Improved Part Quality Research Articles

Breaking the trade­off: multiscale optimization for lower cost, lower residual stress LPBF of SS316L

PurposeThe purpose of this study was to use bridge curvature method (BCM) to quantify stress, while multiscale modeling with adaptive coarsening predicted distortions based on experimentally validated models. Taguchi method and response surface method were used to optimize process parameters (energy density, hatch spacing, scanning speed and beam diameter).Design/methodology/approachLaser powder bed fusion (LPBF) offers significant design freedom but suffers from residual stresses due to rapid melting and solidification. This study presents a novel approach combining multiscale modeling and statistical optimization to minimize residual stress in SS316L.FindingsOptimal parameters were identified through simulations and validated with experiments, achieving an 8% deviation. This approach significantly reduced printing costs compared to traditional trial-and-error methods. The analysis revealed a non-monotonic relationship between residual stress and energy density, with an initial increase followed by a decrease with increasing hatch spacing and scanning speed (both contributing to lower energy density). Additionally, beam diameter had a minimal impact compared to other energy density parameters.Originality/valueThis work offers a unique framework for optimizing LPBF processes by combining multiscale modeling with statistical techniques. The identified optimal parameters and insights into the individual and combined effects of energy density parameters provide valuable guidance for mitigating residual stress in SS316L, leading to improved part quality and performance.

Optimization of Process Parameters and Additives for Improved Part Quality in Sand Casting: An Overview

Optimization of Process Parameters and Additives for Improved Part Quality in Sand Casting: An Overview

An intelligent scanning strategy (SmartScan) for improved part quality in multi-laser PBF additive manufacturing

An intelligent scanning strategy (SmartScan) for improved part quality in multi-laser PBF additive manufacturing

Tool Wear and Surface Roughness in Turning of Metal Matrix Composite Built of Al2O3 Sinter Saturated by Aluminum Alloy in Vacuum Condition.

Metal matrix composites (MMCs) are a special class of materials carrying combined properties that belongs to alloys and metals according to market demands. Therefore, they are used in different areas of industry, and the properties of this type of material are useful in engineering applications. Machining of such composites is of great importance to finalize the fabrication process with improved part quality. However, the process implies several challenges due to the complexity of the cutting processes and random material structure. The current study aims to examine machinability characteristics. Effects of turning a metal matrix composite built of Al2O3 sinter, saturated with an EN AC-44000 AC-AlSi11 alloy, are presented in this paper. In the present study, a turning process of new metal matrix composites was carried out to determine the state-of-the-art material for various engineering applications. During the turning process, the cutting forces, a tool's wear, and surface roughness were investigated. Further, the SEM (scanning electron microscope) analysis of cutting inserts was performed. The influence of MMC structure on the machining process and surface roughness was studied. The Al2O3 reinforcements were used in different graininess. Effects of conventional turning of the metal matrix composite with Al2O3 sinter of FEPA (Federation of European Producers of Abrasives) 046 and FEPA 100 grade were compared. Results analysis of these tests showed the necessity of continuing research on turning metal matrix composites built of an AlSi alloy and Al2O3 ceramic reinforcement. The study showed the properties of MMCs that influenced machinability. In this paper, the influence of feed rate's value on surface roughness was carried out. The significant tool wear during the turning of the MMC was proved.

Discrete Element Modeling (DEM) simulations of powder bed densification using horizontal compactors in metal additive manufacturing

Efficient packing of the powder bed plays a key role in ensuring high quality of parts printed with Powder Bed Fusion (PBF) based additive manufacturing technologies. This paper presents simulations and analysis of a new technique based on horizontal compactors for improved densification of the spread powder layer. Discrete Element Modeling (DEM) based simulation of this technique shows much improved bed densification and force reduction on the layer underneath, thereby contributing to improved part quality. Results are found to be in excellent agreement with past work. Simulations predict the void fraction distribution as a function of various process parameters. Simulations are carried out to understand the effect of number of particles on stresses acting on the previously printed layer. The technique discussed here is a general one, and may also help analyze other powder-based additive manufacturing processes such as selective laser sintering and binder jetting.

Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries

The quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-prone features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.

