Manufacturing Process Parameters Research Articles

Optimize Process Parameter of FDM Techniques to Improve Mechanical Properties of Aerospace Application

The Fused Deposition modelling technique is widely accepted by industries as it is the one of the most convenient modern technology. The fused deposition modelling (FDM) is one of the additive manufacturing techniques which are largely used for printing of metal/thermoplastic materials with ease of design flexibilities the proposed research had been carried out for the investigation and optimization of process parameters for product or application development through FDM. The FDM- (Fused deposition Modelling) is widely used for product development and do contain various control variables. Here the process relates to nozzle temperature, base plate temperature, filament feed, filament material and deposition speed. The research presented here had been conducted considering nozzle temperature, layer thickness, and internal profile as variables for specimen manufacturing. In Aerospace application, optimization process is highly required for the Properties of material, weight and other effects. Hence the tensile specimen had been prepared to represent an aerospace application of ducts for airflow. The full factorial design of experiments had been considered for experimental investigation. The design of experiment had been conducted with three factors; three no. of parameters at three different levels Hence, A total no. of 27 representation samples had been prepared for tensile test and surface roughness for the Optimum result. The results had given considerable parametric effect as an outcome. The optimized results had been manufactured on an Ultimaker3D printer machine and tested which confirmed the results. The outcomes will assure optimal manufacturing process parameters of FDM for improved mechanical properties.

Multiple criteria decision support analysis for manufacturing process parameters selection of metallic bipolar plates for polymer electrolyte membrane fuel cells

The multi-criteria decision-making method, along with experimental and finite-element (FE) methods, was employed to determine the optimal process parameter values for rubber pad forming of metallic bipolar plates. To achieve this aim, the rubber pad forming process was modeled by FE software, and the results were validated by conducting experimental tests. Three significant rubber pad forming parameters—punch force, hardness, and the thickness of the rubber layer—were considered as input variables. A full-factorial design involving 27 experiments was used to assess the alternatives. Filling percentage, thinning ratio, and stress were considered as distinct evaluation criteria. To solve the multi-criteria decision-making problem, a hybrid approach was used to weigh the alternatives. According to the results, the applied method was capable of selecting the optimal values of process parameters while considering different criteria simultaneously. The results demonstrated that a punch force of 50 kN, rubber hardness of 55 Shore A, and rubber thickness of 3.5 mm were the best processing conditions. For these parameters, the filling percentage, thinning ratio, and stress were 97.6%, 28.5%, and 1005 MPa, respectively.

Standardization and Optimization of Process Parameters for Manufacturing of Yak Milk Paneer

Standardization and Optimization of Process Parameters for Manufacturing of Yak Milk Paneer

Defect Classification for Additive Manufacturing with Machine Learning.

Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model's accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process's parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.

Symmetry of gamma distribution data about the mean after processing with EWMA function

Statistical Process Control (SPC) plays a vital role in maintaining quality and reducing variability in manufacturing processes. Among SPC techniques, the Exponentially Weighted Moving Average (EWMA) stands out for its ability to detect small process shifts quickly, making it a valuable tool in ensuring product consistency and preventing quality issues. EWMA constructs control charts to monitor process mean shifts, tracks product/service quality by identifying variations, and monitors manufacturing process parameters for early detection of deviations and necessary adjustments. EWMA control chart has been proposed as an alternative to the Shewhart control chart. Sequential measurements are processed using the EWMA function before being placed on the control chart. One of the crucial concerns about the EWMA control chart is the asymmetry of the data around the mean. Although processing with the EWMA function reduces data skewness, the problem of asymmetric data may not be solved. The control chart is designed to leave in front of the upper control limit (UCL) α/2 of the data and behind the lower control limit (LCL) another α/2 of the data, and this does not occur in the case of symmetric data. α/2 represents the significance level for each tail in a two-tailed hypothesis test, indicating the probability of incorrectly rejecting the null hypothesis for each side of the distribution. Since many of the distributions in real life can be approximated by the Gamma distribution, the Gamma distribution was adopted in this study. The Monte Carlo simulation methodology was implemented to generate Gamma distributed data, process it with EWMA function and assess the skewness and kurtosis. The purpose of this paper is to evaluate the effect of EWMA parameters on the performance of the EWMA control chart. Moreover, it focuses on skewness and kurtosis reduction after data processing using the EWMA function. The findings help researchers and practitioners to select the best parameters. Further, the research investigates the effect of EWMA parameter on the shape of distribution.

