Manufacturing Process Parameters Research Articles

Multi-stage manufacturing process parameter optimization method based on improved marine predator algorithm

Product quality is a critical factor in manufacturing industry competition, and mechanical processing technology has been widely applied in manufacturing, directly affecting product quality. Therefore, it is very important to find the appropriate optimal parameters to improve the impact of processing on product quality. However, modern production processes are characterized by complex mechanisms and the mutual influence of multiple processes, which poses higher challenges for optimizing processing technology parameters. In this regard, the thesis proposes a method for optimizing process parameters in multi-process manufacturing based on an improved marine predator algorithm, aiming to optimize and improve process parameters in multi-process manufacturing processes. Firstly, a multi-process modeling strategy is adopted to explore the nonlinear relationship between process parameters and quality indicators based on multi-gene genetic planning, establishing a multi-process parameter optimization objective model. This effectively solves the problem of modeling difficulty caused by severe coupling of multiple processes. Then, to improve the efficiency of solving the optimization objective model, an improved marine predator algorithm is proposed, utilizing reverse learning strategies and mixed control parameters to enhance optimization capability, thereby obtaining the global optimal solution. Finally, using production process data from a certain factory as an example, the feasibility of the proposed method is verified, achieving the goal of multi-process process parameter optimization and ensuring the stability of product quality.

Additively Manufactured Alnico Permanent Magnet Materials—A Review

Additive manufacturing offers manufacturing flexibility for intricate components and also allows for precise control over the microstructure. This review paper explores the current state of the art in additive manufacturing techniques for Alnico permanent magnets, emphasizing the notable advantages and challenges associated with this innovative approach. Both the LPBF and L-DED processes have demonstrated promising results in fabricating Alnico with magnetic properties comparable with conventionally processed samples. The optimization of process parameters successfully reduced porosity and cracking in the LPBF processing of Alnico. The review further explored the significance of additive manufacturing process parameter optimization in managing the temperature gradient and solidification rate for a desired microstructure and enhanced magnetic properties. Other potential additive manufacturing methods suitable for the fabrication of Alnico were discussed, along with the challenges associated with the process. The insights provided also highlight how additive manufacturing holds the potential to replace post-processing techniques like solutionization, magnetic annealing, and tempering often necessary in Alnico production.

Development of sustainable alkali-activated slag bricks with high content of carbonated recycled concrete fines: Strength, microstructure and environmental benefit

This research presents a novel method of recycling construction waste while capturing CO2 through the formation of mineral carbon. The technology uses carbonated recycled concrete fines (CRCF) as the primary raw material to produce sustainable, high-performance alkali bricks with high replacement rates. Firstly, a general assessment of the physical and chemical properties of CRCF was carried out, which revealed the formation of silica gel encapsulating the surface of CRCF during carbonation. Subsequently, alkali bricks were prepared utilizing CRCF. The study investigated the impact of various mix proportions, water-to-cement ratios, and manufacturing process parameters on brick strength. Additionally, an in-depth analysis of reaction products and brick microstructures was carried out. The results showed that the bricks produced from CRCF were stronger than those from RCF, indicating enhanced reactivity after carbonation. Finally, an analysis demonstrated that utilizing RCF from construction waste can significantly reduce carbon dioxide emissions, thereby mitigating the environmental impact associated with cement production.

Size effects of voids on the mechanical properties of 3D printed parts

Although the effects of additive manufacturing process parameters on the mechanical properties of 3D printed parts have been numerically investigated in the literature, less attention has been paid on the size effects of voids between deposited filaments. This study fills this gap by developing a methodology based on a combined finite element (FE) and design of experiment (DoE) technique. The development of FE methodology is based on micro-mechanical analysis of representative volume element (RVE) of 3D printed parts to predict the effective orthotropic properties. To account for the size effects of inter-bead voids, the RVE includes contribution of the multiple parameters of layer heights, layer widths and overlapping regions. To study the main and interaction effects of the above input parameters on the stiffness properties of 3D printed parts, a structured approach based on full factorial design is used. Although the size effects of voids on the constituents of elastic moduli of RVE were investigated, the main focus in the present work is to develop a regression model to predict the stiffness properties. The FE stress analysis of the RVE conducted in this study provides an insight about the potential failure modes such as delamination and filament debonding that may occur in load bearing 3D printed parts. For a case study, the results of FE-based homogenization technique in terms of stiffness properties are validated against the experimental data via three-point bending and Iosipescu shear tests which were conducted in conjunction with digital image correlation technique. The combined numerical and statistical approach proposed in this study provides a swift iterative design of 3D printed parts prior to the time-consuming computation modelling, contributing to reduce the number of tests and manufacturing costs.