An Intelligent Scanning Strategy (Smartscan) for Improved Part Quality in Multi-Laser Pbf Additive Manufacturing

An Intelligent Scanning Strategy (Smartscan) for Improved Part Quality in Multi-Laser Pbf Additive Manufacturing

Tool wear, surface roughness, cutting temperature and chips morphology evaluation of Al/TiN coated carbide cutting tools in milling of Cu–B–CrC based ceramic matrix composites

Ceramics-based composites are a special class of materials carrying combined properties that belongs to alloys and metals according to market demands. This makes composites completely different and paves the way for new applications that requires the utmost properties. Machining of such composites is of great importance to finalize the fabrication process with improved part quality; however, the process implies several challenges due to the complexity of the cutting processes and random material structure. The current study aims to examine the machinability characteristics when milling novel material, Cu–B–CrC composites using Al/TiN coated carbide tools. Further, the influence of machining parameters along with the different weight ratios of the powders amounts used to fabricate the machined reinforced samples on output parameters namely surface roughness, tool wear, chip morphology and cutting temperatures was investigated. One of the key findings of the study is the dominant effect of reinforcement ratio (Cu, B, CrC) on machinability, which showed that 5% additive (2% B, 3% CrC) provides improved properties such as surface roughness, tool wear and cutting temperature. Cutting speed alterations play an important role in the machinability characteristics, i.e., increasing value increases flank wear and cutting temperatures and reduces surface roughness. Increasing feed rate increases the surface roughness meanwhile its effect shows changing behavior on the flank wear and cutting temperatures according to cutting speed and reinforcement ratio.

Introductory study of sheet metal forming simulations to evaluate process robustness

The ability to control quality of a part is gaining increased importance with desires to achieve zero-defect manufacturing. Two significant factors affecting process robustness in production of deep drawn automotive parts are variations in material properties of the blanks and the tribology conditions of the process. It is imperative to understand how these factors influence the forming process in order to control the quality of a formed part. This paper presents a preliminary investigation on the front door inner of a Volvo XC90 using a simulation-based approach. The simulations investigate how variation of material and lubrication properties affect the numerical predictions of part quality. To create a realistic lubrication profile in simulations, data of pre-lube lubrication amount, which is measured from the blanking line, is used. Friction models with localized friction conditions are created using TriboForm and is incorporated into the simulations. Finally, the Autoform-Sigmaplus software module is used to create and vary parameters related to material and lubrication properties within a user defined range. On comparing and analysing the numerical investigation results, it is observed that a correlation between the lubrication profile and the predicted part quality exists. However, variation in material properties seems to have a low influence on the predicted part quality. The paper concludes by discussing the relevance of such investigations for improved part quality and proposing suggestions for future work.

Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control

Multisensor data fusion can enable comprehensive representation of manufacturing processes, thereby contributing to improved part quality control. The effectiveness of data fusion depends on the nature of the input data. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize data correlation with part quality toward manufacturing process control. By decomposing sensor data into a lifted-dimensional space, contribution from each of the sensors for quantifying part quality is revealed by the corresponding projection vector. Performance evaluation using data measured from polymer injection molding confirmed the effectiveness of the developed technique.

Sensing Uncertainty Evaluation for Product Quality

As sensing techniques continue to enhance the manufacturing industry in terms of condition monitoring, process control and decision making, understanding uncertainty involved in sensing has remained a challenging problem. Sensor readings may be affected by deterioration of the hardware and environmental perturbations. Prior studies reported in the literature assumed multiple co-located homogeneous sensors to provide redundant information for uncertainty evaluation. Such an approach is often times not applicable given space restraint and cost-effectiveness concerns. Furthermore, it is difficult to distinguish the sources that have caused the variation of sensor readings, e.g., limitation in the sensor precision or variation in the measured quantity. To address these challenges, a method for computing uncertainty of non-homogeneous sensors is developed, using on-line injection molding quality monitoring as experimental verification. The method includes two computational steps. First, measurements provided by four spatially distributed sensors (two temperature and two pressure sensors) in an injection mould cavity are fused to estimate the part quality (thickness). Second, the ground truth is approximated from the estimated part quality, through inverse process. Subsequently, the approximated ground truth is utilized to calculate the uncertainty of each sensor used in the measurement process. To quantify sensor uncertainty, a metric including accuracy, precision and trust is defined. The detection of abnormal sensors in turn provides input to improved part quality estimation. The developed technique is evaluated experimentally by measurements on a production-grade injection molding machine working under a variety of machining settings. The results demonstrate the approach's effectiveness for evaluating sensing uncertainty and improving monitoring performance.

