Tuning Of Process Parameters Research Articles

Control polarity of gallium nitride on Si (1 1 1) using atomic layer annealing and thermal atomic layer deposition

Ga-polar GaN thin films and N-polar GaN thin films were fabricated by using thermal ALD and plasma assisted ALA processes, respectively. The polarity of GaN wurtzite thin films was determined by cross-sectional STEM analysis of as-deposited films and top-view SEM images after KOH wet-etching. The crystallinity and grain size of thermal ALD GaN were drastically increased with the GaN ALA buffer layer. The ALA Ar plasma pulse in each ALD cycle decreased the oxygen and carbon impurity content and improved the crystallinity of GaN thin films by removing organic ligands. High purity GaN thin films were successfully deposited on oxide-free Si (111) substrate by using the ALA process with 100 ms of TDMAGa with push gas, 3 × 800 ms of N2H4, and 15 s of 25 W ALA plasma pulse with −25 V stage DC bias at 275 °C. The lowest FWHM value (0.59°), smooth surface morphology (RMS=1.0 nm), and 10–20 nm grain size of columnar GaN thin films were obtained by tuning process parameters.

Multi-laser scan assignment and scheduling optimization for large-scale metal additive manufacturing

Metal Additive Manufacturing (AM) has attracted significant attention in various industry sectors for large-scale fabrication. However, the limited fabrication efficiency has hindered its practical implementation. In comparison to traditional methods of tuning process parameters, concurrent AM equipped with multiple independently driven lasers is a more promising technique recently developed for the efficient fabrication of large metal parts. To maximize fabrication efficiency while ensuring quality for a multi-laser AM processes, an optimization problem is proposed in this work for multi-laser scanning plan, including scan vector assignment and scheduling. The goal is to minimize the makespan while considering factors that may affect the quality of metal AM parts as constraints. Specifically, the constraints associated with heat-affected zones and the user-specified single-laser scanning area are considered. The optimization model is solved by Deep Reinforcement Learning (DRL), offering the flexibility to include or exclude considerations for different quality/process requirements. Two case studies demonstrate the application of DRL models considering different sets of constraints and compare their performance with two baseline scheduling methods in terms of fabrication efficiency and violation of quality constraints. In addition, the impact of the laser number on operational improvement and the computational cost of the DRL model is also studied.

Exploiting Signal Coherence to Simultaneously Detect and Locate Earthquakes

Abstract This study introduces an earthquake detection and location technique that exploits the spatial coherence of the seismic wavefield. The method leverages the signal coherence across clusters of seismic stations to generate characteristic functions that are backprojected (migrated) to detect and locate seismic events. The effectiveness of the technique is assessed using a limited set of stations from the Oklahoma wavefield experiment, with minimal tuning of processing parameters. The technique is then applied to one day of continuous data, leading to the detection of new seismic events compared with an analyst-based catalog. Noteworthy advantages of this method include its independence from prior information or assumptions (such as template waveform) and its ability to operate effectively with a network design for which seismometers are deployed in a relatively small number of clusters rather than distributed throughout a region.

Machine learning models in the process of metal organic chemical vapor deposition epitaxial manufacturing of Gallium Arsenide

Metal–organic chemical vapor deposition (MOCVD) is a vapor-phase epitaxial growth technique commonly used in industry and academia to manufacture high-quality Gallium Arsenide (GaAs) compound semiconductor materials. In the actual industrial GaAs MOCVD epitaxial production process, a large amount of process parameters, sensor data, epitaxial wafer testing data, etc. are generated. These epitaxial-growth big data have large sample size and high noise, making data mining increasingly difficult. Nevertheless, this indicates that these data contain abundant valuable information. Therefore, this study proposes a machine learning–based GaAs MOCVD growth model. By analyzing the GaAs epitaxial growth data generated during MOCVD of GaAs, a machine learning model is established to predict the optimal production process for epitaxial growth. By mining the relationship between variables and developing a predictive model for assessing the performance of epitaxial materials, important guidance can be provided for process parameter tuning of GaAs epitaxial growth, considerably reducing debugging costs and further improving the intelligence level of GaAs MOCVD epitaxial growth.