A novel additive manufacturing strategy with high hydrogen production for ordered-porous stainless steel catalyst support

This paper proposes to improve hydrogen production performance of ordered-porous stainless steel by increasing its forming structure parameters. Firstly, selective laser melting additive manufacturing of ordered-porous stainless steel is carried out through designed orthogonal experiment table, to obtain its forming structure parameters under different manufacturing process parameters. Then, different forming structure parameters of ordered-porous stainless steel are converted into a comprehensive index by grey relation theory, which is grey relational degree (GRD). When powder thickness, scanning speed, scanning space and laser power are 30 μm, 800 mm/s, 90 μm and 60 W, respectively, the highest GRD of 0.930 is obtained, when those are 40 μm, 1200 mm/s, 70 μm and 60 W, respectively, the lowest GRD of 0.410 is gained. Finally, catalyst bonding strength and hydrogen production performance of the ordered-porous stainless steels with the above GRDs are verified. It is found that the ordered-porous stainless steel with the highest GRD has higher catalyst bonding strength and higher hydrogen production performance. When 12 mL/h injection rate of methanol–water solution and 250 ℃ reaction temperature are used, CH3OH conversion, H2 flowing rate and CO selectivity of the above ordered-porous stainless steel are 64.3%, 0.396 mol/h and 6.75%, respectively.

Multi-response optimisation of wire-arc additive manufacturing process parameters for AISI 4130 steel during remanufacturing process

Wire-arc additive manufacturing (WAAM) has emerged as a critical tool for remanufacturing industrial components. A limited understanding of this technique for quality product manufacturing has hindered its utilisation for industrial applications. This study reports on the optimisation of WAAM process parameters for AISI 4130 steel towards remanufacturing of high-quality products for industrial applications. AISI 4130 steel was selected for this study due to its high strength-to-weight ratio, excellent weldability, and suitability for the WAAM process. Taguchi’s Grey Relational Analysis (GRA) used four factors and three levels in the multiple response optimisation process. The study considered process parameters voltage, current, travel speed and gas flow in the gas metal arc welding (GMAW)-based WAAM technique. Analysis of Variance (ANOVA) results show that voltage, travel speed and gas flow significantly affect material deposition. Voltage had the highest significance (31.61%) compared to other parameters. The optimised process parameters were found to be: voltage –23 V, current –100 A, travel speed −350 mm min−1, and gas flow −10 L min−1. These parameters resulted in tensile residual stresses of 25 ± 74 MPa, microhardness of 171.4 ± 12.2 HV0.3, and a relative density of 98.21%. The microstructural analysis reveals the existence of predominant ferritic and pearlitic colonies. This is due to compounded thermal stresses during the deposition process and alloy composition resulting in tailored microstructure and mechanical properties. The study provides some insights into the WAAM remanufacturing process for producing highly quality industrial components.

Effect of process parameters on the roughness and tensile behavior of parts manufactured by the metals LPBF process

AbstractThe laser powder bed fusion process for metals (LPBF‐M) results in the development of stochastic surface features that significantly influence the interactions between parts and their surrounding environment, as well as their mechanical properties. The process parameters influence the surface quality, which is quantified by the surface roughness. Therefore, customizing the surface roughness during the build process can significantly contribute to obtaining ready‐to‐use parts, reducing the need for extensive surface posttreatments. This paper utilizes theoretical estimations of melt pool depth under iso‐linear energy density, iso‐power, and iso‐temperature manufacturing process parameter conditions. These estimations are then compared with experimental evaluations of surface roughness and tensile strength in upright‐built specimens to extract the trends in terms of the input energy versus roughness, and the input energy versus tensile behavior. The results show that iso‐energy values yield similar roughnesses due to the consistent expected melt pool depth. Moreover, an increase in melt pool depth generates higher surface roughness, while smaller melt pool dimensions result in improved roughness. Additionally, a comparison between the melt pool size and tensile test performance reveals a detrimental impact on the tensile strength for specimens estimated to have smaller melt pool depths.

