MRR Value Research Articles

Enhancing the machining productivity in PMEDM for titanium alloy with low-frequency vibrations associated with the workpiece

Abstract The incorporation of frequency vibration into the powder mixed electric discharge machining (PMEDM) process has the potential to significantly enhance the productivity and quality of the machining process, as well as to provide a great deal of opportunities in this sector. An analysis and evaluation of the machining productivity (material removal rate [MRR]) in PMEDM with low-frequency vibration was carried out in this work by considering SKD61 as a workpiece. The oil dielectric solution mixed with titanium powder was used as a dielectric medium with red copper (Cu) as the electrode. At this point in time, the process technology parameters that have been chosen for inquiry are as follows: current (I), pulse on time (T on), powder concentration (C p), P, F, and A. The Taguchi technique (L25) was used to create the experimental matrix. With the use of analysis of variance, it was able to examine the impact that the factors had on MRR, and the analysis was utilized to ascertain the highest possible value of MRR. I was the parameter that has the most significant effect, according to the findings, whereas T on was the parameter that has the least significant effect. The maximum value of the MRRmax is equal to 31.29 mm3/min.

Automated patch correctness predicting to fix software defect

Automated Program Repair (APR) is a technique that can automatically fix software defects without manual debugging, playing a crucial role in software development and maintenance. However, the patches generated by APR still suffer from the problem of overfitting, which poses a significant threat to practical applications. Previous studies have proposed various approaches to predict the correctness of the patch to address this issue, primarily including static-based methods, dynamic-based methods, and learning-based methods. However, these methods all have their own limitations, making it difficult to accurately extract code semantic features and achieve comprehensive prediction. To address the aforementioned challenges, we propose a learning-based unsupervised classification model, Automated Patch cOrrectness aSsessmenT based on muLtiple pErspectives (APOSTLE), for predicting the correctness of patches. Specifically, APOSTLE consists of three components: code vectorization component, where advanced pre-trained models are used to achieve efficient extraction of semantic features from the code; similarity and code change degree calculation component, where APOSTLE calculates the similarity and the degree of change to the code; comprehensive evaluation component, where APOSTLE conducts comprehensive evaluation, addressing the issue of prediction comprehensiveness. Experiments on a collection of 1278 patches (written by developers or generated by 32 APR tools) demonstrate that APOSTLE achieves an AUC value of 0.801, an MAP value of 0.855, and an MRR value of 0.944, outperforming the state-of-the-art approach BATS by 8.3%, 6.0%, and 9.0%, respectively. APOSTLE successfully achieves accurate extraction of code semantic features while achieving comprehensive patch correctness prediction.

TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion

Previous temporal knowledge graph (TKG) reasoning methods often focus exclusively on evolving representations. However, these methods suffer from the inadequacy of capturing the structural nuances of concurrent facts, the intricate relations in topological subgraphs, and the fusion of temporal information across timestamps. To address these challenges, this paper proposes a TKG reasoning method based on Topology-aware dynamic Relation graph and Temporal fusion (TaReT). First, TaReT proposes an innovative attention-based relational graph model, serving as a structural information encoder that captures the intricate structure of concurrent facts. Then, TaReT designs a topology-aware relational correlation unit to discern topological relation graphs of various patterns via an edge-level correlation network, yielding relation representations. Furthermore, TaReT introduces an inter-timestamp temporal information encoder which applies a dual-gate mechanism to integrate structural and relational information for temporal fusion. Finally, the temporal decoder is applied to output entity and relation predictions. Extensive experiments on four benchmark datasets establish TaReT’s superiority over leading TKG reasoning methods. On the ICEWS14 dataset, the MRR value of TaReT exceeds the reasoning baseline RE-GCN by 14.6%.

Fine-Grained Entity-Type Completion Based on Neighborhood-Attention and Cartesian–Polar Coordinates Mapping

Entities refer to things that exist objectively, and entity types are concepts abstracted from entities that have the same features or properties. However, the entity types in the knowledge graph are always incomplete. Currently, the main approach for predicting missing entity types is to learn structured representations of entities and types separately, which ignores neighborhood semantic knowledge of the entity. Therefore, this paper proposes the aggregation neighborhood semantics model for type completion (ANSTC), which extracts neighborhood triple features of target entities with two attentional mechanisms. Meanwhile, the spatial mapping module in ANSTC maps entities from Cartesian coordinate to Polar coordinate system, which can map similar vectors onto a concentric circle and then rotate the angle according to the fine-grained difference to achieve entity-to-type transformation. Moreover, we add semantic features from text to the entity representations to enrich semantics. Through experimental comparison on the FB15K and YAGO43K dataset, we get similar results to the baseline. We also construct person dataset in computer domain, and the values of MRR, Hit@1, Hit@3 and Hit@10 are improved compared with the ConnectE model. The experimental results demonstrate that our model can effectively predict the fine-grained entity types in the domain dataset, and achieve state-of-the-art performance.

