Multi-objective Process Parameters Research Articles

A Comparative Analysis of Single and Multi-objective Process Parameter Optimization of Dissimilar Metal Friction Welds

A Comparative Analysis of Single and Multi-objective Process Parameter Optimization of Dissimilar Metal Friction Welds

Optimizing Time-Constrained Multi-Objective Process Parameters for Thin-Walled Maraging Steels Manufactured by Laser Powder Bed Fusion (LPBF)

Additive manufacturing is an innovative manufacturing process that enables complex topological structures and low-volume, high-variety production. One of the major adaptations of this method is in the tire industry. Thin-walled sipes slit the tires to improve drainage and traction. The material properties of thin-walled structures manufactured by additive manufacturing are different and more sensitive than those of conventional cube-shaped specimens. Thin-walled maraging steel specimens are considered to be able to model the relationship between the process parameters and the properties of the sipes adequately. Tire sipes are made of maraging steel. Maraging steels are a class of low-carbon high-alloy martensitic steel generally providing high strength, ductility, and good fracture toughness. In particular, these alloys exhibit a good combination of strength and toughness at elevated temperatures, which has been desirable for applications in aerospace and tooling. In order to consider productivity, multi-objective process parameter optimization with a build-time-constrained model is proposed.

Multi-Objective Process Parameter Optimization of Ultrasonic Rolling Combining Machine Learning and Non-Dominated Sorting Genetic Algorithm-II.

Ultrasonic rolling is an effective technique for enhancing surface integrity, and surface integrity is closely related to fatigue performance. The process parameters of ultrasonic rolling critically affect the improvement of surface integrity. This study proposes an optimization method for process parameters by combining machine learning (ML) with the NSGA-II. Five ML models were trained to establish relationships between process parameters and surface residual stress, hardness, and surface roughness by incorporating feature augmentation and physical information. The best-performing model was selected and integrated with NSGA-II for multi-objective optimization. Ultrasonic rolling tests based on a uniform design were performed, and a dataset was established. The objective was to maximize surface residual stress and hardness while minimizing surface roughness. For test specimens with an initial surface roughness of 0.54 µm, the optimized process parameters were a static pressure of 900 N, a spindle speed of 75 rpm, a feed rate of 0.19 mm/r, and rolling once. Using optimized parameters, the surface residual stress reached -920.60 MPa, surface hardness achieved 958.23 HV, surface roughness reduced to 0.32 µm, and contact fatigue life extended to 3.02 × 107 cycles, representing a 52.5% improvement compared to untreated specimens and an even more significant improvement over without parameter optimization.

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding.

The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F1-score and R2 was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.

Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning

In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs.

Applying Evolutionary Multitasking for Process Parameter Optimization in Polymerization Process of Carbon Fiber Production

Carbon fiber is becoming a key material for engineering applications due to its excellent comprehensive properties. The process parameter optimization is an important step in the polymerization process of carbon fiber production. At present, most of the research on process parameter optimization is usually carried out on a single production line, without considering the correlation between optimization problems. In this paper, a multiobjective mechanism model for the co-optimization of the polymerization process of carbon fiber production is established. Each of these submodels is a multiobjective process parameter optimization task, corresponding to the polymerization process of a production line. In order to solve the model effectively, we also designed an evolutionary multitasking algorithm based on transfer learning, which reuses the past experiences of one task to generate a population pool for the next iteration of another task, enabling explicit genetic transfer between different tasks and accelerating the population convergence speed. The proposed multitasking framework for operation optimization has been conducted on 10 different production conditions of the polymerization process. Experimental results show that compared with other implicit and explicit genetic algorithms, this algorithm is very competitive in generating effective solutions. This research provides important support for process parameter optimization and manufacturing of carbon fiber production, which will help engineers and technicians to make informed decisions.