Abstract 14 Omidubicel-Onlv: Robustness of the Manufacturing Process

Abstract Introduction Omidubicel-onlv is a nicotinamide (NAM) -modified allogeneic hematopoietic progenitor cell therapy derived from cord blood. NAM expansion increases the number of stem and progenitor cells, inhibits differentiation, and potentiates migration, bone marrow homing, and engraftment efficiency. A global phase 3 trial demonstrated faster hematopoietic recovery, reduced rates of infection, and shorter hospitalizations for patients randomized to omidubicel-onlv compared to standard umbilical cord blood (UCB). Omidubicel-onlv is manufactured for each patient from a single, appropriately matched cord blood unit (CBU). The inherent donor variability as well as the different processing and testing methods used by different Cord Blood Banks (CBBs) present challenges in maintaining a consistent product. Objectives This extensive study was conducted to confirm the robustness of the manufacturing process and the proven acceptable range for the critical and key process parameters. Methods Seven process parameters were evaluated in total. Of these, three parameters were related to the CD34+ expansion phase: CD34+ seeding concentration, incubation temperature, and CO2 levels. Four additional parameters were related to final product formulation and cryopreservation: hold times during harvest and formulation, cell concentration in the drug product, DMSO concentration, and storage temperature. The impact of each process parameter on product performance was assessed by a side-by-side comparison of its target value (control) versus the tested range. Each parameter was evaluated using at least three batches derived from three CBUs. Statistical analysis of the coefficient of variation or recovery against the control was applied to the data sets. Results Statistical analyses demonstrated an overall variability of ≤30% in critical attributes including fold expansion, viability, phenotyping, and recovery of cells post-cryopreservation and stability. Discussion The omidubicel-onlv manufacturing process is robust, overcoming the variability of batches derived from different donors, different processing and testing methods at CBBs, and manufacturing process parameters.

Methodology for developing a prediction model for the remaining fatigue life and residual strength of tidal turbine blades

As tidal energy nears commercial viability, the reliability and safety of a tidal energy device becomes more prevalent. A key aspect for determining their reliability and safety, along with reducing risk during operational deployment, is the structural integrity of tidal turbine blades. Therefore, a validated model for predicting the structural integrity of tidal turbine blades will aid in de-risking tidal energy technologies. In this study, a three-phase approach was used to formulate a strategy to predict the remaining fatigue life and residual strength of tidal turbine blades, over their operational lifespan. In Phase 1, the parameters influencing the structural properties of tidal turbine blades were identified based on the literature review, and the expertise in the field. Then, parameters were extensively studied and classified into four main impact groups, which include load conditions, design and manufacturing, degradation, and unexpected situations. Loading conditions on the blade are directly linked to hydrodynamic forces, maintenance, operating conditions, and corrosion effects. At the same time, these scenarios can vary with fluid-structure interactions, climate conditions, local site conditions, and maintenance and inspection schedules of the blades. The design and manufacturing category mainly represents the impact of the properties of composite materials, the geometry of the blade, and manufacturing process parameters. Similar to the other structures, tidal turbine blades are subject to deterioration and unexpected accidents during their service life, which significantly compromises the structural integrity of the blade. In Phase 2, a data management strategy was formulated related to identified four impact categories and investigated the possible methods of analysing the data. In this context, finite element analysis of composite tidal turbine blades was identified as the most appropriate tool to comprehensively examine collected data, prior to comparing the results to the field and laboratory-based test data. Mesh properties of the numerical models, test standards, instrumentation, and equipment used for field and laboratory-based structural testing of tidal turbine blades, as well as the accuracy of data acquisition systems, influence the comparison of these results. Finally, with the information gathered, as well as knowledge and experience in the field, a method for estimating the residual strength and remaining fatigue life of tidal turbines at each stage of their operation was formulated. The model will undergo a series of extensive validation processes using experimental testing datasets and will be used in the future to develop vulnerability curves related to the remaining structural life of the tidal turbine blades.

An all-encompassing study on the joint effect of powder feedstock characteristics and manufacturing process parameters on the densification and mechanical properties of additively manufactured nickel alloy 718

