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In this study, a method combining magnetic, MIG, and laser was applied to weld the 304 stainless steel with a thickness of 4 mm. The effect of magnetic field on the weld microstructures and mechanical properties was investigated. The weld geometry and microstructure were characterized by optical microscope (OM) and scanning electric microscopy. Electron back scattered diffraction (EBSD) was used to determine the grain sizes and crystallographic orientations. Residual stress and tensile stress of welds were measured and compared with the laser-arc hybrid welds without an external magnetic field. The results showed that with an appropriate magnetic field intensity, an optimal joint was obtained with tensile strength enhanced by nearly 12% and tensile residual stresses reduced. In addition, the grain refining and promotion of the phase transformation with the magnetic field were analyzed. [ABSTRACT FROM AUTHOR]

Laser-MIG hybrid welding of 316L stainless steel was carried out to investigate influence of welding process parameters, i.e., laser power ( P), welding current ( I), distance between laser and arc ( D), and welding speed ( V) on arc characteristics, droplet transfer, and weld quality. Arc characteristics and droplet transfer behavior were investigated using high-speed imaging technology. Microstructures of the joint were observed by optical microscope, and microhardness was measured by microhardness tester. The results showed that the maximum deviation angle of the arc from the wire axis decreases with the increasing laser power. Welding current played a dominant role in arc length and droplet transfer mode. Optimal weld with no spatters and humps was obtained when P = 3 kW, I = 140 A, D = 3 mm, and V = 1.2 m/min. Microstructures in the weld zone mainly consist of columnar and equiaxed dendrites. Microhardness decreased toward the center of the weld zone, increased toward the WZ boundary, and decreased sharply at the heat-affected zone (HAZ). [ABSTRACT FROM AUTHOR]

Laser arc hybrid welding became a useful method to weld thick plates. But a sound weld is not so easy to obtain because of the uncontrollability of droplet transfer state, especially in ultra-narrow gap welding process. In this study, V-like ultra-narrow gap welds were carried out using 80 % Ar + 20 % CO blend as shielding gas. A high-speed video system was used to observe the droplet transfer behaviors. The results showed that the transfer mode did not change with the same welding current. The transfer mode changed from the short circuiting transfer to the mixture of the short circuiting and the globular transfer and then to spray transfer as the welding current increased. The relationship between the droplet transfer process and the welded defects was studied to obtain high-quality weld joint with a stable transfer process. The optimal parameter window between welding current and groove size was found. Only when the welding current was 200 to 270 A and groove gap was 2 to 3 mm, can a good welded joint be got. Finally, 30-mm-thick plate with 5.49-mm maximum groove gap was welded well by one pass laser autogenous welding and seven passes laser arc hybrid welding process. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA), as an assessment tool for environmental performance, is used widely in decision making of product design. However, apparently, it is not sufficient to make final decision only depending on environmental assessment results for profit-directed organizations. One of the main purposes of this paper is to propose a methodology for integration of LCA and economic analysis tool-life cycle costing (LCC) in order to get the integrated evaluation results. A framework of integration of LCA and LCC has been introduced, which is contained by four components: the definition of unity time and physical boundaries, integration of inventory analysis, integration of impact assessment, environmental and economic interpretation. However, the integrated evaluation results always indicate that the relationship between economic and environmental performance is not in balance. Optimization is as important as integration to eco-balance. An optimization method is proposed to improve the initial product design with maximizing the integrated environmental and economic benefit for mechanical product. In optimization step, product system is divided two parts with environmental subsystem and economic subsystem. The integrated evaluation results are adopted to support for the optimization. Some mathematic optimization models are set up for environmental and economic subsystems. Multidisciplinary design optimization is applied to optimize these models so that the initial mechanical product design can achieve the best environmental and economic performance result. A case study of type 4135G diesel engine is presented to validate the rationality and feasibility. [ABSTRACT FROM AUTHOR]

Currently, sustainable manufacturing (SM) attracts more and more attentions due to the increasing environmental pollution and energy shortage threat. Energy consumption is a fundamental element of SM for its valuable effect in the environmental impacts and business opportunities. Analyzing the relationship between process parameters and energy consumption is helpful to reduce production costs, eliminate negative environmental impacts, and increase business opportunities. Since energy consumption is impacted by the inherent uncertainties in the machining process, how to model energy consumption presents a significant challenge. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm and Genetic Programming, and has been successfully applied in formula finding. In this paper, a Greedy Randomized Adaptive Search Procedure (GRASP)-based Gene Expression Programming, named GGEP, is proposed to predict the face milling energy consumption. In this proposed GGEP approach, a GRASP-based learning mechanism and an iterative re-start mechanism have been introduced into the basic GEP. At the basis of defining a GGEP environment for the energy consumption prediction, an explicit model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The analysis of experiment results reveals that the proposed approach models and predicts the energy consumption with high accuracy and high-speed convergence. Moreover, in order to better study the mechanism of machining, the influence and contribution of different impact factors on energy consumption in face milling are analyzed. [ABSTRACT FROM AUTHOR]

