Improving FEM model of low immersion milling process using multi-objective optimization of tool elastic support dynamic properties

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

In this paper, the problems of fuel cost and emission reduction based on multi-objective optimization are studied and solved. Various multi-objective optimization algorithms, such as the multi-objective ant lion optimization (MOALO), the multi-objective multiverse optimization (MOMVO), the multi-objective particle swarm optimization (MOPSO), the multi-objective thermal exchange optimization (MOTEO), and the multi-objective grasshopper optimization algorithm (MOGOA), are used to reduce the cost and emissions of generation fuel. With equality and inequality restrictions, the suggested work is carried out in an IEEE 30 bus system. To determine the lowest system-wide minimization of generating cost and emission, the results of several optimization strategies are compared. In terms of lowering fuel costs and emissions, the MOGOA performs better than any other multi-objective algorithms.

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.

A simplified model for micro milling process is presented, as well as results on temperature on tool and work piece. The purpose is to investigate on finite element modelling of two flute micro end milling process of titanium alloy, Ti6Al4V with prediction of temperature distribution. ABAQUS/Explicit has been chosen as solver for the analysis. A thermo-mechanical analysis was performed. First model was created by selecting medium carbon steel, AISI1045, as workpiece material for model validation purpose. Second model was created by modifying the workpiece material from AISI1045 to Ti6Al4V. The model consists of two parts which are tungsten carbide micro tool and workpiece. Johnson-Cook law model has been applied as material constitutive properties for both materials due to its severe plastic deformation occur during machining. Prediction on forces was obtained during the analysis. Model validation was done by comparing results published by Woon et al. in 2008. The results showed a good agreement in cutting force. Once this was proved, the same model was then modified to simulate finite element analysis in micro milling of Ti6Al4V. Prediction of temperature distribution of micro end mill of Ti6Al4V was done in relation of different undeformed chip thickness. The findings showed that temperature increases as undeformed chip thickness increases. Temperature distribution of Ti6Al4V and AISI1045 under same machining conditions was compared. Results showed that the highest temperature was concentrated at tool edge for Ti6Al4V.

With the complexity of mechanical manufacturing system design increasing, the traditional single-objective optimization method can no longer meet the needs of multi-objective optimization, such as lightweight, high efficiency, low energy consumption and long life. Multi-objective optimization algorithm is widely used in mechanical design and manufacturing process planning because of its good convergence and diversity. Taking robot motion planning as an example, this paper analyzes the application methods of multi-objective optimization algorithm in mechanical structure design in detail, including problem description, multi-objective optimization model construction, algorithm application and performance analysis. Through the application of Genetic Algorithm (GA), this paper shows how the multi-objective optimization algorithm can balance multiple objectives such as path length, energy consumption and obstacle avoidance effect, and realize more intelligent and efficient solutions in robot motion planning. Performance analysis shows that GA is superior to single-objective optimization and random search algorithm in optimization effect, computational efficiency and stability. In addition, this paper also discusses the influence of the change of target weight and algorithm parameters on the optimization results, which provides valuable reference for future algorithm optimization and practical application. Multi-objective optimization algorithm shows obvious advantages in mechanical manufacturing system, which provides new ideas and methods for realizing more efficient, more environmentally friendly and more intelligent mechanical manufacturing design.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

This paper investigates the performance of self-developed polycrystalline diamond (PCD) micro end mills in ductile milling of fully sintered zirconia ceramic. Experiments are conducted on a precision milling machine center. The influence of cutting parameters, including axial depth of cut (ap), feed per tooth (fz), and the PCD particle size on the achievable surface quality is studied by experimental design. Further tests are also conducted to study the tool wear during a milling process. The tool wear characteristics are observed and tool wear mechanism is discussed. The results show that average surface roughness Ra below 70 nm can be achieved on the machined samples. The factor of feed per tooth affects Ra value the most. The maximum tool tip wear VC can be used to indicate the severity of tool wear. According to the results, the PCD micro end mill with bigger particle size possesses longer tool life. The mainly tool wear mechanism of the PCD micro end mill in ductile milling ceramics is the periodic peeling off of diamond particles during the interaction between the workpiece and end mill. Zirconia ceramics can deform in a plastic way and adhere to the cutting edge in the milling process. The adhesion and spalling of zirconia would induce the peeling off of PCD particles.

In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

Surface roughness is one of the main characteristics of a work piece in the quality control process. Several parameters such as cutting tool material and geometry, cutting parameters, work piece material properties, machine tool and coolant type affect the surface quality. An important task of process planners is the proper selection of three main cutting parameters: cutting speed, feed rate, and depth of cut in order to have not only low surface roughness, but also to perform the process within a reasonable amount of time. In this paper, using full factorial experiment design, the multiple regression equation for the surface roughness in the climb milling process of DIN 1.4021 martensitic stainless steel has been obtained and then used as one of the objective functions in the Multi-objective Improved Self- Adaptive Particle Swarm Optimization (MISAPSO) algorithm. This algorithm has been used to obtain cutting parameters to achieve low surface roughness simultaneously with a high material removal rate. The relatively new algorithm MISAPSO developed with some changes in the common particle swarm optimization (PSO) technique, has been used in multi-objective optimization of machining processes and was shown to be able to help the process planners in selecting cutting parameters.