Иерархическая модель оптимизации технологических параметров комплекса рабочих переходов для процесса механической обработки

Optimization of the parameters of the product manufacturing process is one of the key tasks of technological preparation of production. The technological process of mechanical processing has a complex hierarchical struc-ture. The effectiveness of optimizing the manufacturing process of a product directly depends on the level of its detail and the optimal choice of targets and control parameters. In this case, the technological process of mechanical processing, as an object of control, can be described in the form of a hierarchical model. Thus, the task of optimizing the technological process of mechanical processing is to determine the optimal values of control parameters for each structural element (intermediate state of the control object) of the hierarchical control model. The aim of the work is to develop a hierarchical model for optimizing the parameters of a complex of working transitions for machining operations. The structure of the hierarchical model of the product manufacturing process on metal-cutting machines is described. Within the framework of the developed model, five control levels are identified, control parameters for individual structural elements of the model are defined, as well as the relationship between them. For the intermediate states of the control object (structural elements), a description of single and vector optimization criteria is presented. The practical implementation of the developed control model is presented using the example of optimizing the technological parameters for the “Bushing” part. The application of the developed control model will increase the efficiency of the technological process of manufacturing products on metal-cutting machines by optimizing technological parameters based on a multi-criteria analysis at the stage of technological preparation of production.

Steel surface roughness parameter prediction from laser reflection data using machine learning models

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a Time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing.

Simulation-based process parameter optimization for wire arc additive manufacturing

AbstractDuring manufacturing of components using wire arc additive manufacturing, specific cooling times are required to prevent overheating of the structure and geometrical distortions. Currently, these cooling times are inserted based on experience at certain interlayer temperatures — which reduces the reproducibility, leads to unwanted component properties, and increases the process time. In this contribution, instationary thermal finite element simulations are applied to compute the temperature evolution of additively manufactured components using the inactive element method. This allows to optimize the process parameters, which are — in our considerations here — the welding velocity and the cooling time of each layer, to reduce the total process time while achieving sufficient component properties. The optimization is carried out with the gradient-free Nelder–Mead simplex algorithm, where certain constraints of the process parameters are considered via penalty functions. To obtain reasonable simulation results, the temperature-dependent heat transfer of the experimental setup is modeled and calibrated with experimental data beforehand. It becomes apparent that thermal finite element simulations combined with a gradient-free optimization procedure are a suitable numerical tool to perform the optimization of process parameters for wire arc additive manufacturing. The optimized process parameters fulfill certain requirements regarding the cooling of the manufactured component. Moreover, the optimized parameters can significantly reduce the process time compared to manually chosen parameters. In our example, this is around 48 %.

Tailoring surface roughness through the temporal variation of additive manufacturing process parameters

The benefits that additive manufacturing (AM) offers to the industry are generally well understood and appreciated. However, the current design for additive manufacturing (DfAM) methodologies and computer-aided manufacturing (CAM) packages neglect to exploit the full potential that AM can offer through its unique ability to vary material characteristics whilst the final component geometry is being formed. The purpose of this research is to demonstrate that additional design control can be gained through temporal DfAM (TDfAM). In this study, the ability to tailor the surface roughness of fused deposition modelling (FDM) AM polylactic acid (PLA) parts through the variation of two process parameters, nozzle temperature and print speed, is explored. The underpinning hypothesis is that variation of temperature and printing speed, can provide a significant change of surface roughness within one homogeneous part. This research demonstrated that nozzle temperature and print speed have a statistically significant effect on the surface roughness of the top and side surfaces. By increasing temperature and speed, the roughness of the side surfaces decreased and the roughness of the top surface increased. Furthermore, the in-silico implementation of TDfAM is demonstrated. As such, the research supports the hypothesis that TDfAM can enable additional control over the surface characteristics of a homogeneous part.

Investigations and Optimization of Cold Metal Transfer-based WAAM Process Parameters for Fabrication of Inconel 718 Samples using Response Surface Methodology

Investigations and Optimization of Cold Metal Transfer-based WAAM Process Parameters for Fabrication of Inconel 718 Samples using Response Surface Methodology

Parametric optimisation of 3D-printed PLA/wood dust composite for load-bearing application using grey relational analysis