Along with the increasing attention on the environment and resources, green design and green manufacturing has become a tendency nowadays. In order to mitigate the computational burden when solving green design problem of the complex engineering system, a variable fidelity metamodel-based analytical target cascading framework (VFATC) is developed to explore the application of variable fidelity (VF) metamodels in analytical target cascading (ATC). In this VFATC framework, a VF approximation modeling method is developed, which employs both computationally efficient simplified (low fidelity, LF) and expensively detailed (high fidelity, HF) information. To efficiently integrate the LF and HF information, kriging is enhanced to scale LF model (LFM) to HF model (HFM). Then, the scaled LFM is used as an approximation model to replace the analysis models in VFATC. The effectiveness of VFATC framework is applied into a lightweight design problem of cantilever beams and a ship design problem with the objective of oil consumption reduction. Overall results show the VFATC methods introduced in this paper provide an effective way of improving computational efficiency of multidisciplinary green design optimization problems based on complex HF simulation models. [ABSTRACT FROM AUTHOR]

Laser brazing (LB) provides a promising way to join the galvanized steels in automotive industry. The process parameters of LB have significant effects on the bead profile and hence the quality of joint. Since the relationships between the process parameters and bead profiles cannot be expressed explicitly, it is impractical to determine the optimal process parameters intuitively. This paper proposes an optimization methodology by combining Kriging surrogate and particle swarm optimization (PSO) to address the process parameters optimization of the bead profiles in LB with crimping butt of 0.8-mm-thick galvanized steel. Firstly, an experiment using Taguchi L orthogonal array is conducted where welding speed (WS), wire speed rate (WF), and gap (GAP) are taken into consideration as the input parameters, while the bead profiles are the output responses. Secondly, the relationships between the inputs and outputs are established using the Kriging model. Thirdly, the effects of the input parameters on the bead profiles are analyzed, and the global process parameters are obtained by the presented Kriging-PSO approach. At last, the verification experiments were conducted to verify the effectiveness of the optimal values. On the whole, the proposed hybrid method, Kriging-PSO, shows great promise for improving the effectiveness and stability of LB welding process. [ABSTRACT FROM AUTHOR]

The angular distortion is a typical out-of-plane distortion, and it has negative effects on the performance of welded structures. However, the researches on angular distortion of welded structures using intelligent algorithm and mathematical analysis are comparatively less. In this article, the influence from the bead profile on the angular distortion is analyzed, and the angular distortion of tungsten inert gas arc welding with no gap would be predicted by back propagation neural network and the novel inherent strain considering the actual bead geometry model, and the prediction accuracy is also evaluated through comparative analysis of the results with each other. The average relative error of the model built using inherent strain considering the actual bead geometry model method is only 1.289 % and better than that predicted by back propagation neural network algorithm (4.772 %). Meanwhile, the inherent strain considering the actual bead geometry model prediction stability that is estimated using standard deviation is also clearly better than that of back propagation neural network. Actually, the prediction methods by back propagation neural network algorithm and inherent strain considering the actual bead geometry model are proved to be correct and stable. Therefore, the proposed methods can be used to predict the angular distortion of the welded structures with no gap butt joint and guide the actual welding process to decrease the welding deformation. [ABSTRACT FROM AUTHOR]