Methods of Improving Part Accuracy during Rapid Prototyping

Improving part quality during rapid prototyping is the important problem. The part errors during rapid prototyping is classified in dimension error, shaped error and roughness of surface. The shaped errors have many forms in fused deposition modeling (FDM), largely including warpage deformation, stair-stepping effect, and so on. There are several reasons for the errors, including principle, process and equipment. The reasons inducing these errors are analyzed, and the measures improving part accuracy are proposed

Optimization for Injection Molding Process Conditions of the Refrigeratory Top Cover Using Combination Method of Artificial Neural Network and Genetic Algorithms

The process conditions have important influence on final part quality in injection molding, and how to get optimum process conditions is the key to improving part quality. Sinkmarks on the surfaces of injection-molded parts is one of the problems that limit the overall success of injection molding technology, and the presence of sinkmarks significantly impairs the surface quality of injection molded parts. A combination method of artificial neural network and genetic algorithms is proposed to optimize the injection molding process, and the processing parameters of a refrigeratory top cover are optimized using the combining method to minimize the sinkmarks on the part. The results indicate the combining method is an effective tool for the process optimization of injection molding.

Study of ‘fingering’ in water assisted injection molded composites

Abstract Thin, plate-shaped parts of composite materials are usually reinforced with structural ribs. These ribs also serve as water channels in water-assisted injection molding technology. One of the problems encountered in water assisted injection molded composite parts is the ‘water fingering’ phenomenon, in which the water bubbles penetrate outside designed water channels and form finger-shape branches. Severe fingerings can lead to significant reductions in part stiffness. This study investigated the fingering phenomenon in water assisted injection molded composites. Experiments were carried out on a water-assisted injection molding system developed earlier in our lab. The materials used were short glass–fiber filled polypropylene composites. A plate cavity with a fish-bone water-channel layout was used for all experiments. The effects of various processing parameters on the fingering were studied. It was found that the water pressure, water injection delay time and melt short shot size were the principal parameters affecting the formation of water fingerings. In addition, the influence of water channel geometry, including aspect ratio and fillet geometry on the fingering were also investigated. The goal of this study is to gain a better understanding of the formation of water fingerings, so that steps can be taken to ensure that the fingering is minimized. This provides significant advantages in terms of improved part quality.

Machining Process Monitoring and Control: The State-of-the-Art

Research in automating the process level of machining operations has been conducted, in both academia and industry, over the past few decades. This work is motivated by a strong belief that research in this area will provide increased productivity, improved part quality, reduced costs, and relaxed machine design constraints. The basis for this belief is two-fold. First, machining process automation can be applied to both large batch production environments and small batch jobs. Second, process automation can autonomously tune machine parameters (feed, speed, depth of cut, etc.) on-line and off-line to substantially increase the machine tool’s performance in terms of part tolerances and surface finish, operation cycle time, etc. Process automation holds the promise of bridging the gap between product design and process planning, while reaching beyond the capability of a human operator. The success of manufacturing process automation hinges primarily on the effectiveness of the process monitoring and control systems. This paper discusses the evolution of machining process monitoring and control technologies and conducts an in-depth review of the state-of-the-art of these technologies over the past decade. The research in each area is highlighted with experimental and simulation examples. Open architecture software platforms that provide the means to implement process monitoring and control systems are also reviewed. The impact, industrial realization, and future trends of machining process monitoring and control technologies are also discussed.

A New Approach to Low Thermal Inertia Molding

Abstract Processing of polymers by the injection molding process has some inherent problems associated with the coupling of the filling and cooling processes. Kim and Suh [11 introduced the low thermal inertia molding (LTIM) concept to uncouple the filling process from the cooling process without appreciably increasing the total cycle time. A new injection mold was developed to control the surface temperature of the mold rapidly. The mold consisted of a woven graphite fiber fabric as a heating element, RTV silicon rubber as an adhesive, and a plasma-sprayed ZrO2 layer on the mold surface as an electrical insulation. This mold provided the means to show the potential of the low thermal inertia molding technique in terms of improved part quality, reduction in energy input due to lower requirement of injection temperature, injection pressure, packing pressure, and ease of multicavity mold balancing.