Site-specific Cu clustering and precipitation in laser powder-bed fusion 17–4 PH stainless steel

Thermal gyrations inherent to metal additive manufacturing (AM) result in complex microstructural heterogeneities that are linked to the process parameters, geometry, and alloy composition. AM 17–4 precipitation hardened (PH) stainless steel displays heterogeneities due to its multi-phase microstructure where different as-built phases have been reported. These phases include austenite, martensite, and ferrite, as well as varying degrees of AM induced Cu clustering and/or precipitation. To enable a systematic study of the evolution of these strengthening phases, we utilize a non-standard concentric scan strategy to achieve variations in thermal conditions across a build. Using atom probe microscopy and thermal modeling, we show that the response of Cu varies across the build from no clustering at the outer regions to an increased tendency for Cu precipitation towards the center. Our results will enable the tuning of processing parameters to achieve homogenous Cu precipitation, and thus improve mechanical properties of AM 17–4 PH.

Experimental analysis of overlap fiber laser welding for aluminum alloys: Porosity recognition and quality inspection

In recent years, laser welding has become increasingly popular in the manufacturing industry due to its advantages, including a narrow heat affected zone, low levels of distortion, the possibility of remote processing, and high welding speeds compared to conventional electric arc welding techniques. In the automotive industry, the need for overlapped joint welding, particularly for chassis assembly, has become increasingly relevant. The internal porosity detection and control in laser welding of aluminum alloys has gained significant attention. To support laser users in optimizing the welding process, this paper presents an experimental methodology followed by a phenomenological analysis of the quality of overlapped welded joints. The study focuses on the laser welding of two different aluminum alloy configurations (1.6-mm-thick AA 6061-T6 and 2-mm-thick AA 6061-T6) using three welding strategies (ScanLab remote laser welding system, Trumpf D70 laser head, and Precitec YW52 wobbling head) to evaluate single and multiple laser variants for process parameter tuning. Additionally, the paper discusses the use of X-ray technology as an offline monitoring method for porosity recognition, and analyzes laser beam characterization and beam profile shape to calculate beam distribution. Then, statistical analysis of all methods is conducted and regression analysis is performed. These findings provide valuable insights into the integration of laser welding technology in the automotive and surface transportation industries. The analysis of results indicates that as the spot size decreases, the real spot size changes more abruptly with increasing Z position, whereas with a larger spot size, the beam size remains stable up to +/- 20–30 mm in Z position due to the large lens. Porosity is mainly caused by either a too small spot size (in the absence of wobbling; ≤0.4 mm) or low travel speed (≤4.0–4.5 m/min). On the other hand, if the spot size is too large, hot cracking can occur in autogenous laser welding.

Simple regulation of dielectric pulsing properties of polyethylene glycol‐poly(vinylidene fluoride) coaxial nanofiber films based on blow‐spinning process

AbstractUnderstanding the relationship between the fabrication process and the dielectric behavior of thermo‐responsive dielectric materials and accordingly optimizing the dielectric response behavior through simple regulation is critical to advancing their practical applications. For this purpose, we systematically analyze and demonstrate the effects of blow‐spinning process parameters, the feed rate and airflow pressure, on the orientation of poly(vinylidene fluoride) (PVDF) chains, the individual phase fractions of PVDF, the structure of the core‐sheath nanofibers, the mechanical properties, and the dielectric pulsing properties in polyethylene glycol (PEG)‐PVDF core‐sheath fiber films, and achieve the goal of effectively regulating the thermo‐responsive dielectric behavior. In particular, by increasing the airflow pressure, the polar phase fraction of the PVDF can be effectively increased, thus strengthening the dielectric pulsing effect. The significant influence of the processing parameters on the nanofiber structure also allows us to tailor the strength and toughness of the nanofiber films over a wide range by adjusting the feed rate and airflow pressure. Our work shows that the regulation of feed rate and airflow pressure can be used as an effective strategy to optimize the dielectric pulsing and mechanical properties of PEG‐PVDF films, enabling a simple and low‐cost performance optimization of thermo‐responsive dielectric materials.Highlights Effective regulation of thermal dielectric behavior by tuning process parameters. Simple tuning of mechanical properties of nanofiber films over a wide range. High ratio of β‐ and γ‐phases of PVDF by solution blow spinning. Major polar phases contribute to improved mechanical and dielectric properties.