PERFORMANCE OPTIMIZATION OF BS970 MILD STEEL TURNING PARAMETERS UNDER SiC-MoS2 HYBRID NANOFLUID USING HYBRID DEEP BELIEF NETWORK BASED COOT OPTIMIZATION

In the modern world of competitive manufacturing, turning is a basic and important process that needs to be optimized for the fast-evolving industrial nature. It has its application in various fields involving numerous materials. This research study deals with modeling with optimization on the turning process parameters of turning E2-BS970 mild steel with cryogenically treated tungsten carbide tool under NFMQL having [Formula: see text] nanofluid. The parameters considered as the input factors are spindle speed and depth of cut along with feed rate and lubrication condition. The design of the experiment is done based on Box–Behnken design for 27 trials in the response surface method. The Deep Neural Network (DBN) and the Coot optimization algorithm are employed using MATLAB software for the prediction modeling. The prediction model developed by the DBN sigmoid function gave minimal error with prediction regression values of 0.99988 for MRR, 0.99516 for Cutting temperature, and 0.99545 for Tool life. The optimized result by CO shows a value of 188.89 rpm for spindle speed, 0.2318[Formula: see text]mm/rev for feed rate, Doc of 1.45[Formula: see text]mm, and 0.5015[Formula: see text]vol.% nanofluid MQL condition. The optimized values of MRR are 3.35[Formula: see text]mm3/min, [Formula: see text]C cutting temperature, and 1543.12[Formula: see text]s of tool life. The DBN model combined with the Coot algorithm shows minimal deviation compared to the experimental results validation and confirmatory analysis and is deemed suitable for efficient prediction.

Analisis Pengaruh Parameter Terhadap MRR Pada Benda Kerja di Mesin CNC Turning dengan Metode Taguchi

A high material removal rate is one of the goals to be achieved when machining parts with CNC turning. However, slow processing affects product processing time and increases production costs. To overcome this problem, it is necessary to set the correct process parameters so that they provide the most influential contribution to the MRR and produce maximum parameter values. This research aims to determine the influence of parameters (spindle speed, feed speed, and feed depth) on MRR and to determine the correct process parameter settings to produce maximum MRR, namely in this experimental design using an orthogonal array L9 (33) with three replications. The material used is ST-60 steel, with the data analyzed using the Taguchi Larger Is a Better method in analysis software. From the results of the mean factor calculation, the percent contribution to feeding was found to be 19.53%, while the depth of feeding was found to be 64.56%. The optimum MRR value is obtained from a combination of parameters in the CNC Turning process on ST-60 steel which can make the response value optimally obtained at the spindle speed 3,184 [rev/min], feeding 0.22 [mm/rev] and depth of cut 2.5 [mm].

Research on Retrieval Method of Online English Grammar Questions Based on Natural Language Processing

The rapid development of information technology is driving the advancement of natural language processing. The retrieval of grammatical problems in natural language processing is one of its specific tasks, particularly in the context of online learning. Therefore, a retrieval method based on fuzzy tree matching is proposed to tackle the issue of grammatical multiple-choice questions (MCQs) in online English, and its effectiveness is validated through experiments. The experimental results indicate that for incomplete queries, the MRR value STPK of the grammatical MCQ questions STP is increased by 7.9% compared to the proposed method. Compared to the traditional POS sorting algorithm, this algorithm demonstrates a 2.1% improvement. When the recall rate is 0.1, the accuracy rate of other methods is below 0.4, while the method proposed in the study surpasses 0.4. In the case of a comprehensive query, STPK the MRR value for t is STP increases by 29.6%. The proposed method in the research generally maintains an accuracy rate between 0.2 and 1.0. However, when other methods achieve an accuracy rate of 0.2, the proposed method’s accuracy rate drops below 0.2. Overall, the proposed method effectively enhances the retrieval accuracy of online English grammar MCQs compared to existing statistical and grammatical analysis methods. This improvement holds great significance for the actual retrieval of online English grammar multiple-choice questions.

Experimental investigation of 3D taper profile machining of SS304 using WEDM

ABSTRACT Wire electrical discharge machining (WEDM) is an outstanding variety of EDM processes in which the tool is a wire electrode. The medical, electronics, tool, and die industries use stainless steel 304 (SS304). However, there are traditionally many complications that manufacturers face, while machining SS304. Also, the creation of 3D shapes using WEDM is challenging. Due to difficulties in normal machining and producing 3D profiles using conventional machining of SS304, this research aims to utilize the application of WEDM for creating complex shapes or products with top as square and bottom as rectangular shape to get a rectangular frustum. A ϕ 0.25 mm brass wire was used, and experiments were conducted as per L9 array. Further, the determination of the best process parameter (Ton, Toff, Voltage, current) combination for achieving the highest MRR, hardness, and minimal surface roughness (Ra) and kerf with analysis of machined surfaces using an SEM microstructure image was tried. The results indicated that MRR and Ra values varied from 7.05 to 47.29 mm3/min and 1.251 to 2.019 µm, respectively. Furthermore, the kerf ranged from 0.294 to 0.475 mm and hardness started at 164 to 233 HV. To optimize WEDM results Grey relational analysis (GRA) was utilized.

