Quality Of Parts Research Articles

A Comparative Study on the Muscle and Gut Microbiota of Opsariichthys bidens from Rice Field and Pond Culture Breeding Modes.

To investigate difference in the quality of the different parts (back, tail muscles, and fish skin) of Opsariichthys bidens from pond and rice field cultures, a comparative study was conducted in terms of nutritional composition, volatile flavor profiles and gut microbiota. In detail, the texture, free amino acids, fatty acids were further assessed. The results suggested that the moisture content, crude protein and crude fat content in the skin of O. bidens are higher than those in the back and tail muscles, regardless of breeding modes. The fish cultured in the rice field had a higher protein content than those from the pond culture, while the fat content of the rice field-cultured fish was significantly low compared to the fish from the pond culture, especially in the back and tail parts. A total of 43 volatile components were detected by Gas Chromatography-Mass Spectrometry (GC-MS), with a maximum of 18 types of aldehydes and the highest concentration being nonanal. Compared to pond cultures, the fish from the rice field cultures showed more abundant flavor composition and odor-active compounds. The total content of DHA (Docosahexaenoic Acid) and EPA (Eicosapentaenoic Acid) in the rice field-cultured fish was higher than that of the pond group, while no significant disparity in amino acid composition was observed (p > 0.05). Comparative and clustering analyses of gut microbiota revealed notable discrepancies in the gut microbiota of O. bidens from two aquaculture systems. However, an inherent correlation between the gut microbiome and meat quality would be further emphasized in further studies. This study can offer a theoretical reference for the development of high-quality aquatic products by selecting the appropriate aquaculture models.

Corrosion Evaluation of Metal Binder-Jetted Stainless Steel Parts: Influence of Printing Parameters on Performance

Additive manufacturing is a rapidly expanding fabrication process in both research and industry, capable of constructing parts layer by layer from a 3D CAD model. ASTM recognizes seven distinct additive manufacturing processes, with the majority capable of producing metal components. Among these technologies, binder jetting stands out, offering the potential to create a variety of metallic parts for applications in the biomedical and energy sectors at reduced costs and shorter lead times. Achieving acceptable corrosion resistance is crucial for such applications. Binder jetting exhibits advantages such as relatively high part density (~99%) and good surface roughness (~6μm for as-sintered parts), making it an ideal candidate for investigating corrosion behavior, given the general correlation between low surface roughness, high density, and improved corrosion performance. Despite its promise, binder jetting lacks standardized procedures for producing parts with specific properties, necessitating users to determine parameters through trial and error. The corrosion behavior of parts created through binder jetting remains uncertain due to various influencing parameters. This study focuses on two key printing factors: layer thickness and binder saturation, chosen due to their impact on surface roughness, density, and thereby on corrosion. Existing literature suggests that increased layer thickness may reduce powder bed density, potentially affecting green part density and causing more significant shrinkage during sintering. Simultaneously, binder saturation is recognized as a critical factor influencing final part quality. Predicting the combined impact of layer thickness and binder saturation on corrosion performance proves challenging. To unravel this relationship, we conducted experiments using an ExOne Innovate Plus metal binder jetting machine and 316L stainless steel metal powder (mean powder size of 22μm) which is widely recognized as the most common powder in research. A number of test runs were conducted by varying layer thickness and binder saturation, offering insights into their nuanced interplay in the context of corrosion performance. Initially, to address insufficient green strength, printer drying time and curing cycle time were optimized by conducting a series of experiments changing the drying time and curing time. Subsequent printing involved changing binder saturation parallel to the layer thickness variations, resulting in distinct parts with varying properties. Then the predetermined curing cycle was executed, followed by de-powdering, and sintering in a furnace. Sintering is imperative to increase the part density and enhance the overall strength of the final product. However, it's essential to acknowledge that sintering may adversely impact corrosion resistance due to the remaining porosity and surface fissures. The choice of sintering profile becomes crucial, necessitating careful consideration to minimize elemental migration during sintering and mitigate porosity. Scanning electron microscopy and optical microscopy are used to analyze sintered parts and interpret corrosion mechanisms. Electrochemical tests were conducted to identify corrosion rates, corrosion potential, passivation behavior, pitting potential, etc. in contrast with conventionally manufactured 316L in M NaCl solution. This study offers insights and recommendations aimed at enhancing the corrosion resistance of binder jetted stainless steel parts.