Laser-powder bed fusion (L-PBF) is a commonly employed additive manufacturing (AM) technique in manufacturing 3D metallic parts with complex geometries from powder materials. It is a complex process with more than 100 process parameters affecting the properties of fabricated parts. Studies on AM of nickel alloy 718 are currently focusing on investigating the effect of the process parameters and heat treatment on final part properties. Nevertheless, the effect of different powder characteristics such as particle size distribution (PSD), the presence of fine particles, powder reuse, and moisture content has not been studied comprehensively in the literature. In this study, two types of powder: virgin and recycled, as well as four parameters: PSD, which is represented by the median particle size and the particle size range, moisture content, and volumetric energy density (VED), were studied. To reduce experimental cost and burden, statistical design of experiment (DOE) techniques were utilized. Then, the measured results were analyzed using analysis of variance (ANOVA) to ascertain the effect of the different powder characteristics on multiple quantities of interest (QoIs). Different densification and mechanical tests were performed, and their outputs were studied as the QoIs. This study revealed that VED, powder type, PSD range, and median are the most common significant factors for most QoIs. Meanwhile, moisture content had a significant effect only on the Charpy impact energy. Besides, the study showed that the presence of fine particles <20μm improved the densification of the fabricated parts but reduced the mechanical properties.

Simultaneous optimization of topology and process parameters for laser-powder bed fusion additive manufacturing

Simultaneous optimization of topology and process parameters for laser-powder bed fusion additive manufacturing

The structure and mechanical properties of high-strength aluminum alloys fabricated with wire arc additive manufacturing

In this study, optimized wire arc additive manufacturing (WAAM) process parameters were used to fabricate 2319 aluminum alloy samples. The microstructure and mechanical properties of printed samples were carefully studied. The results showed that 2319 aluminum alloy samples manufactured by WAAM had good surface quality, and no metallurgical defects such as cracks or incomplete fusion were found in the samples. The printed 2319 aluminum alloy sample was mainly composed of the matrix phase ɑ (Al) and strengthening phase θ (Al2Cu). It was found that the quantity and distribution of strengthening phases were affected by the heat treatment process. Compared to the T4-state heat treatment, the distribution of the strengthening phase a (Al2Cu) was more uniform and its quantity was significantly increased after the T6-state (530 °C/20 h+170 °C/18 h) heat treatment. As a result, the results showed that its transverse tensile strength was 436.3 MPa and the elongation was 12%. Meanwhile, the longitudinal tensile strength was 376.3 MPa, and the elongation was 8.5%, which was much higher than the performance of casted 2319 aluminum alloy. The fracture surfaces of 2319 samples had small and uniform dimples, which indicated that they belonged to ductile fracture.

Effect of internal defects on tensile strength in SLM additively-manufactured aluminum alloys by simulation

This research investigates the effect of internal defects on the tensile strength of Selective Laser Melting (SLM) additively-manufactured aluminum alloy (AlSi10Mg) test parts used for civil aircraft light weight design. A Finite Element Analysis (FEA) model containing internal defects was established by combining test data and the stress concentration factor comparison method. The effect of variation in the number, location and shape of defects on the finite element results was analyzed. Its results show that it is reasonable to use spherical defect modeling. The finite element modeling and analysis methods are also applied to the study of the effect of internal defects on tensile strength in additive manufacturing of other metallic materials. According to the FEA results of single defects at different scales, the formula for calculating the weakening degree of tensile strength applicable to the defective area of less than 15% was established. The result of the procedure is reliable and conservative. This research results can guide the selection of process parameters for the additive manufacturing of aluminum alloys. Further, the research results can promote the application of metal additive manufacturing in designing light-weight civil aircraft structures.

A framework for the robust optimization under uncertainty in additive manufacturing

This paper introduces a conceptual framework for the robust optimization under uncertainty in the directed energy deposition (DED) of M4 High-Speed Steel. The goal is to identify optimal process parameters for robust manufacturing of printed parts with a stationary melt pool depth and low consumed energy under uncertainty within the multiple layers of a bulk sample. To increase the computational efficiency, a deep learning-based surrogate model is built using the training data generated by a validated high-fidelity DED two-dimensional FE model. The robustness of the optimized result is verified using the Monte-Carlo method and compared with experiments and two other deterministic approaches. Furthermore, we conduct a global sensitivity analysis, which indicates that among six uncertain input variables, the thermal conductivity and the convection have the most significant impact on the melt pool depth variation. This study shows the promising possibilities of the presented framework in optimizing the DED process.

Recommendation of SLM Process Parameters Based on Analytic Hierarchy Process and Weighted Particle Swarm Optimization for High-Temperature Alloys.

