Search

With the imminent digitalisation of the manufacturing processes of gas turbine components, a large volume of geometric data of as-manufactured parts is being generated. This geometric data can be used in aerodynamic simulations to predict component performance. Both the cost and accuracy of these simulations increase with their fidelity. To efficiently exploit Digital Twin technology, one must therefore understand how realistic the aerodynamic simulations need to be to give useful performance predictions. This paper considers this issue for a sample of scrapped high-pressure turbine rotor blades from a civil aero engine. The measured geometric data was used to build aerodynamic models of varying degrees of realism, ranging from quasi-three-dimensional blade sections for an Euler solver to three-dimensional, multi-passage and multi-stage Reynolds-Averaged-Navier-Stokes models. The flow near the tip of these shrouded blades is sensitive to manufacturing variability and can switch between two quasi-stable horseshoe vortex modes. In general, capacity and exit flow angle can be adequately predicted by three-dimensional, single-passage calculations: averaging single-passage calculations gives a good prediction of the multi-passage behaviour. For efficiency and stage loading, the approach of averaging single-passage calculations is less accurate as the multi-passage behaviour requires an accurate prediction of the horseshoe vortex modes.

Purpose Eye plaques are widely used for ocular melanoma and provide an effective alternative to enucleation with adequate tumor control. A COMS plaque utilizes a Silastic insert for precise positioning of the radioactive seeds with respect to the scleral surface of the eye; however, due to manufacturing variability, the insert may unintentionally increase or decrease the distance between the sources and tumor. The purpose of this work is to provide guidance in measuring and identifying outliers in Silastic inserts. The importance of regular quality assurance (QA) is illustrated in an experience where a systematic problem was detected and the manufacturer's 22‐mm mold was corrected. Methods A detailed description of the molds and manufacturing process used to produce Silastic inserts is provided, including photographs of the process steps. The variability in Silastic insert production was evaluated by measuring the thickness of 124 Silastic inserts. An estimate of how the observed Silastic thickness discrepancies impact the dose to the tumor and critical eye structures was performed using homogeneous dose calculations. A standard QA protocol was developed to guide the clinical user. Results Thickness of the measured Silastic inserts ranged from 1.22 to 2.67 mm, demonstrating variation from the 2.25 mm standard. Six of the 22‐mm inserts were outliers (Δthickness >3 standard deviations) and were excluded from the statistics. The outliers were investigated with the help of the manufacturer, who discovered that a systematic error was accidentally introduced into the 22‐mm mold. Conclusions Due to manufacturing errors or variability, the Silastic inserts used in COMS eye plaques may be thicker or thinner than the design standard. Such variations may impact tumor control or increase the risk of normal tissue side effects. A standardized QA program is recommended to detect variations and communicate unusual findings to the manufacturer.

Aircraft designers are legally required to treat manufacturing variability so as to minimize the probability of structural failure, pursuant to Code of Federal Regulations (CFR) 25.613. The usual d...

We investigated the impact of the iSiPP50G platform photonic devices manufacturing variability on the spectral parameters of silicon ring resonators. The analysis was performed on 30 nominally identical chips of the same 200-mm wafer which integrating passive add-drop ring resonators with different coupling strengths.

Multiple human tissue engineered cartilage constructs are showing promise in advanced clinical trials but identifying important measures of manufacturing reproducibility remains a challenge. FDA guidance suggests measuring multiple mechanical properties prior to implantation, because these properties could affect the long term success of the implant. Additionally, these engineered cartilage mechanics could be sensitive to the autologous chondrocyte source, an inherently irregular manufacturing starting material. If any mechanical properties are sensitive to changes in the autologous chondrocyte source, these properties may need to be measured prior to implantation to ensure manufacturing reproducibility and quality. Therefore, this study identified variability in the compressive, friction, and shear properties of a human tissue engineered cartilage constructs due to the chondrocyte source. Over 200 constructs were created from 7 different chondrocyte sources and tested using 3 distinct mechanical experiments. Under confined compression, the compressive properties (aggregate modulus and hydraulic permeability) varied by orders of magnitude due to the chondrocyte source. The friction coefficient changed by a factor of 5 due to the chondrocyte source and high intrapatient variability was noted. In contrast, the shear modulus was not affected by changes in the chondrocyte source. Finally, measurements on the local compressive and shear mechanics revealed variability in the depth dependent strain fields based on chondrocyte source. Since the chondrocyte source causes large amounts of variability in the compression and local mechanical properties of engineered cartilage, these mechanical properties may be important measures of manufacturing reproducibility. Statement of significance Although the FDA recommends measuring mechanical properties of human tissue engineered cartilage constructs during manufacturing, the effect of manufacturing variability on construct mechanics is unknown. As one of the first studies to measure multiple mechanical properties on hundreds of human tissue engineered cartilage constructs, we found the compressive properties are most sensitive to changes in the autologous chondrocyte source, an inherently irregular manufacturing variable. This sensitivity to the autologous chondrocyte source reveals the compressive properties should be measured prior to implantation to assess manufacturing reproducibility.