Construction and Analysis of Cutting Force and Stability Prediction Model in CNC Milling Thin-Walled Parts Machining

Thin-walled parts are widely used in precision instruments, automobiles and other fields for their high strength, light weight and other characteristics. However, in the process of CNC milling, as the thickness of the parts decreases, their rigidity gradually decreases, resulting in the machining process is very easy to produce vibration, and even chatter, which seriously affects the accuracy and surface quality of the parts. Traditional manufacturing methods usually use conservative cutting dosage to reduce vibration, but this greatly limits the performance of CNC machine tools and cutting tools. In order to solve this problem, this paper thoroughly investigates the dynamic characteristics of the cutting process of thin-walled parts, and constructs a cutting force and stability prediction model. Through a combination of theoretical analysis, experimental verification and numerical simulation, this paper comprehensively analyses the impact of cutting parameters, tool geometry, workpiece material properties and other factors on cutting force and machining stability. The research results not only help to improve the cutting theory of thin-walled parts, but also provide an important technical support for the realisation of high-efficiency and high-precision machining. The results of this paper show that by optimising the cutting parameters and tool geometry, the cutting force can be effectively reduced and the machining stability can be improved, so as to achieve efficient and high-precision machining of thin-walled parts. In addition, this paper also explores the causes and mechanisms of vibration in the machining process, providing a theoretical basis for the development of targeted vibration control measures. The research in this paper is important for promoting the development of thin-walled parts machining technology, not only helps to improve the processing efficiency and quality of parts, but also helps to promote the manufacturing industry to a more efficient, more precise direction.

Wire arc additive manufacturing of ZL205A: Heat input effect on the forming quality, pore defects and mechanical properties

The magnitude of heat input (HI) plays a pivotal role in determining the deposition quality of wire arc additive manufacturing (WAAM) components. This article studies the effects of high and low HI on the forming quality, microstructure, pore defects, and mechanical properties of ZL205A parts, providing a theoretical foundation for choosing optimal HI parameters in the utilization of WAAM technology to fabricate ZL205A structural components. In this study, four thin-walled walls, SP1, SP2, SP3, and SP4, were manufactured with HI of 300.12, 200.08, 163.70, and 138.51 J/mm, respectively. The findings show that the SP4 has the least side forming error, and the maximum deposition efficiency, resulting in the best forming quality. From microstructural studies，SP4 has the lowest average grain size, 72μm. The quantity of θ phases (Al2Cu) on the grain boundary and the quantity and size of θ' phases in the middle and bottom grains both diminish with decreasing HI. As the HI decreases, the porosity of the four samples shows a trend of first decreasing and then increasing, with the highest being 1.41 % of the SP1. This change is explained from the four stages of bubble nucleation, growth, detachment, and escape. Reducing HI can significantly improve the ultimate tensile strength (UTS), and yield strength (YS) of the specimen. The SP4 has the highest UTS and YS, which are 232.99 MPa and 101.20 MPa, respectively.

Critical state-of-the-art literature review of surface roughness in incremental sheet forming: A comparative analysis

Incremental sheet forming (ISF) is a controlled and die-less forming process that can produce customized products from the sheets layer by layer using the same setup, i.e., forming tools and machine. This novel and emerging forming technique can potentially be useful as a green aspect of manufacturing by reducing the required power and by saving the resources for producing the components of lightweight materials because material is deformed locally during ISF. The surface quality of the formed parts from sheet material affects the aesthetics aspects, stress concentration, fatigue life and their applicability. This review article aims to provide a state-of-the-art review for various aspects and parameters of ISF process that affect the surface roughness significantly by conducting literature survey quantitatively. Furthermore, the techniques of ISF and roles of various parameters responsible for affecting the surface quality of parts have also been explored for providing the critics for existing literature and setting the further guidelines for the researchers. Various instruments used for measuring the surface roughness of parts formed by ISF has also been discussed. Comparative analysis of exiting literature available in the context of various process parameters, ISF hardware, and surface roughness makes this study comprehensive and exhaustive for the researchers to find the gaps and challenges in this field. The relationship of wall angle with other process parameters can be explored in the future to obtain the desired surface quality of a particular material. Significant parameters responsible for the surface quality have also been discussed critically. Further, the new tool paths can be developed for high surface quality and low forming time. A study on the selection of feed rate for various applications and relation with other parameter is also recommended for the future work. The lubricants with additives can further be tested for high surface quality of ISF. Further, significant study on surface roughness is not conducted on hybrid sheet forming and other advanced variants of ISF (like hot forming, friction stir assisted incremental forming, waterjet forming). It is required to develop the comparisons between the different techniques of ISF. Results also showed that majority of researchers used SPIF technique followed by DSIF and hybrid ISF.

