Search

Choosing the optimum cutting parameters is regarded as one of the important energy saving technologies. This paper presents a method for complex optimization of cutting parameters with the objectives of energy efficiency and processing time, which integrates Taguchi method, response surface method (RSM) and multi-objective particle swarm optimization algorithm (MOPSO). In this study, specific energy consumption (SEC) is selected to evaluate energy efficiency and the calculation model is introduced firstly. Then the method is described in detail. Taguchi is used to design the experiment, signal-to-noise (S/N) ratio is subsequently employed to analyze the performance of parameters on SEC and processing time, and the significant contributions of parameters can be determined by use of range analysis method. RSM is conducted to develop regression models for the responses based on the experimental data, and the optimal machining parameters for minimizing energy and time are determined through the modified MOPSO algorithm. Finally, four machining parameters schemes with different optimization objectives are compared, and the results show that a trade-off point can be drawn between the low processing time and high energy efficiency. [ABSTRACT FROM AUTHOR]

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time. [ABSTRACT FROM AUTHOR]

Dielectric medium (kerosene) between the tool electrode and workpiece forms a reaction product, owing to thermal effect of discharge in the process of electrical discharge machining (EDM), which is emitted from the surface in the form of harmful gas and may get into the operator's body by breath system inhalation and contact with skin. Therefore, exhaust emissions should be considered as important performance indicators for the green machining in the process of EDM. Moreover, energy efficiency and machining productivity should be improved and the cut process must take ecological constraints into consideration. Thereafter, the definition of green manufacturing helps to evaluate different and sometimes conflicting aspects of the manufacturing process, such as machining productivity, surface quality, energy efficiency and exhaust emissions. But it is difficult to dynamically adjust the weights of different aspects according to the actual processing conditions. This paper proposes a framework for optimization of cutting parameters to minimize energy consumption and exhaust emissions during electrical discharge machining of Al 6061 and SKD 11. Three cutting process parameters (peak current, pulse period, and duty cycle) are tested against material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV), and exhaust emissions characteristics (EEC) for EDM of these two materials. First, the process energy and exhaust emissions are analyzed. The results show that EEV is negatively correlated with surface quality (Ra) for both Al 6061 and SKD 11 material. Second, two data analyses (Taguchi and adaptive-network-based fuzzy inference system (ANFIS) analyses) are utilized to obtain high MRR and best Ra, EEV and EEC for the desired machining performances and environmental sustainability. It is found that the relative errors between the predicted values and experimental results of MRR, Ra, EEV and EEC are all not more than 20%, and found that the maximum of mean relative errors of them is 13.04%. Finally, a framework implemented by an optimization system, based on ANFIS, enhanced cuckoo search (ECS) algorithm and desirability function, is built to obtain the desired cutting parameters. It is found that high peak current (8.94A) and pulse period (500 μ s) are recommended to obtain multi-objective optimization for machining Al 6061, and that the optimal solutions of the EEV and EEC are significantly decreased, by 87.13% and 37.09%, respectively. Similarly, high peak current (8.77A) and pulse period (261.5 μ s) are recommended to obtain multi-objective optimization for machining SKD 11, and the optimal solutions of the EEV and EEC are significantly decreased, by 93.73% and 46.85%, respectively. Thereafter, it can be concluded from the above research that the proposed technique offers significant advantages and potential for applications in the green manufacturing field. Image 1 • Energy efficiency per volume was negatively correlated with Ra. • Maximizing energy efficiency and minimizing exhaust emissions by empirical models were obtained. • Balancing the machining performances and environmental sustainability was considered. • An optimization system with manual multi-objective weights was build. • Green regions of exhaust-friendly and energy-friendly were acquired. [ABSTRACT FROM AUTHOR]

Process route planning is vital for implementing energy saving and low-cost production in mechanical processing as it can directly affect the energy consumption and the cost of mechanical product processing. Therefore, a multiobjective optimization approach of machining center process routes to realize energy saving and low-cost mechanical processing is proposed in this paper. To provide theoretical support for this study, process route optimization problems of a machining center are analyzed, the concept of workstep element is introduced to represent the features of machined parts, and a multiobjective optimization model is established. The optimization model is solved based on the combination of a workstep chain intelligent generation algorithm and a non-dominated sorting genetic algorithm II. Finally, the emulsion pump case process route is used as a case study to verify the feasibility and practicability of the proposed method. Comparison with actual data shows that with the single objective of energy consumption and processing cost, based on the multiobjectives of energy saving and low cost as the optimization goal, the energy consumption was 1.018 × 107 J, and the processing cost was CNY32.65. Compared with the other two experimental results, the energy consumption and the processing cost demonstrated the best comprehensive performances, consistent with energy saving, low cost and sustainable production, thereby validating the established model. Furthermore, the optimization analysis shows that the combinatorial optimization algorithm has a better solution speed and optimization precision than the general genetic algorithm. • Analyzed the underlying effects of the mechanism of process route optimization. • A multiobjective optimization model of the machining center process route was built. • The concept of workstep element was proposed. • An intelligent combinatorial optimization algorithm for the workstep chain was proposed. [ABSTRACT FROM AUTHOR]

Presented in this paper is a methodology for evaluation of dry turning process along with optimal machining parameters, which allows a compromise between technological, economical, and ecological requirements. Dry longitudinal turning of Ni-based superalloy - Inconel 601 workpiece using three turning inserts coated with TiAlN + AlCr 2 O 3 by physical vapour deposition was investigated. The machining was performed at three different levels of cutting speed, feed, and depth of cut. During machining, power characteristics were measured in the form of current and interphase voltage. Machined parts and turning inserts were tested for surface roughness and tool life, respectively. Machining times, power and energy consumption were also calculated. Regression models were defined for arithmetical mean roughness, flank wear, and power. Negative environmental impact of the cutting process was monitored through midpoint and endpoint life cycle assessment (LCA). Finally, multi-objective optimization of output functions was performed. Optimization problem of minimizing the objective function was solved for eight output functions which characterize the process. The objective functions within this problem are: minimization of surface roughness, minimization of flank wear, minimization of cutting time, minimization of energy consumption, and minimization of four LCA indicators. The results indicate that dry turning of Ni-based superalloy - Inconel 601 requires higher turning inserts radii, higher cutting speeds, and smaller feeds and depths of cut. This allows acceptable flank wear of turning insert and required surface quality of workpiece, while drastically reducing cutting time, energy consumption and negative impact on the environment. The results also indicate that, within the set limitations and conditions of this experiment, increase of cutting speed can be a good strategy for reducing energy consumption and diminishing the impact of machining on the environment. • The longitudinal turning of an Inconel 601 in dry environment is presented. • Current, voltage, mean roughness, and flank wear are measured; while time, power and energy consumption are calculated. • Life cycle assessment is applied to quantify the negative environmental impacts. • Multi-objective optimization is applied to evaluate input variables of the process. • The results indicate that increasing cutting speed can be a good strategy for cleaner production. [ABSTRACT FROM AUTHOR]

Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modelled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved. [ABSTRACT FROM AUTHOR]