Multi-objective process parameters optimization in rapid heat cycle molding incorporating variable packing pressure profile for improving weldline, clamping force, and cycle time

Multi-objective process parameters optimization in rapid heat cycle molding incorporating variable packing pressure profile for improving weldline, clamping force, and cycle time

Laser additive remanufacturing parameters optimization and experimental study of heavy-duty sprocket

Experimental research on laser additive remanufacturing technology of heavy-duty sprocket was carried out. The influences of laser power, scanning speed, and powder feeding rate on cladding height, cladding area, melting area, and dilution rate were compared and analyzed. The prediction models of the combination of process parameters with the geometric characteristics of cladding layer and dilution were established. A multi-objective process parameter optimization model with the maximum cladding height and cladding area, the minimum melting area, and dilution rate as objective functions was established, and the model was optimized and solved based on MOPSO algorithm. The laser additive remanufacturing repairing experiment of damaged sprocket was carried out by using the optimal parameter combination, and the microstructure and mechanical properties of the repaired region were analyzed. The results show that the scanning speed and powder feeding rate are the main factors influencing the geometric characteristics and dilution of the cladding area, and the models have good prediction accuracy. The optimal process parameters (1150 W, 950 mm/min, 3.8 rad/min) obtained by MOPSO algorithm are adopted to repair the damaged sprocket. The repaired area without cracks and pores and the cladding layer show good metallurgical bonding with the substrate, and the microhardness is twice that of the substrate. The experimental results prove that the laser additive remanufacturing technology is feasible to repair the damaged heavy-duty sprocket and has a strong engineering application prospect.

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

Research on Recovery Mechanism and Process of Waste Thermosetting Phenolic Resins Based on Mechanochemical Method

The present study analyzed the recovery mechanism of the thermosetting phenolic resin waste, optimizing the recovery process parameters, based on the mechanochemical recovery method. The physical and chemical structure of the ultrapulverized phenolic resin powder was characterized by the low‐field NMR (nuclear magnetic resonance), IR (infrared spectroscopy), and SEM (scanning electron microscopy). The recovery process parameters were designed by using the response surface analysis method, and the multiple quadratic regression model was established and the multiobjective process parameter was optimized. The results show that the body network structure of the material is destroyed during the crushing process. The intensity of the internal cross‐linking signal is reduced and tends to be stable at about 60%, when the particle size exceeds 120 mesh. The cross‐linking bonds methylene (‐CH2‐) in the main chain of the molecular chain and the C‐O bond in the methylol group on the branch are broken. The tensile strength of the recycled sheet made of the phenolic resin powder is 8.27 MPa, and the bending strength is 17.76 Mpa through the mechanical properties test. The influences on the change rate of the cross‐linking signal intensity and the yield of the recycled powder are in the following order: rotation speed, feed size, time, and feed volume. The best recovery parameters are as follows: rotation speed of 2820 r/min, time of 80 min, feed particle diameter of 0.43 mm, and feed volume of 60 g.

Parametric Analysis of WEDM to Optimize Cutting Parameters for Inconel 800

Abstract The intent of the current research is to investigate the effect on cutting speed, average surface roughness, total surface roughness, kerf width and wire consumption for Inconel 800 by varying peak current (IP), pulse on time (Ton) and pulse off time (Toff) of wire electrical discharge machining. It has been observed from analysis of variance that Ton has highest impact on all the responses followed by Toff and IP. The mathematical models established are found reliable representatives of the experimental results with prediction errors approximately ±5 %. Grey relational analysis has been used to optimize the multi-objective process parameters. The optimal performance has been obtained for Ton 105 (0.35µ s), Toff 35 (11.5 µ s) and peak current 200 A

Multi-Objective Process Parameter Optimization for Surface Roughness and Material Removal Rate in Face Milling of Al 6061 T6 Alloy

This study uses Taguchi methodology and Gray Relational Analysis approach to explore the optimization of face milling process parameters for Al 6061 T6 alloy.Surface Roughness (Ra), Material Removal Rate (MRR) has been identified as the objective of performance and productivity.The tests were performed by selecting cutting speed (mm / min), feed rate (mm / rev) and cutting depth (mm) at three settings on the basis of Taguchi's L9 orthogonal series.The grey relational approach was being used to establish a multiobjective relationship between both the parameters of machining and the characteristics of results. To find the optimum values of parameters in the milling operation, the response list and plots are used and found to be Vc2-f1-d3. To order to justify the optimum results, the confirmation tests are performed.The machining process parameters for milling were thus optimized in this research to achieve the combined goals such as low surface roughness and high material removal rate on Aluminum 6061 t6.It was concluded that depth of cut is the most influencing parameter followed by feed rate and cutting velocity.

A hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of the 3D additive screen printing of athletic shoes

This paper presents a hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of 3D additive screen printing of athletic shoes. 3D additive screen printing is mainly used in the high-end athletic shoes and clothes field. It requires overlapping and overprinting dozens of times to make the printed patterns stereoscopic. The process of 3D additive screen printing is complex and variable and the production cycle is long. Because of the variability of the screen printing process and the coupling between process parameters, there is no simple method to guide the trial production of new products and obtain the optimal process parameters of screen printing. Trial-and-error is often used but it expends a lot of manpower, materials, and financial resources. To solve the optimization problem, a Taguchi experiment based on fuzzy comprehensive evaluation with five factors and two responses was first designed. Then, a back-propagation network (BPN), least-squares support-vector machine (LSSVM), and random forest (RF) were trained with experimental data to obtain a forecasting model for the process parameters. On comparison, the RF forecasting model performed best in this case. Then, the multi-objective antlion optimizer (MOALO), which is a new multi-objective optimization algorithm with excellent performance, was improved to the IMOALO, and it was proved that IMOALO has a better performance than MOALO. Combining the RF forecasting model with IMOALO, and carrying out the optimization, the optimal process parameters were obtained. Actual printing production shows that the proposed hybrid intelligence technique improves the production efficiency and first pass yield of printed products.

Cutting stability and optimization of process parameters for the large-pitch screw high-feed turning

In this study, the machining characteristics of the large-pitch screw in high-feed turning are initially analyzed, and a dynamic model for the screw subjected to moving forces is proposed to study the chatter, which has been taken into consideration “quasi-regeneration effect” of the machined surface and the actual machining path. Then, a stability model of the lobes between the amount of feed and spindle speed in the turning screw finishing stage is achieved, taking into consideration deformation of the workpiece and the tool, on the basis of the vibration equation of the workpiece and the dynamic equation of the tool, and is experimentally verified as well. The dynamics model, and stability domain provide a basis upon which multiobjective process parameter optimization method including the amount of feed and spindle speed can be put forward during high-feed turning process. Experiments are likewise performed and the results show that no chatter occurs, verifying the optimization of machining process parameters for large-pitch turning. Meanwhile, it should be noted that the surface quality and shape accuracy of the machined surface are equivalent to the high-feed turning process.

Multi-objective process parameters optimization of hot-wire laser welding using ensemble of metamodels and NSGA-II

Abstract Hot-wire laser welding (HLW) can reduce the dispersion of laser energy and improve deposition efficiency by preheating filler wire during laser welding process. In order to obtain sound welding results, it is crucial to select appropriate process parameters. In this study, an optimization methodology based on ensemble metamodels (EM) which take the advantages of the prediction ability of stand-alone metamodels (Kriging, RBF and SVR), and Non-dominated sorting genetic algorithm (NSGA-II) are presented to obtain optimum process parameters during stainless steel 316L hot-wire laser welding. Firstly, EMs are developed through minimizing the generalized mean square Leave-one-out (LOO) errors to find the optimum weight factors of the used stand-alone Kriging, RBF and SVR metamodels. And then the EMs are applied to establish the relationships between process parameters (i,e., laser power (LP), welding speed (WS) and hot-wire current (I)) and welding results (i,e., welding depth-to-width ratio (DW), welding reinforcement (BR) and tensile strength (TS)). During optimization process, NSGA-II is employed to search for multi-objective Pareto optimal solutions based on EMs. In addition, the main effects of multiple process parameters on welding results are analyzed. The verification tests indicate that the obtained optimal process parameters are effective and reliable for producing expected welding results (maximized DW, maximized TS and desired BR value). In general, the proposed optimization method can provide a reliable guidance for HLW in engineering practice.

Multi-objective process parameters optimization of Laser-magnetic hybrid welding combining Kriging and NSGA-II

Abstract Laser-magnetic hybrid welding (LMW), which utilizes the stirring effect of magnetic field on molten pool to restrain the welding defect and increasing welding penetration, provides a promising way to improve the quality of welding joint. The process parameters of LMW have crucial effects on the welding seam profile which related to the quality of welding joint. This paper presents an integrated methodology by combining Kriging metamodel and Non-dominated sorting genetic algorithm-II (NSGA-II) for process parameters optimization of LMW. Firstly, a three-factor, five-level experiment using Taguchi L25 orthogonal array is conducted considering magnetic flux density (MF), laser power (LP) and welding speed (WS). Secondly, Kriging metamodel is introduced to establish the relationships between LMW process parameters and welding seam profile. A set of actual tests was carried out to verify the prediction accuracy of the constructed Kriging metamodels. Thirdly, NSGA-II is employed to finish multi-objective process parameters optimization and Pareto optimal solutions searching. Finally, the obtained optimal process parameters are validated by macro-weld profile, microstructure and micro-hardness in the confirmation tests. Results illustrate that the proposed integrated methodology is helpful for reducing welding defects and obtaining high-quality joints for LMW in practical production.

Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade

Summary Close tolerance and good surface finish are achieved by means of grinding process. This study was carried out for multi-objective optimization of MQL grinding process parameters. Water based Al 2 O 3 and CuO nanofluids of various concentrations are used as lubricant for MQL system. Grinding experiments were carried out on instrumented surface grinding machine. For experimentation purpose Taguchi's method was used. Important process parameters that affect the G ratio and surface finish in MQL grinding are depth of cut, type of lubricant, feed rate, grinding wheel speed, coolant flow rate, and nanoparticle size. Grinding performance was calculated by the measurement G ratio and surface finish. For improvement of grinding process a multi-objective process parameter optimization is performed by use of Taguchi based grey relational analysis. To identify most significant factor of process analysis of variance (ANOVA) has been used.

Optimization of Process Parameters of Hybrid Laser–Arc Welding onto 316L Using Ensemble of Metamodels

Hybrid laser–arc welding (LAW) provides an effective way to overcome problems commonly encountered during either laser or arc welding such as brittle phase formation, cracking, and porosity. The process parameters of LAW have significant effects on the bead profile and hence the quality of joint. This paper proposes an optimization methodology by combining non-dominated sorting genetic algorithm (NSGA-II) and ensemble of metamodels (EMs) to address multi-objective process parameter optimization in LAW onto 316L. Firstly, Taguchi experimental design is adopted to generate the experimental samples. Secondly, the relationships between process parameters (i.e., laser power (P), welding current (A), distance between laser and arc (D), and welding speed (V)) and the bead geometries are fitted using EMs. The comparative results show that the EMs can take advantage of the prediction ability of each stand-alone metamodel and thus decrease the risk of adopting inappropriate metamodels. Then, the NSGA-II is used to facilitate design space exploration. Besides, the main effects and contribution rates of process parameters on bead profile are analyzed. Eventually, the verification experiments of the obtained optima are carried out and compared with the un-optimized weld seam for bead geometries, weld appearances, and welding defects. Results illustrate that the proposed hybrid approach exhibits great capability of improving welding quality in LAW.

Multi-objective Process Optimization to Improve Surface Integrity on Turned Surface of Al/SiCp Metal Matrix Composites Using Grey Relational Analysis

The utilization of Al/SiCp metal matrix composites in different engineering fields has undergone a tremendous increase due to its tailor-made properties that can be achieved by varying the size and volume fraction of reinforcement. However, the difficulty in machining of metal matrix composites (MMCs) arises not only from the excessive wear of the cutting tools but also from fracturing of the reinforcement particles which leaves pits and cavities. These characteristics in machining of MMCs affect the machined surface integrity. Hence, the objective of this study is to identify the optimum process parameters to improve the surface integrity on Al/SiCp composites. The machined surface integrity have been analysed as a function of processing parameters, such as feed rate, cutting speed, depth of cut and cutting tool geometry. Surface integrity is associated with surface roughness and sub surface damage. Both these response variables are governed by the cutting forces, surface finish, residual stresses generated on the machined surface and microhardness variation beneath the machined surfaces. Thus, to improve the surface integrity on Al/SiCp composites multi objective process parameter optimization is performed using grey relational analysis. Experiments on Al/SiCp composites of four different compositions are performed using L27 orthogonal array as per the Taguchi method. Analysis of experimental results indicates that the surface roughness is more sensitive to a change in size than a change in volume fraction of reinforcement. Investigations on sub-surface integrity involving micro-hardness variation have shown that depth of altered material zone (AMZ) changes with a change in size of abrasive reinforcement in MMCs. The grey relational analysis shown that wiper insert geometry with 0.8mm tool nose radius, 0.05mm rev-1 feed, 40 m min-1 cutting speed and 0.2mm depth of cut are optimized machining conditions that enhances the surface integrity on Al/SiCp composite within the scope of the experiments performed.