High-speed synchrotron X-ray imaging of melt pool dynamics during ultrasonic melt processing of Al6061

Ultrasonic processing of solidifying metals in additive manufacturing can provide grain refinement and advantageous mechanical properties. However, the specific physical mechanisms of microstructural refinement relevant to laser-based additive manufacturing have not been directly observed because of sub-millimeter length scales and rapid solidification rates associated with melt pools. Here, high-speed synchrotron X-ray imaging is used to observe the effect of ultrasonic vibration directly on melt pool dynamics and solidification of Al6061 alloy. The high temporal and spatial resolution enabled direct observation of cavitation effects driven by a 20.2 kHz ultrasonic source. We utilized multiphysics simulations to validate the postulated connection between ultrasonic treatment and solidification. The X-ray results show a decrease in melt pool and keyhole depth fluctuations during melting and promotion of pore migration toward the melt pool surface with applied sonication. Additionally, the simulation results reveal increased localized melt pool flow velocity, cooling rates, and thermal gradients with applied sonication. This work shows how ultrasonic treatment can impact melt pools and its potential for improving part quality.

Multi-objective cell configuration considering part quality and machine compatibility

Multi-objective cell configuration considering part quality and machine compatibility

Mitigating domain shift in online process monitoring for material extrusion additive manufacturing via transfer learning

The additive manufacturing (AM) method has experienced rapid growth in recent decades. However, its application in end-use products is constrained by printing defects. Therefore, online process evaluation and optimization are crucial for producing parts that meet quality standards. Data-driven methods have been extensively employed for in-situ process evaluation and control in various AM processes to establish correlations among process parameters, process information, and part quality. However, a critical weakness of these methods is their poor generalization, primarily due to two factors: the diverse nature of printing itself and the significant influence of environmental factors. This study proposes a framework for in-situ process evaluation and control of AM using transfer learning to address the domain shift problems. It highlights a significant domain shift issue in AM monitoring, especially when using deep learning models. A comprehensive monitoring system for material extrusion additive manufacturing is constructed to collect data from different domains, emphasizing the notable differences in data characteristics across these domains. Lastly, transfer learning methods are tested to tackle the domain shift issue in AM, enhancing the robustness and reliability of the monitoring system.

Redefining Neural Architecture Search of Heterogeneous Multinetwork Models by Characterizing Variation Operators and Model Components.

With neural architecture search (NAS) methods gaining ground on manually designed deep neural networks-even more rapidly as model sophistication escalates-the research trend is shifting toward arranging different and often increasingly complex NAS spaces. In this conjuncture, delineating algorithms which can efficiently explore these search spaces can result in a significant improvement over currently used methods, which, in general, randomly select the structural variation operator, hoping for a performance gain. In this article, we investigate the effect of different variation operators in a complex domain, that of multinetwork heterogeneous neural models. These models have an extensive and complex search space of structures as they require multiple subnetworks within the general model in order to answer different output types. From that investigation, we extract a set of general guidelines whose application is not limited to that particular type of model and are useful to determine the direction in which an architecture optimization method could find the largest improvement. To deduce the set of guidelines, we characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.

Comparative evaluation of insulation cotton configurations on the investment casting quality of industrial valve parts

Comparative evaluation of insulation cotton configurations on the investment casting quality of industrial valve parts

Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts

This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.

A Modeling Framework for the Thermoforming of Carbon Fiber Reinforced Thermoplastic Composites.

A comprehensive modeling framework for the thermoforming of polymer matrix woven laminate composite was developed. Two numerical indicators, the slip path length and traction magnitude, have been identified to be positively correlated to matrix smearing and wrinkling defects. The material model has been calibrated with picture-frame experimental results, and the prediction accuracy for intra-ply shear and thickness distribution was examined with measurements of the physically formed parts. Specifically, thickness prediction for most locations on the formed parts was accurate within an 11.6% error margin. However, at two points with significant intra-ply shear, the prediction errors increased to around 20%. Finally, a parametric study was conducted to determine the relationship between various process parameters and the quality of the formed part. For the trapezoidal part, orienting the laminate at 45 degrees to the mold axis reduces the likelihood of matrix smear and wrinkling defects. Although this laminate orientation yielded a greater spatial variation in part thickness, the thickness deviation is lower than that for the 0-degree orientation case. Two forming analyses were conducted with ramp rates of 25 mm/s and 80 mm/s to match the equipment's operational limits. It was observed that higher forming rates led to a greater likelihood of defects, as evidenced by a 15% and 10% increase in the formed part areas with longer slip paths and higher traction magnitudes, respectively. It was discovered that shallower molds benefit from faster ramp rates, while deeper molds require slower rates to manage extensive shearing, stretching and bending. Faster forming rates lead to smaller thickness increases at high intra-ply shear regions, indicating a shift from intra-ply shear to out-of-plane bending due to the visco-plastic effect of the molten laminate and can negatively impact part quality. Lastly, it was shown that a well-conceived strategy using darts could improve the part quality by reducing the magnitude of the defect indicators.