This chapter will provide an overview of pragmatic trials in the biologic medicines setting. This chapter will start with an introduction to biologic medicines in terms of how they are defined, how they are broadly used in clinical medicine, and types of research questions typically studied in biologics using a pragmatic clinical trial design. A high-level summary of general pharmacovigilance concerns, such as immunogenicity, manufacturing variability, and identification/traceability is provided. Biologic-specific considerations will include topics such as the complexity of patient profiles and treatment. A biologic focus of pragmatic trial designs and their applicability to biologics, common endpoints, and strengths and limitations of real world data sources for pragmatic trials will be described. This chapter will be valuable to researchers and scientists who are interested in understanding considerations for conducting a pragmatic clinical trial of biologic medicines.

Variability in the manufacturing process (the coefficient of the OC curve), together with its counter measures, is the primary cause of long cycle times and reduced fab throughput. In an IC fab model, where substrate lots are processed through steps, and where each step is followed by a gap in processing, the wafer lots will spend more time in the accumulated transitions through the gaps than in-process. In these gaps non-linear growth of manufacturing variability occurs, through multiplication. Then, as this growth of variability is countered by stabilizing measures, such as wafer lot buffering and storage, cycle times are increased (and capacity reduced). An analysis of the above growth of variability is presented. Alternate product handling methods in the gap are studied to minimize this growth of variance. and so reduce fab cycle times (increase throughput).

To detect open defects of power TSVs (Through Silicon Vias) in PDNs (Power Distribution Networks) of stacked 3D-ICs, a method was proposed which measures resistances between power micro-bumps connected to PDN and detects defects of TSVs by changes of the resistances. It suffers from manufacturing variabilities and must place one micro-bump directly under each TSV (direct-type placement style) to maximize its diagnostic performance, but the performance was not enough for practical applications. A variability cancellation method was also devised to improve the diagnostic performance. In this paper, a novel middle-type placement style is proposed which places one micro-bump between each pair of TSVs. Experimental simulations using a 3D-IC example show that the diagnostic performances of both the direct-type and the middle-type examples are improved by the variability cancellation and reach the practical level. The middle-type example outperforms the direct-type example in terms of number of micro-bumps and number of measurements.

Some of the most prized woods used for the backs and sides of acoustic guitars are expensive, rare, and from unsustainable sources. It is unclear to what extent back woods contribute to the sound and playability qualities of acoustic guitars. Six steel-string acoustic guitars were built for this study to the same design and material specifications except for the back/side plates which were made of woods varying widely in availability and price (Brazilian rosewood, Indian rosewood, mahogany, maple, sapele, and walnut). Bridge-admittance measurements revealed small differences between the modal properties of the guitars which could be largely attributed to residual manufacturing variability rather than to the back/side plates. Overall sound quality ratings, given by 52 guitarists in a dimly lit room while wearing welder's goggles to prevent visual identification, were very similar between the six guitars. The results of a blinded ABX discrimination test, performed by another subset of 31 guitarists, indicate that guitarists could not easily distinguish the guitars by their sound or feel. Overall, the results suggest that the species of wood used for the back and sides of a steel-string acoustic guitar has only a marginal impact on its body mode properties and perceived sound.

Oftentimes, the complexity in manufacturing composite materials leads to corresponding structures which although they may have the same design specifications they are not identical. Thus, composite parts manufactured in the same production line present differences in their dynamics which combined with additional uncertainties due to different operating conditions may lead to the complete concealment of effects caused by small, incipient, damages making their detection highly challenging. This damage detection problem in nominally identical composite structures is pursued in this study through a novel data-based response-only methodology that is founded on the autoregressive with exogenous (ARX) excitation parametric representation of the transmittance function between vibration measurements at two different locations on the structure. This is a statistical time series methodology within which two schemes are formulated. In the first, a single-reference transmittance model representing the healthy structure is employed, while multiple transmittance models from a sample of available healthy structures are used in the second. The model residual signal constitutes for both schemes the damage detection characteristic quantity that is used in appropriate hypothesis testing procedures with the likelihood ratio test. The methodology is experimentally assessed via damage detection for a population of composite beams which are manufactured in the same production line representing the half of the tail of a twin-boom unmanned aerial vehicle. The damage detection results demonstrate the superiority of the multiple transmittance models based scheme that may effectively detect damages under significant manufacturing variability and varying boundary conditions.