Evaluating the adhesion response of acrylonitrile-butadiene-styrene (ABS)/thermoplastic polyurethane (TPU) fused interface using multiscale simulation and experiments

This paper implements reactive forcefield molecular dynamics (MD) simulation to evaluate the mechanical performance of the fusion bond line formed between acrylonitrile butadiene styrene (ABS), and thermoplastic polyurethane (TPU). The simulated interfacial adhesion responses, as obtained from MD simulation, are further implemented as the interfacial properties between ABS and TPU in an upscaled cohesive zone-based finite element analysis (FEA) model for macroscopic response evaluation. Such upscaling enables fair comparison of the simulated macroscopic stress–strain response with experimental results obtained for 3D printed ABS/TPU hybrid samples with a fusion-bonded interface. Overall, the stress–strain response predicted from a FEA-based model shows a good correlation with the experimental data signifying good predictive efficacy of the simulation approach. Thus, by interactively linking the molecular structures of the polymers and the processing parameters with the macroscale interfacial adhesion response of the fusion bond line, the experimentally validated comprehensive approach presented in this paper paves the way for atomistic engineering of the molecular structures as well as efficient fine tuning of processing parameters to meet desired macro-scale performance needs besides enabling exploration of the mechanisms and atomistic origins of the interfacial damage at the fused bond line.

Roll-to-roll reactive ion etching of large-area nanostructure arrays in Si: Process development, characterization, and optimization

Roll-to-roll (R2R) nanofabrication processes are recognized as key enabling-technologies for many next-generation applications in flexible electronics, displays, energy generation, storage, as well as healthcare. However, R2R processing techniques reported in the literature currently lack a scalable method of performing high-throughput nanoscale pattern transfer of geometry requiring a high degree of fidelity in terms of critical dimension resolution, etch uniformity, and aspect ratio. Reactive ion etching (RIE) addresses the need for sub-10 nm pattern transfer with large-area uniformity in wafer-scale semiconductor manufacturing, but adapting plasma etch systems for use in R2R nanopatterning has proven to be nontrivial. Moreover, robust models for simulating R2R RIE do not exist, which is an obstacle to the creation of computational approaches to design, control, and scale-up of nanoscale R2R equipment and processes. To address these challenges, we demonstrate a process flow for fabricating Si nanopillar arrays utilizing a combination of nanoimprint lithography and RIE with all pattern transfer steps performed using a R2R plasma reactor system. Specifically discussed are process development details for etching imprint resist and Si including etch rates, cross-web etch uniformity, etch directionality, and etch selectivity at varying gas chemistries, powers, and pressures. 2k full-factorial Design of Experiments (DoEs) and ordinary least-squares regression analysis are also employed to study influence of process parameters on multiple outgoing etch quality characteristics and generate stochastic models of the R2R RIE pattern transfer process into Si. Utilizing these DOE-based models and desired targets for etch quality characteristics, we describe a bounded multivariate inverse-optimization scheme for automated etch process parameter tuning. The culmination of these efforts, to the best of the authors' knowledge, is the first reported RIE-based pattern transfer of 100 nm-scale features performed in continuous R2R fashion with control of feature geometry over large area. The methodology employed herein may be applied similarly to additional materials and geometries for future applications.

A New Manufacturing Methodology under Limited Machining Capabilities and Application to High-Performance Hemispherical Resonator

High-performance component production requires nearly homogeneous materials, ultra-precise machining and a high surface integrity. However, conventional design and manufacturing methodologies, which continuously increase the geometric tolerance to meet the performance requirements, lead to cost increases that even exceed the existing machining capabilities. This paper presents a general manufacturing methodology for high-performance components under realistic conditions, such as the current capability for material forming and machining. In order to achieve an excellent component performance, this methodology includes parameter extraction, sensitivity analysis, and trimming processes that consider the effect of material removal resolution and processing uncertainties. Simulations performed on the hemispherical resonator demonstrate how the proposed methodology is applied to fabricate extremely high-performance resonators. An efficient modal tuning strategy is proposed based on a sensitivity analysis of the tuning process parameters subject to material removal resolution and uncertainties. The tuning results prove the feasibility of this new methodology for high-performance components manufacturing. The elimination of the frequency split can finally be achieved with surface tuning iteratively. The proposed methodology provides an effective solution to achieve high-performance components under limited machining capabilities.