Advances in Laser Powder Bed Fusion of Tungsten, Tungsten Alloys, and Tungsten-Based Composites.

In high-tech areas such as nuclear fusion, aerospace, and high-performance tools, tungsten and its alloys are indispensable due to their high melting point, low thermal expansion, and excellent mechanical properties. The rise of Additive Manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (L-PBF), has enabled the precise and rapid production of complex tungsten parts. However, cracking and densification remain major challenges in printing tungsten samples, and considerable efforts have been made to study how various processing conditions (such as laser power, scanning strategy, hatch spacing, scan speed, and substrate preheating) affect print quality. In this review, we comprehensively discuss various critical processing parameters and the impact of oxygen content on the control of the additive manufacturing process and the quality of the final parts. Additionally, we introduce additive manufacturing-compatible W materials (pure W, W alloys, and W-based composites), summarize the differences in their mechanical properties, densification, and microstructure, and further provide a clear outlook for developing additive manufactured W materials.

A general inspection and replacement policy for protection systems subject to shocks with state dependent effect

This paper proposes a new maintenance policy for protection systems which fail due to competing causes: internal deterioration and fatal external shocks. The study is motivated by a real problem in a power distribution company, in which a circuit breaker is designed to protect an electrical network from critical events such as overloads and short circuits. The failure of the system is hidden and can lead to a disaster if it is not replaced before a demand is received. So, to improve its readiness, we have developed a new two-phase (inspection & replacement) policy for a protection system with a heterogeneous population. Heterogeneity reflects factors such as variability in the quality of spare parts or in the installation of the components, which can be poorly or properly executed, resulting in some having a short life, while others may achieve a long life. Inspections are imperfect, which induces misclassification. The deterioration process is modeled via the delay-time concept. Furthermore, the shock rate depends upon the state of the system and follows a non-homogeneous Poisson process. Thus, using a numerical approach, the objective of this study is to determine the number of inspections during the first phase, the interval between consecutive inspections, and the age for scheduled preventive replacement. We compared this policy to two other classical policies via a case study and found that it has significant cost-saving potential. Generally, our results indicate when inspections should be prioritized over age-preventive replacement, and vice versa. Multi-phase policies are particularly effective for components with different characteristic lives, with inspections being crucial for maintaining readiness, often requiring investment in quality assurance to prevent misclassification. Conversely, age-based policies are more advantageous for strong (long-lived) components or when the probability of misclassification is high.

Machine learning techniques for quality assurance in additive manufacturing processes

Additive manufacturing (AM) processes have revolutionized manufacturing industries by enabling the production of complex geometries with reduced material waste and lead times. However, ensuring the quality of AM parts remains a significant challenge due to the complexity of the process and inherent variability in material properties. This review investigates the use of artificial intelligence (AI) to enhance quality assurance in AM processes, focusing on specific machine learning techniques such as convolutional neural networks for defect detection, support vector machines for classification of material properties, and reinforcement learning for real-time process optimization. The AI-driven methodologies are applied to predict defects, optimize process parameters, and monitor real-time production quality, utilizing large datasets generated from sensors and in-situ monitoring systems. The study demonstrates significant improvements in the accuracy of defect detection, the reliability of material property classification, and the efficiency of process optimization. In addition, it addresses challenges such as data pre-processing, model interpretability, and integration with existing AM systems. The findings highlight the potential of AI to transform quality assurance in AM and outline future research directions for further integration and enhancement of AI techniques in AM.