Laser brazing process with crimping butt is sensitive to welding parameters, and it is difficult to acquire a good quality of welding joint. To achieve good welding parameters (welding speed, wire feed rate, gap), this paper addresses the multi-objective optimization of bead profile, namely sum of left side and right side of bead geometry and subtraction between top width of bead and bottom width of bead profile. Back propagation neural network was used to predict goals with average error of 9.95 and 8.54 %; non-dominated sorting genetic algorithm was adopted to acquire a Pareto set, and verification experiments demonstrated that relative errors were controlled within 3.97 %. Meanwhile, the importance from welding parameters on goals was ranked by signal-noise ratio and interactions between each parameter. Therefore, a novel multi-objective optimization method was proved to be feasible and would be useful to guide the actual welding process of laser brazing. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two crucial components in a manufacturing system. The integration of the two functions has an important significance on improving the performance of the manufacturing system. However, integrated process planning and scheduling is an intractable non-deterministic polynomial-time (NP)-hard problem, and the multiple objectives requirement widely exists in real-world production situations. In this paper, a multi-objective mathematical model of integrated process planning and scheduling is set up with three different objectives: the overall finishing time (makespan), the maximum machine workload (MMW), and the total workload of machines (TWM). A multi-objective memetic algorithm (MOMA) is proposed to solve this problem. In MOMA, all the possible schedules are improved by a problem-specific multi-objective local search method, which combines a variable neighborhood search (VNS) procedure and an effective objective-specific intensification search method. Moreover, we adopt the TOPSIS method to select a satisfactory schedule scheme from the optimal Pareto front. The proposed MOMA is tested on typical benchmark instances and the experimental results are compared with those obtained by the well-known NSGA-II. Computational results show that MOMA is a promising and very effective method for the multi-objective IPPS problem. [ABSTRACT FROM AUTHOR]

In the present paper, the application of laser cleaning technology in thick plate ultra-narrow gap multi-pass multi-layer laser non-autogenous welding process was described. The comparison between weld cross sections and the amount of oxide on the weld surface before and after welding tested by environmental scanning electron microscopy (ESEM) proved the laser cleaning method does play an important role in cleaning the weld. The influence rule of some cleaning parameters to welded joints, including groove angle, laser power, defocusing amount, and cleaning speed, was studied, and the welding defects decreased obviously with the optimal cleaning parameters. Combining this cleaning technology and the laser non-autogenous welding system comprised of the laser autogenous welding, laser wire filling welding, and the laser arc hybrid welding, a 40-mm-thick plate was welded well using a 4-kW fiber laser. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two of the most important functions of a manufacturing system. Traditionally, these two functions are executed separately. Since they are interrelated, conducting process planning and scheduling simultaneously will provide more advantages. In this paper, according to the characteristics of the integrated process planning and scheduling (IPPS) problem, a hybrid honey bee mating optimization (HBMO) algorithm, which combines the HBMO algorithm and variable neighborhood search (VNS), is proposed to settle the problem with makespan criterion. Different with conventional HBMO, we utilize VNS with two effective and efficient neighborhood structures in the algorithm to simulate the workers' brood caring action to avoid premature convergence and to find more excellent broods. In addition, a novel individual initialization method is developed in the algorithm. The proposed algorithm is tested on typical benchmark instances taken from related literature, and the computational results are compared with those of other algorithms. Experimental results show the effectiveness and efficiency of the hybrid HBMO algorithm. New upper bounds have been captured for 16 instances, and most instances have been improved within reasonable CPU times. [ABSTRACT FROM AUTHOR]

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In order to avoid the costly trial-and-error process in machining parameters determination, the Gaussian process regression (GPR) was proposed for modeling and predicting the surface roughness in end face milling. Cutting experiments on C45E4 steel were conducted and the results were used for training and verifying the GPR model. Three parameters, spindle speed, feed rate, and depth of cut were considered; the experiment results showed that depth of cut is the main factor affecting the surface roughness and regression results showed that the GPR model has a good precision in predicting the surface roughness in different cutting conditions. The prediction accuracy was nearly about 84.3 %. Based on the GPR prediction model, 3D-maps of surface roughness under various cutting parameters could be obtained. It is very concise and useful to select the appropriate cutting parameters according to the maps. As experimental results did not conform to the empirical knowledge, frequency spectrums of the tool were analyzed according to the 3D-maps, it was found that tool vibration is also a crucial factor affecting the machined surface quality. [ABSTRACT FROM AUTHOR]

This paper describes a study on laser butt welding of 4 and 2 mm SUS301L stainless steel and a detailed analysis of welding joints. The gap tolerance of butt joint was also studied with optimized process parameters. The electrolytic etching in 10 % oxalate solution was used to test the intergranular corrosion of the 4 mm SUS301L welded joint. Fatigue property of the 2 mm SUS301L welded joint was tested under the conditional cycle times of 1 × 10. Using optical microscopy, the changes of metallurgical microstructure in the weld zone of 4 mm SUS301L were also studied. It has been found that laser butt welding of 4 mm SUS301L is able to achieve sound metallurgical morphology and high strength weld joint when the butt gap is within certain tolerance. The weld joint also has good resistance to intergranular corrosion and has a fatigue limit of 310 MPa. [ABSTRACT FROM AUTHOR]