Model-based process optimization for mAb chromatography

Protein A affinity chromatography is an effective method for capturing and purification of monoclonal antibodies (mAbs), which are amongst the most important products in the biopharmaceutical industry. Being one of the most expensive steps of downstream purification, optimization of Protein A affinity chromatography towards higher productivity offers great potential for the reduction of production cost. Hence, this work presents the productivity optimization through four strategies of crude harvest loading in Protein A affinity chromatography. Loading strategies were optimized using a mathematical model and were compared on basis of their maximal productivities. It is theoretically shown, based on computational analysis, that the performance of existing classical batch processes can be optimized by implementing an improved loading step and fine tuning of process parameters without any additional investment in new or modified equipment, materials or energy. This approach offers an attractive alternative to existing capture steps and helps bridging a technological gap to new semi continuous processes that are still lacking sufficient reliability due to technical complexity. Increased productivity leads to lower amount of affinity resin demanded to process a given amount of crude harvest or to reduce the processing time. With a new loading strategy, less expensive affinity resins may also become an effective alternative. Amongst four different loading strategies, the loading using flow ramp was predicted by model as the most promising one and the mAb binding dynamic at changing loading velocity was tested experimentally on five different affinity resins to validate model predictions.

Direct laser sintering for lunar dust control: An experimental study of the effect of simulant mineralogy and process parameters on product strength and scalability

Dust is a hazard for lunar surface operations, and controlling lunar dust is critical to the operation of a permanent lunar base. The cost of importing materials from earth necessitates methods of dust control which use only in-situ materials. One such method is direct sintering, a process by which areas of the lunar surface are melted or sintered to seal the surface. Previous trials of solar, microwave and radiant direct sintering have produced mixed results, but direct laser sintering (DLS) offers a viable alternative. Lasers can be focussed to very high intensities, so a system power of a few hundred watts would be sufficient for DLS. In this study, almost 900 trials of DLS using a CO2 laser were conducted to investigate the influence of processing parameters and lunar dust simulant type on the physical and mechanical properties of DLS crusts formed on compacted simulants. A process parameter tuning method was implemented to maximise product mass, density, flexural strength and production rates while minimising energy requirements. Thereafter, strategies were developed by which a sintered crust of unlimited dimensions could be produced. The potential advantages of screening regolith prior to DLS were considered, and the impact of simulant type on the process was investigated. From the study, it was concluded that DLS was a viable method of dust control for un-trafficked or lightly trafficked areas and could be implemented by a small robot that could be accommodated within the payload of proposed Commercial Lunar Payload Services. Therefore, DLS has the potential to be the first ISRU dust control measure to be implemented on the moon.

Evaluation of a laser powder bed fusion designer Al-Mg-Zr-Si alloy for cold spray additive manufacturing

Metal additive manufacturing (AM) is a constantly developing approach utilized in many novel applications. Most metal AM techniques utilize alloys designed for traditional processing conditions, such as casting, whereas implementing alloys designed for AM may prove useful. Given the proven utility of the Al-Mg-Zr-Si Addalloy 5T® in melt-driven AM, this study investigated the use of this alloy in cold spray (CS), as minimal research has explored alloy design for use in solid-state processing. The as-atomized powder and CS samples were evaluated microstructurally and mechanically to determine suitability for CS. Premature brittle fracture were seen in the deposits, necessitating processing improvements for enhanced deposit performance. Feedstock thermal pre-processing was explored, guided by thermodynamic modeling, to improve spray-ability. Heat treatments resulted in generalized powder softening, suggesting improved processability using a CS gas-particle dynamics model. This model allowed for theoretical tuning of processing parameters, predicting improved deposition using a specific configuration with He gas. The through-process model-informed experimental approach emphasizes the potential of using Addalloy 5T® powder in CS when coupling heat treatment with processing parameter optimization. Results suggest that the CS community may benefit from alloy design specifically for solid-state processing, tailoring powder chemistry and microstructure for enhanced properties and processability.

Investigation of thermal influence on weld microstructure and mechanical properties in wire and arc additive manufacturing of steels

Alloy steels are commonly used in many industrial and consumer products to take advantage of their strength, ductility, and toughness properties. In addition, their machinability and weldability performance make alloy steels suitable for a range of manufacturing operations. The advent of additive manufacturing technologies, such as wire and arc additive manufacturing (WAAM), has enabled welding of alloy steels into complex and customized near net-shape products. However, the functional reliability of as-built WAAM products is often uncertain due to a lack of understanding of the effects of process parameters on the material microstructure and mechanical properties that develop during welding, primarily driven by thermal phenomena. This study investigated the influence of thermal phenomena in WAAM on the microstructure and mechanical properties of two alloy steels (G4Si1, a mild steel, and AM70, a high-strength, low-alloy steel). The interrelationships between process parameters, heating and cooling cycles of the welded part, and the resultant microstructure and mechanical properties were characterized. The welded part experienced multiple reheating cycles, a consequence of the layer-by-layer manufacturing approach. Thus, high temperature gradients at the start of the weld formed fine grain structure, while coarser grains were formed as the height of the part increases and the temperature gradient decreased. Microstructural analysis identified the presence of acicular ferrite and equiaxed ferrite structures in G4Si1 welds, as well as a small volume fraction of pearlite along the ferrite grain boundaries. Analysis of AM70 welds found acicular ferrite, martensite, and bainite structures. Mechanical testing for both materials found that the hardness of the material decreased with the increase in the height of the welded part as a result of the decrease in the temperature gradient and cooling rate. In addition, higher hardness and yield strength, and lower elongation at failure was observed for parts printed using process parameters with lower energy input. The findings from this work can support automated process parameter tuning to control thermal phenomena during welding and, in turn, control the microstructure and mechanical properties of printed parts.