The main objective of this research is to predict burst pressure of composite pressure vessels subjected to internal pressure taking into account manufacturing uncertainties. Firstly, first-ply-failure (FPF) of composite pressure vessels with/without liner is studied comparing performance of different failure criteria. Then, burst pressure of the vessels are deterministically predicted using progressive damage modeling based on continuum damage mechanics approach. Both theoretical modeling approaches on predicting FPF and burst pressure are validated using available experimental data. Finally, stochastic modeling is conducted to estimate burst pressure of composite pressure vessels taking into account fiber volume fraction, winding angle and mechanical and strength properties as random parameters resembling manufacturing-induced inconsistencies. Statistical data analysis shows the importance of taking into consideration manufacturing variability.

International audience; Data centers are energy-hungry facilities. Building energy consumption predictive models for servers is one of the solutions to use efficiently the resources. However, physical experiments have shown that even under the same conditions, identical processors consume different amount of energy to complete the same task. While this manufacturing variability has been observed and studied before, there is lack of evidence supporting the hypotheses due to limited sampling data, especially from the thermal characteristics. In this article, we compare the power consumption among identical processors for two Intel processors series with the same TDP (Thermal Design Power) but from different generations. The observed power variation of the processors in newer generation is much greater than the older one. Then, we propose our hypotheses for the underlying causes and validate them under precisely controlled environmental conditions. The experimental results show that, with the increase of transistor densities, difference of thermal characteristics becomes larger among processors, which has non-negligible contribution to the variation of power consumption for modern processors. This observation reminds us of re-calibrating the precision of the current energy predictive models. The manufacturing variability has to be considered when building energy predictive models for homogeneous clusters.

Modern high pressure turbine (HPT) blade design stands out due to its high complexity comprising three-dimensional blade features, multi-passage cooling system (MPCS) and film cooling to allow for progressive thermodynamic process parameters. During the last decade, probabilistic design approaches have become increasingly important in turbomachinery to incorporate uncertainties such as geometric variations caused by manufacturing scatter and deterioration. Within this scope, the first part of this two-part paper introduces parametric models for cooled turbine blades that enable probabilistic FE analysis taking geometric variability into account to aim at sensitivity and robustness evaluation. The statistical database is represented by a population of more than 400 blades whose external geometry is captured by optical measurement techniques and 34 blades that are digitized by computed tomography (CT) to record the internal geometry and the associated variability, respectively. Based on this data, parametric models for airfoil, profiled endwall (PEW), wedge surface (WSF) and MPCS are presented. The parametric airfoil model which is based on traditional profile theory is briefly described. In this regard, a methodology is presented that enables to adapt this airfoil model to a given population of blades by means of Monte-Carlo based optimization. The endwall variability of hub and shroud are parametrized by radial offsets that are applied to the respective median endwall geometry. WSFs are analytically represented by planes. Variations of the MPCS are quantified based on the radial distribution of cooling passage centroids. Thus, an individual MPCS can be replicated by applying adapted displacement functions to the core passage centroids. For each feature that is considered within the present study, the accuracy of the parametric model is discussed with respect to the variability that is present in the investigated blade population and the measurement uncertainty. Within the scope of the second part of this paper (cf. Högner et al. [1]), the parametric models are used for a comprehensive statistical analysis to reveal the parameter correlation structure and probability density functions (PDFs). This is required for the subsequent probabilistic finite element analysis involving real geometry effects.

Semiconductor manufacturing is a race for cycle time. Variability in the manufacturing process causes factory cycle times 2–3 times longer than the true front end processing time for an IC. Reducing variability is the key to efficient Fab operations. This two part study examines variability contributions of the inter process logistics. Manufacturing variability at the individual tool levels is amplified by the variability in the linking of process steps with AMHS. A stochastic simulation model of abstract substrate flow is constructed for: a) based on the current paradigm of inter process logistics, and b) based on that of a new hybrid AMHS concept. Fab OC curves are developed, highlighting differences in operating curves, which show the manipulating power of the AMHS on manufacturing process variability, and gains in cycle time via Hybrid AMHS.

This research is to develop the dyeing parameter optimization model for functional textiles based on the analysis of relationship between the manufacturing parameters in the dyeing process and the dyeing performance. The aim of this research is to minimize the total dyeing cost including the production and energy costs with the consideration of robustness measure and dyeing performance. The first task of this research is to analyze the relationship between the dyeing parameters and dyeing performance by the Central Composite Design (CCD). The result of the CCD is the estimated response surface for the use of our second task of the dyeing parameter optimization to search for the optimal combination of dyeing parameters with a robust performance against the manufacturing variability.