Selective laser melting (SLM) of high-temperature alloys involves intricate interdependencies among key process parameters, such as laser power and scanning speed, affecting properties such as density and tensile strength. However, relying solely on experiential knowledge for process parameter design often hampers the precise attainment of target requirements. To address this challenge, we propose an innovative approach that integrates the analytic hierarchy process (AHP) and weighted particle swarm optimization (WPSO) to recommend SLM process parameters for high-temperature alloy fabrication. Our proposed AHP-WPSO model consists of three main steps. First, a comprehensive historical database is established, capturing the process parameters and performance metrics of high-temperature alloy SLM parts. Utilizing an AHP framework, we compute the performance similarity between target and historical cases, applying rational thresholds to identify analogous cases. When suitable analogs are elusive, the model seamlessly transitions to the second step. Here, the WPSO model optimizes and recommends process parameters according to target specifications. Lastly, our experimental validation of the GH4169 high-temperature alloy through SLM experiments corroborates the effectiveness of our AHP-WPSO model in making process parameter recommendations. The outcomes underscore the model's high accuracy, attaining a recommendation precision of 99.81% and 96.32% when historical analogs are present and absent, respectively. This innovative approach offers a robust and reliable solution to the challenges posed in SLM process parameter optimization for high-temperature alloy applications.

Local control of microstructure and mechanical properties of high-strength steel in electric arc-based additive manufacturing

Additive manufacturing offers a significant potential for producing metallic parts with distinctly localised microstructures and mechanical properties, commonly known as functional grading. While functional grading is generally accomplished through compositional variations or in-situ thermo-mechanical treatments, variation of process parameters during additive manufacturing can offer a promising alternative approach. Focusing on the electric arc-based additive manufacturing process, this work focuses on the functional grading of high-strength steel (S690 grade) by adjusting the travel speed and inter-pass temperature. Through a combination of thermal simulations and experimental measurements on single bead-on-plate depositions, it is shown that the microstructure and the mechanical properties of parts can be controlled through the rational adjustment of process parameters. A rectangular block was fabricated to demonstrate functional grading using a constant wire feed rate and varying travel speed. The rectangular block consisted of a low heat input (LHI) region deposited between high heat input (HHI) zones. A graded microstructure was obtained with the HHI zones composed of a mixture of polygonal ferrite, acicular ferrite, and bainite, while the LHI region was primarily composed of martensite. The hardness and profilometry-based indentation plastometry measurements indicated that the LHI region exhibited higher hardness (32%) and strength (50%), but lower uniform elongation (80%), compared to the HHI zones. The present study demonstrates the potential to achieve functional grading by adjusting process parameters in electric arc-based additive manufacturing, providing opportunities for tailor-made properties in parts.

A framework for predicting grain morphology during incremental sheet metal forming using generative adversarial networks

Properties and service performance of parts and components depend upon microstructural characteristics including grain morphology, size and size distribution, and crystallographic orientation. Microstructure itself is dictated by manufacturing process parameters. Microstructural analysis is often necessary for evaluating product quality. Past research has attempted to map product/process characteristics using analytical, numerical, and experimental approaches. To address the limitations of conventional modeling approaches, researchers have begun to explore the application of artificial intelligence (AI) techniques in correlating and predicting the effect of process parameters on material microstructure and mechanical properties. For instance, AI-based approaches have been used to model the influence of process parameters on forming force, formability, geometric accuracy, tool path, and surface quality in incremental sheet forming (ISF), a flexible forming process suitable for low-volume and custom product manufacturing. The research reported herein introduces a framework to map ISF process parameters to microstructural images using Generative Adversarial Networks (GAN). This approach can reduce the experimental and analytical effort and costs typically necessary to evaluate the effects of ISF process settings on material microstructure and mechanical properties.

Construction of Graphene-Paper Flexible Pressure Sensor

Combining with flexible materials, two-dimensional nanomaterial graphene can effectively improve the mechanical and electrical properties of composites for flexible sensors, which has broad application prospects in health monitoring and human wearable devices. Here, we designed a sandwich paper-based flexible graphene sensor. The amplification and circuit were constructed to realize the acquisition and display of voltage signals caused by micro-strain to achieve the purpose of pulse signal detection. The qualitative and quantitative fabrication process parameters studies were conducted through experiments to make the sensor have high conductivity and sensitivity.

Three-dimensional physical modeling of the wet manufacturing process of solid-state battery electrodes

Poor design of composite electrodes in Solid-State Batteries (SSBs) is one of the main reasons for their low performance. Although modeling techniques offer great potential in investigating multiple conditions for microstructure optimization, SSB manufacturing remains almost unaddressed in terms of computational studies. We present here a three-dimensional physics-based modeling workflow to investigate the impact of wet manufacturing process parameters on the properties of SSB tape casted composite electrodes based on LiNi0.6Mn0.2Co0.2O2 as the active material and Li6PSCl5 sulfide solid electrolyte. Our proposed workflow models the entire process of wet manufacturing of SSB electrodes, starting with the slurry consisting of active material, carbon additive, binder, solid electrolyte and solvent, and continues through its drying process, and the calendering of the resulting electrodes. Our focus is in particular on the impact of the calendering degree on the microstructure of the electrode. We characterize the resulting microstructures in terms of electronic and ionic conduction properties. We believe that this first-of-its-kind wet manufacturing process model for SSB cathodes is an important step towards the development of systematic modeling approaches that can provide practical optimization of the interfaces between materials in electrodes to improve SSB performance and durability.