Programmable Nanoarchitectonics of Pore Array for Electronic-Nose-Based Early Disease Diagnose

In this article, we present a novel electronic nose fabrication process based on highly programmable anodic aluminum oxide (AAO) nanoarchitectonics and ultrasonic spray pyrolysis (USP) deposition. Featuring an ultralow manufacturing cost, the deposited material’s morphology can be accurately controlled with fabricated general-purpose AAO template. Compared with nonstandard lithography-based template fabrication method, the need of complicated Bosch etching process and its associated complex process parameter tuning is eliminated. As a result, the cost-effective mass production of 3-D nanotemplate-based material and devices can be enabled. In addition, the target material’s limited coverage and time efficiency issues widely existing in the previous deposition methods are well-addressed by our customized USP deposition, especially for the 3-D nanotemplate with large surface-to-volume ratio, leading to significantly improved gas-sensing performance. Moreover, the proposed fabrication recipe, together with the adopted gas recognition algorithms based on linear discriminant analysis (LDA), is validated based on the reported extensive measurement results for five gas biomarkers widely exploited for patients’ exhaled gas-sensing and recognition applications. This shows great potential for the early disease diagnose of diabetes, breast cancer, acute lung injury, colon diseases, lung cancer, and so on.

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

Low-pressure die cast (LPDC) is widely used in high performance, precision aluminum alloy automobile wheel castings, where defects such as porosity voids are not permitted. The quality of LPDC parts is highly influenced by the casting process conditions. A need exists to optimize the process variables to improve the part quality against difficult defects such as gas and shrinkage porosity. To do this, process variable measurements need to be studied against occurrence rates of defects. In this paper, industry 4.0 cloud-based systems are used to extract data. With these data, supervised machine learning classification models are proposed to identify conditions that predict defectives in a real foundry Aluminum LPDC process. The root cause analysis is difficult, because the rate of defectives in this process occurred in small percentages and against many potential process measurement variables. A model based on the XGBoost classification algorithm was used to map the complex relationship between process conditions and the creation of defective wheel rims. Data were collected from a particular LPDC machine and die mold over three shifts and six continuous days. Porosity defect occurrence rates could be predicted using 36 features from 13 process variables collected from a considerably small sample (1077 wheels) which was highly skewed (62 defectives) with 87% accuracy for good parts and 74% accuracy for parts with porosity defects. This work was helpful in assisting process parameter tuning on new product pre-series production to lower defectives.

Supercritical CO2-assisted atomization for deposition of cellulose nanocrystals: an experimental and computational study

Nanoparticle spray deposition finds numerous applications in pharmaceutical, electronics, manufacturing, and energy industries and has shown great promises in engineering the functional properties of the coated parts. However, current spray deposition systems either lack the required precision in controlling the morphology of the deposited nanostructures or do not have the capacity for large-scale deposition applications. In this study, we introduce a novel spray system that uses supercritical CO2 to assist the atomization process and create uniform micron-size water droplets that are used as cellulose nanocrystal (CNC) carriers. CNCs are selected in this study as they are abundant, possess superior mechanical properties, and contain hydroxyl groups that facilitate interaction with neighboring materials. We fundamentally investigate the effect of different process parameters, such as injection pressure, gas-to-liquid ratio, the axial distance between the nozzle and substrate, and CNC concentration on the final patterns left on the substrate upon evaporation of water droplets. To this end, we show how tuning process parameters control the size of carrier droplets, dynamics of evaporation, and self-assembly of CNCs, which in turn dictate the final architecture of the deposited nanostructures. We will particularly investigate the morphology of the nanostructures deposited after evaporation of micron-size droplets that has not been fully disclosed to date. Different characterization techniques such as laser diffraction, polarized microscopy, and high-resolution profilometry are employed to visualize and quantify the effect of each process parameter. Numerical simulations are employed to inform the design of experiments. Finally, it is shown that the fabricated nanostructures can be engineered based on the size of the carrier droplets controlled by adjusting spray parameters and the concentration of nanoparticles in the injected mixture. Process parameters can be selected such that nanoparticles form a ring, disk, or dome-shaped structure. Moderate operational conditions, simplicity, and time efficiency of the process, and use of abundant and biodegradable materials, i.e., water, CNCs, and CO2 promote the scalability and sustainability of this method.

