and noise reduction performance is verified based on experiments. The results show that the energy consumption of the machining system is reduced by 0.514 kW-h by machining only the above six gear parts with a small difference in the maximum machining completion time and that the spindle speed has the most significant effect on the machine tool machining noise at a significance level α of 0.05. The analysis of this study verifies that the energy-saving and noise-reducing scheduling arrangement method can reduce the system machining energy consumption and noise, which is important for green manufacturing.

Effect of Feed Materials on Pellet Properties, Capacity and Energy Consumptions Values

In this study, it was aimed to determine the power consumption, work capacity and some properties of the pellet feed of the pelletizing machine used in the pelleting of animal feeds consisting of small and granular mixtures.
 A pellet machine with a die hole inlet diameter of 8 mm, an outlet diameter of 5 mm and a flat die with a diameter of 195 mm was used to pellet the feeds. In the experiments, two different feed materials consisting of mixed feed and barley only were used. The bulk weight of the pellets (kg m-3), durability (%), compression resistance (N mm-2), pellet moisture (%) were determined. In addition, hourly pelleting capacity (kg h -1), required energy consumption (kW), specific energy consumption (kW kg -1) of the pelletizing machine were determined.As a result of the trials, the capacity of the pellet feed making machine was 172.80 kg h-1 in the pelleting process from barley and 170 kg h-1 in the pelleting process from mixed feed. The required electrical energy consumption was 7.63 kW, in barley pelleting and 9.18 kW in mixed feed pelleting. Specific energy consumption 0.044 kW kg-1 in barley pelleting, 0.054 kW kg-1 in mixed feed pelleting

High performance computing with inter-virtual-machine communication

ABSTRACT Most applications use Transport Layer Security (TLS) as a default to encrypt network traffic to increase privacy and security. The adversary, however, also used LS encryption to conceal attacks or command-and-control communications. Encryption is essential for deciphering and assessing risks associated with communications. This issue should be resolved in a way that provides effective and covert decryption without compromising overall security. As a result of server consolidation, virtualization has emerged as a key technique for increasing data center energy efficiency. Consolidation of servers relies on the location of virtual machines. The placement of virtual machines has been approached from many different perspectives in recent years. Virtual machine placement approaches, however, only take into account the energy consumption of the physical machines within a data center, not the energy consumption of the communication network. Through the application domain, CVM offers a model-driven, user-centered approach to design, synthesis, and communication solutions by utilizing a fundamentally new method for handling these issues. This proposal discusses the concept, architecture, model languages, design prototypes, and implementation of CVM, arguing that it is a significantly more effective approach to technical communication solutions.

MINIMASI KONSUMSI ENERGI PADA PERMASALAHAN HYBRID FLOW SHOP SCHEDULING DENGAN ALGORITMA SALP SWARM

The scarcity and possible depletion of energy sources in the future is due to the high energy consumption in the manufacturing industry. One of the efforts to deal with this problem is scheduling. This article investigates the Hybrid Flow Shop Scheduling Problem (HFSSP) to minimize energy consumption. A metaheuristic algorithm procedure is proposed, inspired by the Salp swarm in the ocean, the Salp Swarm Algorithm. The SSA algorithm is proposed to determine the optimal sequencing considering minimal energy consumption. An interesting finding of this study is that the higher the population and iterations, the lower the energy consumption. In addition, the optimization results show that the total energy consumption is due to variations in setup and removal energy consumption and idle machine energy consumption. From the numerical results, the SSA algorithm can produce scheduling with minimal energy consumption compared to the heuristic procedure.