Effects of additive manufacturing process parameters and heat treatment on texture evolution and variant selection during austenite-martensite transformation in 18%Ni-M350 maraging steel

Variant selection and texture evolution of 18%Ni M350 maraging steel produced by laser-based directed energy deposition (DED) additive manufacturing (AM) technology are investigated in this study. The effects of heat treatment and AM processing parameters, including energy area density (EAD), powder feed rate, and laser power, on variant selection and texture evolution are discussed. Theoretically, 24 martensite variants tend to be formed during a cubic-to-cubic transformation based on the Kurdjumov–Sachs orientation relationship. Identifying the preference of variants in a thermo-mechanical process can pave the way for tailoring the desired product texture. Grain graph and local neighbor voting algorithms using measured electron backscatter diffraction (EBSD) were employed to reconstruct the parent austenite grain structure in this study. Even though no evidence of global variant selection is found, the variant selection tends to take place locally in small parent grains of heat-treated samples. Strong rotated Copper 112110γ and Z 111110γ components are observed in the parent austenite phase of the low EAD sample after heat treatment due to a high degree of recrystallization and second-order twins. A Goss texture 110001α in child martensite transitioned from these strong components is also observed to be strongly derived from packet 3 of this sample. This finding indicates that some low-misorientation variants of packet 3 are more energetically favorable to develop in a heat-treated M350 alloy produced by a low EAD.

Mechanical and tribological properties of aluminium based metal matrix composites: An overview

This paper presents a comprehensive review of open literatures to segregate the effects of composite fabrication process parameters, reinforcement types and its contents on mechanical and tribological properties of the developed metal matrix composites (MMCs). Effects of various wear factors (i.e. load, sliding speed and sliding distance) on wear rate (WR) and coefficient of friction (COF), along with different dominating wear mechanisms are emphasized. Outcomes of the review revealed that increase in stirring time affects the mechanical properties adversely, whereas increase in ball milling time improves these. Incorporation of ceramic and carbonaceous reinforcements in Al alloy improves its mechanical and wear resistance properties. Effects of wear process parameters on WR and COF depend upon the types of matrix-reinforcement combinations, reinforcement content and test conditions.

Research on Rotor Sleeve Winding Techniques for High-Speed Permanent Magnet Motors via NOL Ring Testing

Surface-mounted high-speed permanent magnet motors are known for their small size, high power factor, and reliable control and operation. However, these motors may have low mechanical strength and weak tensile strength in their rotor magnet, which necessitates the adoption of an external sleeve for protection. Currently, carbon fiber composite sleeves are garnering increasing attention for their durable and reliable protective capabilities. Despite the apparent benefits of carbon fiber composite protective sleeves, systematic research on the relevant parameters such as winding angle, fiber type, resin type, winding tension, and curing method is currently lacking in the literature for high-speed permanent magnet motors. In this context, this paper employs an innovative approach, utilizing the Taguchi method, the National Ordnance Laboratories (NOL) ring failure test, and a resin content test, to investigate the winding process parameters of carbon fiber composite protective sleeves for high-speed permanent magnet motors. The results indicate that the tensile strength of the composite sleeve with a winding angle of 45 and 30° is reduced by 20~25% compared with the sleeve with a pure hoop winding angle. The loss of strength caused by the manufacturing process accounts for 30%. When the wet winding tension is 130 N, the tensile and shear strength of the sleeve decreases by 13 and 12%, respectively, compared with that of 70 N. When the dry winding temperature rise rate is 3 min/°C, the strength of the sleeve decreases by 16% compared with that of 1 min/°C. For a small-thickness sleeve with a thickness of 1.5 mm, two-layer curing can increase the tensile strength of the sleeve, while three-layer curing or more can decrease the strength. By studying the process parameters of the sleeve, this article prepared a composite sleeve for a high-speed permanent magnet motor with a line speed of 200 m/s. This study provides a comprehensive analysis of the manufacturing process parameters of the rotor sleeve, which is helpful for the design and performance optimization of high-speed permanent magnet motors.