Tuning process parameters to optimize microstructure and mechanical properties of novel maraging steel fabricated by selective laser melting

Maraging steel is a promising material for additive manufacturing due to its ultrahigh yield strength, reasonable ductility and good weldability. However, the ductility of the fabricated part will be degraded after aging treatment. In this regard, we firstly designed a new composition of maraging steel Fe-18.3Ni-9Co-4.84Mo-0.92Ti-0.27Al-0.13Cr-0.01C (wt.%), whose strength and ductility can be simultaneously improved by selective laser melting. The relationship between laser process parameters and forming defects has been studied. Using single-track and single-layer experiments, fully dense parts were fabricated with a certain range of process parameters, corresponding to the 30–50% lap rate in the X–Y plane and 70% remelted rate along Z-axis. Besides, we found film-like reverted austenite along the martensite lath boundaries in the as-fabricated part. The effects of heat treatment processes on the reverted austenite and mechanical properties of the fabricated parts were also studied; the strength-ductility trade-off of maraging steel after heat treatment can be alleviated. The tensile strength and elongation of printed samples after direct aging treatment can respectively reach 2037 MPa and 6.4%, and 2182 MPa, 4.8% after solution and aging treatment.

Friction improvement via grinding wheel texturing by dressing

The economic and environmental cost of friction in machinery and transport is significantly remarkable, the machine tool being one of the most affected. In this sector, the movements are fully affected by the friction originated in the displacements between the guideways and the slides, which results in terms of precision, energy consumption and component deterioration. In this line, texturing has proved to be a useful tool to lessen friction. Nowadays, flaking is the technique in use for the texturing of machine tool guideways, a manual and costly process in which the final finish of the workpiece is not controllable. Other techniques like laser texturing constitute an effective and precise procedure which is at the same time expensive, requires fine tuning of process parameters and is not applicable in large workpieces. Therefore, the main challenge is to develop an industry-implemented technique capable of producing textures in a repetitive and economically viable way. In this work, a method that allows generating a wide range of controlled textures by grinding with wheels textured with an assisted dressing device is presented. With this technique it is possible to create textures in large workpieces, such as machine tool guideways and to do it in a repetitive way and in the same machine in which the guide has been ground previously, which involves a reduction in time and costs. The technique has been validated by means of tribological tests in which textures generated with this technique have obtained lower friction coefficients than flaking.

Influence of Elastomer Layers in the Quality of Aluminum Parts on Finishing Operations

In finishing processes, the quality of aluminum parts is mostly influenced by static and dynamic phenomena. Different solutions have been studied toward a stable milling process attainment. However, the improvements obtained with the tuning of process parameters are limited by the system stiffness and external dampers devices interfere with the machining process. To deal with this challenge, this work analyzes the suitability of elastomer layers as passive damping elements directly located under the part to be machined. Thus, exploiting the sealing properties of nitrile butadiene rubber (NBR), a suitable flexible vacuum fixture is developed, enabling a proper implementation in the manufacturing process. Two different compounds are characterized under axial compression and under finishing operations. The compression tests present the effect of the feed rate and the strain accumulative effect in the fixture compressive behavior. Despite the higher strain variability of the softer rubber, different milling process parameters, such as the tool feed rate, can lead to a similar compressive behavior of the fixture regardless the elastomer hardness. On the other hand, the characterization of these flexible fixtures is completed over AA2024 floor milling of rigid parts and compared with the use of a rigid part clamping. These results show that, as the cutting speed and the feed rate increases, due to the strain evolution of the rubber, the part quality obtained tend to equalize between the flexible and the rigid clamping of the workpiece. Due to the versatility of the NBR for clamping different part geometries without new fixture redesigns, this leads to a competitive advantage of these flexible solutions against the classic rigid vacuum fixtures. Finally, a model to predict the grooving forces with a bull-nose end mill regardless of the stiffness of the part support is proposed and validated for the working range.