Analysis of the Dimensional and Shape Accuracy and Repeatability of Models Produced in the Process of Additive Extrusion of Thermoplastic Polymers Using Fused Filament Fabrication Technology

To achieve high precision and repeatability of prototyped models, work is carried out to improve existing solutions and thus expand the areas of application of 3D printers. Ways to increase the efficiency of 3D printing are being sought. The subject of the work concerns the analysis of dimensional and shape accuracy and repeatability of samples produced by a technology based on layered extrusion of thermoplastic polymers using FFF—fused filament fabrication technology. The key parameter for assessing the quality of parts is the property most expected by 3D printer users regarding the accuracy of dimensions of manufactured prototypes. As part of the research, conclusions were drawn regarding the precision of the samples as original patterns. The dimensional accuracy in the x, y, and z axes of the 3D printer was determined in terms of the design of the 3D printer’s actuator system. Three-dimensional maps of deviations of the pattern surface were used in relation to the nominal 3D CAD model to test the accuracy. The analytical results obtained during the research work, together with the graphs of the normal distribution probability function, indicate a high repeatability for each of the axes, and the highest repeatability was for the z-axis. The dimensions in the remaining axes were within the assumed tolerance of 0.1 mm, except for the two extreme dimensions, which were caused by a different method of heat dissipation, proving the influence of the arrangement of samples on the working platform.

Pre- and in-process dimensional compensation in the selective thermoplastic electrophotographic process

As a cutting-edge additive manufacturing (AM) technology, the selective thermoplastic electrophotographic process (STEP) has opened up possibilities for mass production with its combination of real engineering plastics and potential high part quality. To improve the accuracy and fidelity of STEP for the most demanding applications, this paper proposes a novel method encompassing both pre-processing and in-process dimensional compensations. Iterative compensation before production is achieved through physics-driven simulation, resulting in input masks that better match the required dimensions at the design level. Layer-wise compensation is implemented during the production process through the laser profiler system, thereby suppressing the accumulation of surface unevenness during printing. With the compensation method proposed in this paper, the maximum distortion during simulated printing is decreased by 86.2%, and surface unevenness is effectively controlled during the printing process.

Experimental investigation and optimization of the effects of manufacturing parameters on geometric tolerances in additive manufacturing of AlSi10Mg alloy

While the quality of parts produced by additive manufacturing is generally evaluated by surface roughness, relative density, and mechanical properties, the issue of dimensional accuracy is not examined sufficiently. However, dimensional accuracy is very important for the final use and finishing of a product. Since the dimensional change mainly occurs due to shrinkage resulting from the heat energy applied during the sintering process, the effect of production parameters in the additive manufacturing method is quite large. To minimize shrinkage and increase dimensional accuracy, manufacturing parameters need to be optimized and meticulously examined. This study was aimed at determining the effects of manufacturing parameters on geometric tolerances in the production of parts using the additive manufacturing method. AlSi10Mg powder alloy and selective laser melting (SLM) technology were used in the additive manufacturing of this alloy in part production. Twelve different laser powers and scanning speeds, as well as fixed scanning range and layer thickness parameters, were used in production. In determining geometric tolerances, features such as hole diameter change, deviation from angularity, deviation from perpendicularity, deviation from flatness, and deviation from parallelism were taken into consideration. As a result of the study, deviation values increased in high and low laser power/scanning speed combinations. Minimum deviation amounts were obtained in the range of 250–310 laser power and 785–974 scanning speed, which are the middle values of the parameters used. The optimum values of different output responses have been obtained with different production parameters, but for the final use and quality control approval of the product, it is necessary to determine the input parameters at which all output responses are optimal. In this process, the gray relational analysis optimization method, which is one of the multi-criteria decision-making methods, was preferred. As a result of the optimization, the optimum manufacturing parameters for geometric tolerances were determined as the 290/911 laser power/scanning speed combination.

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

Optimization Strategy for Process Design in Rubber Injection Molding: A Simulation-Based Approach Allowing for the Prediction of Mechanical Properties of Vulcanizates.

Selecting the optimal settings for the production of rubber goods can be a very time-consuming and resource-intensive process. A promising method for optimizing rubber processing in a short period of time is the use of simulation routines. However, process simulations have only recently enabled meaningful predictions of not only the part's state of cure but also its mechanical characteristics. As a first approach, second-order polynomials were considered suitable for describing the properties of compression-molded parts. However, more precision is required for injection molding due to the narrower distribution of mechanical characteristics of parts produced at different vulcanization temperatures. This became evident when the approximation of mechanical data with second order models partly revealed significant failures of part behavior prediction. To tackle this issue, a combined approach for approximation is proposed in this contribution by means of logistic growth function in addition to second order polynomials. To feed the model, an experimental plan was designed for producing injection-molded parts from an SBR compound at various temperatures and to different degrees of cure. The parts obtained were then characterized mechanically, and the results were opposed to varying degrees of cure and extents of reaction to calculate the model coefficients. Once available, a simulation-based calculation of the mechanical part quality is possible. The comparison of test results from the simulation and the real process has shown a reliable prediction, as simulation results were found within the natural deviation of the real measurements.