Optimization for the surface grinding process is a problem with high complexity and nonlinearity. Hence, evolutionary algorithms are needed to apply to get the optimum solution of the problem instead of the traditional optimization algorithms. In this work, a hybrid particle swarm optimization (HPSO) algorithm which combines the dynamic neighborhood particle swarm optimization (DN-PSO) algorithm with the strategy of mutation considering constraints is presented to handle multi-objective optimization for surface grinding process. Such four process parameters as wheel speed, workpiece speed, depth of dressing, and lead of dressing are considered as the variables for optimization, and the following three objectives such as production cost, production rate, and surface roughness are used in a multi-objective function model with a weighted approach. Meanwhile, the constraints of thermal damage, wheel wear, and machine tool stiffness are considered. Computational experiments are conducted on cases of both rough grinding and finish grinding, and comparison results with the previously published results obtained by using other optimization techniques shows the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the medium-speed wire electrical discharge machining (MS-WEDM) process, the extremely high temperature and massive electrical discharges in a fraction of 1 s result in the poor surface quality such as high tensile residual stresses, high surface roughness, white layers, and micro cracks. In this paper, an experimental plan for central composite design (CCD) of processing the tool steel (SKD11) has been conducted according to response surface methodology (RSM). The aim is to develop the mathematical model that can correlate the main process parameters of MS-WEDM with machining performance and to seek the optimal parameters on material removal rate (MRR) and 3D surface quality (Sq) by integrated RSM and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Then, a set of pareto-optimal solutions is obtained. Moreover, from the confirmation experiment, it has been proved that the optimal process-parameter combinations are suitable on the MRR and the 3D surface texture. Eventually, it has also demonstrated that the method of integrated RSM and NSGA-II is an effective way for multi-objective optimization. [ABSTRACT FROM AUTHOR]

The core loss of the grain-oriented silicon steel influences the energy loss of transformer. Pulse energy, point spacing, and scribing spacing are the three key parameters of laser scribing process, which determine the core loss of silicon steel. In this study, the relationship between the three key parameters and core loss reduction was investigated and analyzed. Experiments of laser scribing on 30Q130 silicon steel with different parameter combinations were implemented. The results showed that there was no obvious silicon coating burns in the surface of the steel after laser scribing and that the core loss reduction rate (reduction rate) significantly increased with the increasing laser energy density. However, when the energy density exceeded a certain range, the reduction rate decreased and gradually tended to a stable level. Hence, the parameters had significant influence on the reduction rate of core loss, and the two-factor interaction effects of reduction rate of core loss were prominent when parameters of high pulse energy, small point spacing, and small scribing spacing were selected. The optimization combination values of the three key parameters of laser scribing process on 30Q130 silicon steel was acquired by genetic algorithm-enhanced artificial neural network algorithm. For achieving high reduction of core loss, the optimized combination of pulse energy of 2.52 mJ, small point spacing of 0.22 mm, and small scribing spacing of 3.03 mm were selected, and the reduction rate of 13.12 % was verified through experiment. [ABSTRACT FROM AUTHOR]

Flexible job-shop problem has been widely addressed in literature. Due to its complexity, it is still under consideration for research. This paper addresses flexible job-shop scheduling problem (FJSP) with three objectives to be minimized simultaneously: makespan, maximal machine workload, and total workload. Due to the discrete nature of the FJSP problem, conventional particle swarm optimization (PSO) fails to address this problem and therefore, a variant of PSO for discrete problems is presented. A hybrid discrete particle swarm optimization (DPSO) and simulated annealing (SA) algorithm is proposed to identify an approximation of the Pareto front for FJSP. In the proposed hybrid algorithm, DPSO is significant for global search and SA is used for local search. Furthermore, Pareto ranking and crowding distance method are incorporated to identify the fitness of particles in the proposed algorithm. The displacement of particles is redefined and a new strategy is presented to retain all non-dominated solutions during iterations. In the presented algorithm, pbest of particles are used to store the fixed number of non-dominated solutions instead of using an external archive. Experiments are performed to identify the performance of the proposed algorithm compared to some famous algorithms in literature. Two benchmark sets are presented to study the efficiency of the proposed algorithm. Computational results indicate that the proposed algorithm is significant in terms of the number and quality of non-dominated solutions compared to other algorithms in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]