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Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique (density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve undesired problems occurring during neural network training to increase the model accuracy. The model’s performance was proven with a value of R2 of 99% for both density ratio and surface roughness. A selection system was then built, allowing users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement. Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9–4.4% of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process parameters for the desired density ratio and surface roughness.

The mechanical properties of a fused deposition modeling (FDM) process product are greatly influenced by many process parameters. The identified parameters namely deposition orientation, layer thickness and deposition style, raster width, and raster gap are more significant factors contributing to the strength of a FDM product. In this paper, tensile strength, flexural strength, and impact strength are considered as three evaluation indexes to characterize the mechanical properties of a FDM part. An experimental research approach based on the Taguchi method was presented and some special specimens were designed. The influences of the five parameters on the three evaluation indexes were analyzed by the use of analysis of variance (ANOVA). Finally, based on the gray relational analysis, a set of optimal process parameter combination was obtained to optimize comprehensive mechanical properties of FDM parts.

Additive manufacturing (AM) applications are rapidly expanding across multiple domains and are not limited to prototyping purposes. However, achieving flawless parts in medical, aerospace, and automotive applications is critical for the widespread adoption of AM in these industries. Since AM is a complex process consisting of multiple interdependent factors, deep learning (DL) approaches are adopted widely to correlate the AM process physics to the part quality. Typically, in AM processes, computer vision-based DL is performed by extracting the machine’s sensor data and layer-wise images through camera-based systems. This paper presents an overview of computer vision-assisted patch-wise defect localization and pixel-wise segmentation methods reported for AM processes to achieve error-free parts. In particular, these deep learning methods localize and segment defects in each layer, such as porosity, melt-pool regions, and spattering, during in situ processes. Further, knowledge of these defects can provide an in-depth understanding of fine-tuning optimal process parameters and part quality through real-time feedback. In addition to DL architectures to identify defects, we report on applications of DL extended to adjust the AM process variables in closed-loop feedback systems. Although several studies have investigated deploying closed-loop systems in AM for defect mitigation, specific challenges exist due to the relationship between inter-dependent process parameters and hardware constraints. We discuss potential opportunities to mitigate these challenges, including advanced segmentation algorithms, vision transformers, data diversity for improved performance, and predictive feedback approaches.

Purpose Masked stereolithography (MSLA) or resin three-dimensional (3D) printing is one of the most extensively used high-resolution additive manufacturing technologies. Even though, the quality of 3D printing is determined by several factors, including the equipment, materials and slicer. Besides, the layer height, print orientation and exposure time are important processing parameters in determining the quality of the 3D printed green state specimen. The purpose of the paper is to optimize the printing parameters of the Masked Stereolithography apparatus for its dimensional correctness of 3D printed parts using the Taguchi method. Design/methodology/approach The acrylate-based photopolymer resin is used to produce the parts using liquid crystal display (LCD)-type resin 3D printer. This study is mainly focused on optimizing the processing parameters by using Taguchi analysis, L-9 orthogonal array in Minitab software. Analysis of variance (ANOVA) was performed to determine the most influencing factors, and a regression equation was built to predict the best potential outcomes for the given set of parameters and levels. The signal-to-noise ratios were calculated by using the smaller the better characteristic as the deviations from the nominal value should be minimum. The optimal levels for each factor were determined with the help of mean plots. Findings Based on the findings of ANOVA, it was observed that exposure time plays an important role in most of the output measures. The model’s goodness was tested using a confirmation test and the findings were found to be within the confidence limit. Also, a similar specimen was printed using the fused filament fabrication (FFF) technique; it was compared with the quality and features of MSLA 3D printing technology. Practical implications The study presents the statistical analysis of experimental results of MSLA and made a comparison with FFF in terms of dimensional accuracy and print quality. Originality/value Many previous studies reported the results based on earlier 3D printing technology such as stereolithography but LCD-based MSLA is not yet reported for its dimensional accuracy and part quality. The presented paper proposes the use of statistical models to optimize the printing parameters to get dimensional accuracy and the good quality of the 3D printed green part.