This paper presents an alternative to conventional antifouling methods through the projection of ultraviolet (UV) irradiation onto submerged surfaces. Tests carried out establish that UV irradiation can effectively prevent biofouling growth on a small scale using commercially available technology with low cost. The demonstration for antifouling purposes also indicates the potential for ultraviolet irradiation to be applied to many different surfaces and diverse applications across the maritime industry. It is demonstrated that intermittent UV can achieve successful antifouling results on submerged surfaces. This significantly increases the design versatility for this antifouling approach, without the need for constant UV exposure there is inherently greater design flexibility for such applications. This suggests the possibility of in-situ UV antifouling systems which can be deployed when vessels are stationary. UV intensity measurements carried out suggest manufacturing variability exists between light emitting diodes (LEDs) of the same specification. The large variation in light intensity observed indicates that devices may perform below the indicated manufacturer's specifications and this could have a detrimental effect on antifouling performance. Results indicate that minor shadowing from direct UV irradiation exposure can impede antifouling performance. Tests indicate prolonged UV LED exposure is likely to result in the photo-degradation of polymer based materials.

This paper examines the impact of measured manufacturing variability on the three-dimensional aerodynamic performance of a high-pressure turbine (HPT) rotor. Blade geometries were measured at the end of a modern aviation gas turbine manufacturing line. The aerodynamic assessment was performed using single-and multi-passage three-dimensional Reynolds-Averaged Navier Stokes calculations. Analysis of the sampled blades demonstrates that for the lower half of the HPT rotor blade, the variability introduces linear changes in both the single-passage and multi-passage calculations. However, changes in the structure of the secondary flow near the tip of the HPT rotor blade is observed in some cases and causes non-linear variations in performance. Thus, the performance of the entire rotor row is not always a simple average of the performances of the individual blades that make up the row.

Modern CPUs suffer from performance and power consumption variability due to the manufacturing process. As a result, systems that do not consider such variability caused by manufacturing issues lead to performance degradations and wasted power. In order to avoid such negative impact, users and system administrators must actively counteract any manufacturing variability. In this work we show that parallel systems benefit from taking into account the consequences of manufacturing variability when making scheduling decisions at the job scheduler level. We also show that it is possible to predict the impact of this variability on specific applications by using variability-aware power prediction models. Based on these power models, we propose two job scheduling policies that consider the effects of manufacturing variability for each application and that ensure that power consumption stays under a system-wide power budget. We evaluate our policies under different power budgets and traffic scenarios, consisting of both single- and multi-node parallel applications, utilizing up to 4096 cores in total. We demonstrate that they decrease job turnaround time, compared to contemporary scheduling policies used on production clusters, up to 31% while saving up to 5.5% energy.

This paper presents an analysis of the impact of manufacturing variability in PV modules when interconnected into a large PV panel. The key enabling technology is a compact semi-empirical model, that is built solely from information derived from datasheets, without requiring extraction of electrical parameters or measurements. The model explicits the dependency of output power on those quantities that are heavily affected by variability, like short circuit current and open circuit voltage. In this way, variability can be included with Monte Carlo techniques and tuned to the desired distributions and tolerance. In the experimental results, we prove the effectiveness of the model in the analysis of the optimal interconnection of PV modules, with the goal of reducing the impact of variability.

Many engineering structures are built by assembly of plates, and in recent years there has been a widespread use of composite plates, and in particular of orthotropic plates, to improve dynamic structural performance and reduce structural weight. Depending on the manufacturing process used, orthotropic plates might be affected by variability of the mechanical properties, which in turn will affect their performance. The four elastic constants of orthotropic plates can be obtained via direct static or dynamic tests, or via inverse methods which combine dynamic experiments with mathematical models. However these approaches may not be appropriate for establishing manufacturing variability across a number of manufactured plates, since they might require expensive testing equipment and they may be time consuming. In this paper an inverse procedure based on the Chladni patterns is investigated to analyse thin orthotropic rectangular plates. A detailed mathematical derivation of the equations needed for estimating the elastic constants based on the knowledge of five natural frequencies is presented. The repeatability and reproducibility of the results obtained with the proposed procedure is first assessed, and the evaluation of the manufacturing variability of an ensemble of "nominally identical" plywood plates with the proposed approach is then investigated.