This article proposes an investigation on sustainable polylactic acid/wood dust (PLA/WD) composite material for three-dimensional (3D)-printing applications. This eco-friendly material aids a remarkable reduction in thermoplastic waste, while minimising resource depletion, as a result it promotes circular economies, and mitigates environmental pollution. In order to extend the service life of manufactured components, it becomes necessary to investigate the key fused filament fabrication (FFF) processing parameters that affect the mechanical performance of PLA/WD composite parts. Taguchi-based L27 orthogonal array composite design was implemented for experimentation, and mathematical models were developed by using grey relational analysis to predict the responses for the proposed material. Moreover, the FFF input parameters with three levels of values that were considered for the experimentation are the nozzle temperature (190°C–200°C), layer height (0.12–0.28 mm), print speed (30–50 mm/min), infill density (80–100%) and raster angle (0°–90°). The results revealed that the highest grey relational grade obtained was 0.8333 for the experiment run 19 with the sample having ultimate tensile strength and shore D hardness of 20.71 ± 1.24 MPa and 74 ± 2 SHD, respectively. Moreover, raster angle, layer height and infill density were found to be the influential parameters that affect the tensile and hardness properties of the sample. Furthermore, nozzle temperature of 200°C, layer height of 0.2 mm, print speed of 30 mm/min, infill density of 100% and raster angle of 90° were obtained as optimum FFF input parameters.

Mechanical, corrosion and tribocorrosion resistance of additively manufactured Maraging C300 steel

This article presents the effect of selected parameters of the additive manufacturing process on the structure of Maraging C300 steel and consequently on its properties: hardness and resistance to abrasive wear, corrosion and tribocorrosion in 3.5% NaCl. The samples were produced with laser beams of 300, 350 and 400 W. Materials with different average microhardnesses of 436, 374 and 315 HV0.05 have been manufactured, respectively. Comparative evaluation of wear resistance on a ball-on-plate model node bench was carried out. Tests were performed under tribocorrosion conditions and for only mechanical forcing. Corrosion resistance was estimated from polarization curves. The article summarizes tribocorrosion model for Maraging C300 steel, which takes into account the effect of grain size on the course of elementary wear processes, especially on the formation of friction and corrosion synergy. The results show that reducing the power of the laser beam in the manufacturing process of Maraging steel produces a material with a more fine-grained structure. Smaller grains provide higher hardness and greater resistance to abrasive wear. Unfortunately, at the same time, the corrosion resistance in 3.5% NaCl is worse. The different effect of grain size on abrasion intensity and corrosion wear means that under tribocorrosion conditions, increasing microhardness does not reduce volumetric wear. The least tribocorrosion wear is provided by the material produced by a 350 W laser beam, amounting to 1.34 × 10−3 mm3.

Data-driven advisory system for industrial manufacturing. Application to the aluminum extrusion process

The article presents a methodology for developing systems supporting optimal decision-making in the selection of manufacturing process parameters and tooling, from the point of view of minimizing the tendency for the occurrence of product defects. The developed advisory system is based on two essential pillars: searching a database extracted from the company's database and a model obtained from the same data; the latter is used to extract the working database from the original enterprise database and set up the search algorithm. The paper first discusses the characteristics of typical data recorded in a manufacturing process and presents the data used in the present work. Next, various general concepts of building an advisory system are presented, and the choice of a system that combines a database approach with modeling based on machine learning is justified. The fundamentals of the system dedicated to the aluminum extrusion process are presented, followed by the system implementation, including examples of its functioning. The developed system not only allows decisions to be made based on recorded cases and hidden dependencies detected by models in the production process but also helps identify the causes of problems. It can also help to draw attention to critical parameters of a product from the point of view of its manufacturing.

Process parameter optimization for laser directed energy deposition (LDED) of Ti6Al4V using single-track experiments with small laser spot size

Single-track experiments are routinely used in the optimization of process parameters in additive manufacturing processes. Most of the process parameter optimization studies use a laser spot size of 1 mm or more. Since laser spot size affects the input energy density and in turn the efficiency of the deposition process, it is important to develop process maps every time a laser of different spot sizes is used. In this work, we determine the process maps for a laser of 0.6 mm spot size. By combining the process maps and the metallographic inspection, we estimate the optimum process parameters (laser power, scan speed, powder feed rate) for building Ti6Al4V components using powder-based laser-directed energy deposition(LDED). Single-tracks corresponding to 64 different parameter combinations are deposited. After eliminating the process parameter combinations resulting in defective tracks, the optimum process parameters of 300 W laser power and 720 mm min−1 scan speed is established by considering the relationship between the process parameters and the geometrical features of the deposit. The experimental results are then used to calibrate the modeling parameters of a three-dimensional finite element model for simulating the deposition process.