Micromilling mechanism studies are the fundamentals for high-quality micro components fabrications. Based on the finite element method (FEM), the simulation model for micromilling of the oxygen-free copper (OFC) was established in this paper. The influences of the key process parameters (feed engagement fz, axial depth of cut ap, radial depth of cut ae, and spindle speed n) on the milling forces were investigated. According to the simulation results, to achieve the minimum undeformed chip thickness, the critical feed engagement was identified to be 2.5 μm/z. when the axial depth of cut increased, the milling force was increasing linearly, but at the same time the milling force took an oscillator decreases due to the increasing of spindle speed. The overall trend of milling force under different radial depths of cut is upward. Therefore, the feed engagement should be greater than 2.5μm/z for micro milling of oxygen-free copper. Small radial depth of cut, small axial depth of cut, and appropriate spindle speed should be selected to obtain a high machining quality. Introduction As an excellent metal with extremely low oxygen content, high conductivity and high corrosion resistance, oxygen-free copper C10100 is widely used in the manufacturing of audio-visual equipment and communication components. With good ductility and no hydrogen embrittlement, it can also be processed into various precision micro parts. Compared with traditional cutting, high-speed micromilling is more suitable for machining micro-small parts. In order to obtain higher dimensional accuracy and better surface quality, it is necessary to study the milling parameters of OFC in the processing of precision parts. In the actual machining process, the changes of milling force caused by the changes of milling parameters can reflect the quality of the final machined parts more intuitively. Therefore, the adjustment of milling parameters can be guided by measuring the milling force in the milling process. In order to reveal the difference in mechanism between traditional cutting and micromilling, scholars have conducted a lot of studies. In the study of cutting poly-crystalling OFC with single crystalline diamond (SOD) micro-tools, X. Ding has proposed that the cutting strategy of reducing cross-feed can improve the cutting performance with using micro-tools. Constant cutting forces, burr size and an improved roughness of the machined surface have been obtained by employing this cutting strategy [1]. Niu has presented an innovative cutting force modeling concept by taking account of micro-cutting dynamics. Experimental cutting trial is conducted to validate the modeling approach and the cutting force model [2]. Qu has studied the impact of MQCL method on the machining results and mechanical properties of the component [3]. Yuan has proposed an innovative uncut chip thickness algorithm and developed the model for determining the instantaneous uncut chip thickness in micro end-milling processes [4]. D.DAS has studied the effect of stress parameters on ratcheting deformation stages of polycrystalline OFHC copper [5]. For the micromilling of pure copper, M. Rahman has studied the failure mechanism and factors which affect the micro end mill [6]. Liu has studied the relationship between size effect and burr in micromilling OFC and constructed a geometric model of exit burrs [7]. Liu has established SPH simulation model and carried out systematic research on formation mechanism of chip [8].For oxygen-free copper materials, Tian has established a constitutive model and carried out simulation analysis based on Abaqus [9]. Cheng has proposed 211

Size limitations and wear behavior of TiB2 coated micro end mills (Ø < 50 µm) when machining cp-titanium

This article formulates the daily economic/environmental hydrothermal scheduling problem as a multi-objective optimization problem. By introducing non-dominated sorting and crowding distance, the multi-objective artificial physical optimization algorithm is proposed to solve the daily economic/environmental hydrothermal scheduling problem. To enhance the performance of the proposed algorithm, new velocity update equation, which takes advantage of the individual memory and population information, is utilized. To overcome the drawback of premature convergence, a chaotic mutation is adopted in the multi-objective artificial physical optimization algorithm. Especially for handling the equality constraints of daily economic/environmental hydrothermal scheduling, novel heuristic strategies are developed to repair the infeasible solutions. To demonstrate the effectiveness of the multi-objective artificial physical optimization algorithm for solving daily economic/environmental hydrothermal scheduling, the proposed method is implemented on a hydrothermal system and the numerical results are compared with several optimization approaches. It demonstrates that the proposed multi-objective artificial physical optimization algorithm is competent as an alternative for the daily economic/environmental hydrothermal scheduling problem.

In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.

Industries are forced to seriously evaluate their design and manufacturing of their products due to the competition in the marketplace. Product characteristics such as tolerance specification, appearance, and service life now become a major concern for machined components. One of the most important aspects in machined components is surface integrity. It involves mainly the surface roughness and micro hardness changes during machining operation, which should be controlled and monitored to fulfill the product functions and customer needs. This paper presents the comparison effect of the end milling and EDM parameters on the surface integrity of AISI H13 tool steel (HRC50 3). The parameters studied were the cutting speed (224 m/min – 280 m/min), feed rate (0.25 mm/tooth) and depth of cut (0.3 mm-0.8 mm for end milling process. Whereas for EDM, the parameters studied were the peak current (1 and 4 A), pulse ON-time (6 and 12 μs), and pulse OFF-time (2 μs). The electrodes used were graphite and copper. In this study, the workpiece surface and recast layer were examined using an optical microscope. The observation revealed that both processes of end milling and EDM had cause the formation of three layers structure, i.e. white, martensite quenched and bulk material layers. The subsurface alteration for EDM process is considered rigorous as compared with the end milling process. Damages beneath the machined surface such as micro cracks and void were observed for EDM process, and microscopic pitting and surface roughning for end milling process. The measurement of the microhardness beneath the machined surface of AISI H13 was carried out using Vickers microhardness tester to characterize its mechanical properties. It was found that the highest hardness of 1010 Hv was in the white layer, and hardness of martensite quench layer was 423 Hv, which was lower than the bulk material layer of 430 Hv in EDM process. Whereas, a maximum of 550 Hv was measured directly underneath the generated surface, i.e. 30% more as compared to the hardness of the basic material in the end milling process.