Fiber reinforced composite structures require extensive experimental evaluation for validation due to their heterogeneous properties and manufacturing variability. A scaled model replicating the structural characteristics of its full-scale parent structure facilitates and expedites the assessment of the mechanical performance of large composite structures. This study primarily investigates the problems associated with the design of a scaled model and its similarity to its full-scale parent structure when there is uncertainty in the design parameters. A successful design should be robust to its assumptions and the sources of uncertainty. In this study, scaled-down composite I-beams are designed from their reference full-scale I-beam representing the spar caps and the shear web structure inside a utility-scale wind turbine blade. Similitude analysis is used in conjunction with Info-Gap theory to design scaled composite I-beams under uncertainty in the design parameters. The scaling laws for the strain field of the composite I-beam are derived and used as a metric to design scaled models that are robust to the uncertainties in the design parameters. The range of influence of uncertainty in different design parameters is investigated. The effect of uncertainty on accuracy of the scaled model in predicting the strain field of the full-scale structure is studied under uncertainty in all design parameters and the strain field of the full-scale I-beam is predicated using that of the scaled model for the worst-case scenario.

Adaptive voltage scaling (AVS) is a promising approach to overcome manufacturing variability, dynamic environmental fluctuation, and aging. This paper focuses on design of AVS circuits. For pursuing efficient AVS, this work presents a design methodology that optimizes both voltage-scaled circuit and voltage control logic achieving practical long mean time to failure (MTTF), e.g., years. Evaluation results show the proposed AVS achieves 20.8% voltage reduction while satisfying target MTTF.

Implant migration can be accurately quantified by model-based Roentgen stereophotogrammetric analysis (RSA), using an implant surface model to locate the implant relative to the bone. In a clinical situation, a single reverse engineering (RE) model for each implant type and size is used. It is unclear to what extent the accuracy and precision of migration measurement is affected by implant manufacturing variability unaccounted for by a single representative model. Individual RE models were generated for five short-stem hip implants of the same type and size. Two phantom analyses and one clinical analysis were performed: "Accuracy-matched models": one stem was assessed, and the results from the original RE model were compared with randomly selected models. "Accuracy-random model": each of the five stems was assessed and analyzed using one randomly selected RE model. "Precision-clinical setting": implant migration was calculated for eight patients, and all five available RE models were applied to each case. For the two phantom experiments, the 95%CI of the bias ranged from -0.28 mm to 0.30 mm for translation and -2.3° to 2.5° for rotation. In the clinical setting, precision is less than 0.5 mm and 1.2° for translation and rotation, respectively, except for rotations about the proximodistal axis (

This paper examines the impact of measured manufacturing variability on the aerodynamic performance of a High Pressure Turbine (HPT) rotor blade. Emphasis is placed on building an aerodynamic analysis framework to understand the mechanisms that drive changes in HPT rotor blade profile loss and performance. This framework is based on perturbations of the blade surface curvature distribution and the associated response of the boundary layer. Aerodynamic assessments are performed numerically for scanned geometries obtained from an existing manufacturing line for a modern gas turbine. Manufacturing variability causes changes in blade curvature that impact the development of surface boundary layers and hence the performance of HPT rotor blades. Blade curvature is therefore a more fundamental indicator of aerodynamic performance than geometric assessments using point measurements for manufacturing accuracy.

Typical manufacturing bio-processes operate as multi-step batch operations. Abnormal variations are often experienced in commercial manufacturing which affects the ability to maintain the product supply to the patients. The low titer is usually highly correlated with high lactate in recombinant Chinese hamster ovary (CHO) cells producing immunoglobulin G, but there are limited means to analyze the vast amount of process data archived from manufacturing sites and understand the causes of the multiple sources of process variability. In this study, we investigate diverse data from a cell culture process, from historical runs of a multi-step manufacturing process for a recombinant antibody. Data includes intra-batch measurements, batch conditions and media preparation measurements. Cell culture process stages from inoculum train to production runs. To analyze manufacturing variability in multiple stages with various sources of data sets, we first adopt principal component analysis (PCA) based monitoring to find in which space high lactate and low lactate batches can be separated, and apply clustering to identify fault directions for intra-batch data from production runs. We then predict final lactate using data from inoculum stages with a nonlinear support vector machine (SVM) classifier. These models are used to uncover potential causes of high lactate. This data-driven modelling can provide new insights on process characteristics to understand the sources of process variability and thus mitigation procedures can be implemented.

This paper presents hardware topologies utilizing MEMS accelerometer in FM modulation scheme to allow for active correction of vibration impairments on timing and phase noise parameters. The exact derivation of the corrective vector using FM modulation scheme is shown with associated relationship to g-sensitivity vector. Measurement techniques for g-sensitivity, manufacturing variability of the corrective vector, and experimental results are presented.