Machine learning-based performance predictions for steels considering manufacturing process parameters: a review

Machine learning-based performance predictions for steels considering manufacturing process parameters: a review

Development, Prospect and Challenge for Additive Manufacturing of Polymer Materials

As a manufacturing technology that fabricates three-dimensional objects by stacking materials layer by layer, additive manufacturing can be divided into laser fused deposition, electron beam fused deposition, inkjet printing and so on based on different additive manufacturing processes. In recent years, remarkable progress has been made in the additive manufacture of polymer materials. Firstly, the additive manufacturing equipment and process parameters suitable for polymer materials have been successfully developed, which realized the accurate manufacturing of complex structures. Secondly, by optimizing the formula and post-treatment of polymer materials, the performance and durability of additive products are improved. In addition, the researchers also explored the application potential of additive manufacturing of polymer materials in medical, aerospace and other fields. However, the additive manufacturing of polymer materials still faces some challenges. One of them is that the rheological properties of polymer materials have a great impact on the progress of additive manufacturing, which needs further research and improvement. Meanwhile, the heat and stress generated in additive manufacturing may lead to the deformation and degradation of polymer materials, which requires effective thermal management. Finally, compared with the traditional cutting process, the additive manufacturing cost of polymer materials is still high, so it is necessary to reduce the cost and promote production efficiency. In a word, additive manufacturing of polymer materials has broad prospects and application potential, but further research and improvement are still needed to overcome related challenges.

Rapid additive manufacturing of all-carbon, all-dielectric metastructures

Metamaterials have been explored to achieve extraordinary response to incident radiations, not accessible to conventional materials. Metamaterial attributes are derived from spatially arranged sub-wavelength resolved metaelements. Additive manufacturing technologies offer capabilities to fabricate complex metastructures with novel remarkable responses. Two-photon polymerization (TPP) based additive manufacturing has gained popularity due to its exceptional precision and minimal processing stages. However, the major bottleneck in the development of TPP based fabrication is its dependence on engineered/proprietary/expensive two-photon active materials for initiation of polymerization process. Here, we propose an acrylate and carbon dot composite resin for additive manufacturing of all-carbon, all-dielectric metamaterials. Two-photon irradiation induces photo-induced electron transfer from the surface states of carbon quantum dots, which in turn initiates the TPP. The composite resin is devoid of any other two-photon dye/initiator. The effect of carbon dot concentration on fabrication process parameters and fabrication resolution have been systematically investigated. Several linear/complex planar and 3D structures have been fabricated with high replication fidelity. As a proof-of-concept, the composite resinhas been used for the realization of resonant and chiral metamaterials. Furthermore, theoretical calculations are also performed to evaluate the optical activity of these metastructures. Our results demonstrate steps towards realizing all-dielectric, all-carbon based metamaterials.

Exploring the Effects of Process Parameters during W/O/W Emulsion Preparation and Supercritical Fluid Extraction on the Protein Encapsulation and Release Properties of PLGA Microspheres.

In this study, protein-loaded poly(lactic-co-glycolic acid) (PLGA) microspheres were prepared via supercritical fluid extraction of emulsion (SFEE) technology. To understand the correlation between process parameters and the main quality characteristics of PLGA microspheres, a comprehensive prior study on the influence of process variables on encapsulation efficiency (EE), initial drug burst release (IBR), morphology, surface property, and particle size distribution (PSD) was conducted within a wide process condition range of each unit process step, from the double-emulsion preparation step to the extraction step. Bovine serum albumin (BSA), a high-molecular weight-protein that is difficult to control the IBR and EE of PLGA microspheres with, was used as a model material. As double-emulsion manufacturing process parameters, the primary (W/O) and secondary emulsion (W/O/W) homogenization speed and secondary emulsification time were evaluated. In addition, the effect of the SFEE process parameters, including the pressure (70-160 bar), temperature (35-65 °C), stirring rate (50-1000 rpm), and flow rate of supercritical carbon dioxide, SC-CO2 (1-40 mL/min), on PLGA microsphere quality properties were also evaluated. An increase in the homogenization speed of the primary emulsion resulted in an increase in EE and a decrease in IBR. In contrast, increasing the secondary emulsification speed resulted in a decrease in EE and an increase in IBR along with a decrease in microsphere size. The insufficient secondary emulsification time resulted in excessive increases in particle size, and excessive durations resulted in decreased EE and increased IBR. Increasing the temperature and pressure of SFEE resulted in an overall increase in particle size, a decrease in EE, and an increase in IBR. It was observed that, at low stirring rates or SC-CO2 flow rates, there was an increase in particle size and SPAN value, while the EE decreased. Overall, when the EE of the prepared microspheres is low, a higher proportion of drugs is distributed on the external surface of the microspheres, resulting in a larger IBR. In conclusion, this study contributes to the scientific understanding of the influence of SFEE process variables on PLGA microspheres.