With the rapid advancement of intelligent manufacturing technologies, Reconfigurable Flexible Assembly Lines (RFALs) have emerged as a promising solution to enhance production flexibility and efficiency. However, the process parameter optimization for RFALs, particularly in assembly line balancing, presents a complex NP-hard combinatorial optimization problem. This study aims to address the process parameter optimization in RFALs by considering multiple critical performance metrics, including production cycle time, tool replacement time, and assembly unit cost. A mathematical model is formulated to describe the optimization problem, clearly defining the objectives and constraints. Based on this model, a novel Synergistic Genetic Algorithm and Particle Swarm Optimization (SGAPSO) is proposed. The SGAPSO algorithm effectively combines the global exploration capability of Genetic Algorithms (GA) and the local exploitation and fast convergence characteristics of Particle Swarm Optimization (PSO). It employs a task sequence-based encoding method. In the GA phase, population evolution is driven by selection, crossover, mutation, and elitism. In the PSO phase, particle positions are decoded using the Smallest Position Value (SPV) rule, and iterations are optimized through standard velocity and position update equations. The key synergistic mechanism proved by ablation study involves periodically guiding the PSO population with elite individuals from GA and enhancing the GA population with superior solutions found by PSO. Experimental validation on standard benchmark problems and an industrial case study of pressure-reducing valve assembly shows that the SGAPSO algorithm outperforms standalone GA and PSO in terms of solution quality, convergence speed, and solution stability.

Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

Metal 3D printing has gained a lot of attention among industries since it offers a practical solution to problems rising during the manufacturing of parts and components with complex geometry. This is an additive technology that eliminated several fabrication steps and at the same time reduces material waste during the manufacturing process. However, in all additive manufacturing technologies, the final properties of the parts are determined by the operational process parameters. In this study, several machine learning algorithms were examined to characterize the effects of the printing process parameters on relative density, hardness, yield strength, and tensile strength in manufactured parts. It was possible by using “Big Data” collected from a large number of previously published articles on the application of laser powder bed fusion (LPBF) for the 3D printing of 316L stainless steel samples. Among different process parameters, laser power, laser energy density, and scanning speed were proven to have the largest effects directly on the physical and mechanical properties of LPBF-processed parts. Six different classification models and five support vector machine regression-based models were tested to find the most accurate prediction algorithm. To validate the obtained results from the applied machine learning models, a set of 316L specimens were produced using LPBF technology using a random set of process parameters. The physical and mechanical properties of 3D printed samples were tested and compared to the ones predicted from the optimum models from machine learning analysis. The results were in great agreement, which shows the high accuracy of the developed machine learning algorithms in this study.

Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios.

Forging is a traditional and important manufacturing technology to produce various high strength products and is widely used in engineering fields such as automotive, aerospace and heavy industry. To produce highly accurate product, underfill that the material is not filled into the cavity should strongly avoided. For material saving and near-net product, flash should be minimized. To make the tool life long, it is preferable to produce product with low forging load. It is also preferable to uniformly deform the billet as much as possible for high strength product. Crack is a crucial defect and should strongly be avoided. Therefore, many requirements are taken into account in order to produce the forged product. To meet the requirements, design optimization in forging coupled with computer aided engineering (CAE) is an effective approach. This paper systematically reviews the related papers from the design optimization point of view. For the billet or die shape optimization, the papers are classified into four approaches. The process parameters optimization such as the billet temperature, the die temperature, the stroke length and the friction coefficient is conducted, and the related papers are also classified into four categories. The design variables and the objective function(s) used in the papers are clarified with the design optimization technique. The multi-stage forging including the hammer forging for producing complex product shape is also briefly reviewed. Finally, major performance indexes and the future outlook are summarized for the further development of design optimization in forging.

Fused deposition modeling (FDM) is an additive manufacturing (AM) technology capable of producing functional parts with complex geometries. However, optimizing both dimensional and mechanical quality characteristics is challenging due to the influence of multiple process parameters. This study aims to determine how key FDM parameters affect the multivariate dimensional and mechanical quality characteristics and to establish an integrated framework for optimizing these responses simultaneously. An experimental design based on response surface methodology (RSM) was implemented to optimize four process parameters: layer thickness, extruder temperature, plate temperature, and printing speed. Cylindrical PLA samples adhering to compression standards were fabricated, and both dimensional characteristics (length and diameter) and mechanical characteristics (compressive strength and modulus) were evaluated. Multivariate process capability indices (MPCIs) were then estimated to assess overall process capability. The results revealed that layer thickness and extruder temperature are the most influential parameters affecting MPCIs. The optimal dimensional MPCI was achieved with a low layer thickness, high extruder temperature, low plate temperature, and low printing speed. Also, the proposed model explained 88% of the total variability in mechanical MPCI. This research introduces an integrated RSM–multivariate process capability analysis approach for the simultaneous optimization of multiple correlated quality characteristics in FDM, which improves both dimensional precision and mechanical performance in AM processes.