Theoretical Advancement of Xi Jinping’s Socialist Thought with Chinese Characteristics in the New Era under the Construction of Knowledge Map

Abstract This paper discusses the theory and practice of constructing knowledge graph under the guidance of Xi Jinping’s socialist thought with Chinese characteristics in the new era. The study first adopts the Transformer technique based on the BERT model for effectively extracting entity relationships. Then, the article proposes an entity alignment method based on Neural Tensor Network, which enhances the semantic and structural information utilization of entity embedding vectors through optimized NTN and joint knowledge representation learning. In addition, the study employs a knowledge graph link prediction method that combines semantics and paths to fill in missing relations, and applies probabilistic soft logic to solve the inference problem of non-deterministic knowledge. Ultimately, the effectiveness of the constructed knowledge graph is demonstrated through empirical analysis of Xi Jinping’s theoretical dataset of socialist thought with Chinese characteristics in the new era. The results show that the method is more effective in dealing with polysemous words and participle errors than traditional methods, improving the data structuring level. For example, the accuracy of entity alignment is significantly better than the baseline algorithm on different datasets, e.g., on the DBP-WE dataset, its Hits@1, Hits@10 and MRR values are higher than those of the baseline by 4.01-29.49, 1.99-31.05, and 0.036-0.289, respectively. The method of this paper is remarkably effective in improving the performance of the entity alignment task, which is helpful in understanding and applying the Xi Jinping’s thought of socialism with Chinese characteristics in the new era provides new theoretical perspectives and practical methods.

A study of deep semantic matching in question-and-answer events in civil litigation in the environmental justice system

Abstract Information retrieval and text mining fields extensively utilize text semantic matching models. In this paper, civil litigation Q&A under the environmental justice system is taken as a specific research field, and after constructing a civil litigation Q&A system based on deep learning, two of the key techniques—question categorization and semantic matching—are selected as the main research content. Specifically, the ALBERT algorithm is used to extract word vectors, and the hidden feature vectors are obtained through BiLSTM modeling of contextual relationships and then combined with the Attention mechanism for scoring and weighting to obtain the final text-level vectors for classification so as to establish the civil litigation question classification model based on ALBERT. Then, we establish the BERT-based civil litigation question and answer matching model by sorting the set of candidate answers by semantic matching degree based on the BERT algorithm. Selected datasets and comparison algorithms are experimented with, and the analysis shows that the question classification model has a better effect than civil litigation question text classification, and the values of each index have been improved by 0.75%~3.00% on the basis of the baseline model. The MAP and MRR values (0.76~0.86) of the question-matching model are higher than those of the comparison model, verifying its superior performance in semantically assigning characters. The model proposed in this paper is more useful because it can provide civil litigation counseling to the public.

Research on personalized recommendation system for social media marketing content in big data environment

Abstract A personalized recommendation system is an effective and consistent marketing method that caters to consumer demand for goods. It solves the problem of consumer shopping choices on e-commerce websites and is currently a hot spot in the field of network information services. In this paper, from the perspective of the user’s shopping process, we summarize the four factors that can maximally reflect the consumer’s interest and preference, i.e., product browsing, collection, adding to cart, and purchasing behavior. To achieve static user interest weights, we quantify each index factor and set the corresponding rules. Considering the change in user interest, the decaying interest value over time is designed to compensate for the deficiencies of static system recommendations. We use implicit user feedback data to identify content that truly interests users and then construct a personalized recommendation algorithm for social media marketing content using multi-source data. The test results show that UIPR has an MRR value of more than 0.8 and an NDCG value of more than 0.55 in both the MovieLens and Taobao datasets, compared to the baseline model’s best value. This proves that UIPR consistently performs better and makes better recommendations.

Research on Strategy and Practice of Enterprise Knowledge Management in Network Environment

Abstract The continuous progress of society and the emergence of science and technology have made knowledge management more and more the core and focus of enterprises, which provides constant power for the sustainable development and core competitiveness of enterprises. The article designs the enterprise knowledge management system model from five dimensions: knowledge resource layer, knowledge activity layer, information platform layer, protection mechanism layer and knowledge management system, and analyzes the application mode of enterprise knowledge management by combining the knowledge management execution process. Knowledge embedding and iterative alignment are introduced to build the enterprise knowledge entity alignment model, and the ConvE model and LSTM model are combined to integrate the enterprise’s historical knowledge to build the dynamic knowledge reasoning model. The evaluation index system for enterprise knowledge management capability maturity is designed and combined with the entropy weight method and object element topology method for comprehensive evaluation. The MRR value of the enterprise knowledge entity alignment model was 94.05%, the maximum score of entity and relationship prediction Hits@15 of the dynamic knowledge inference model was 88.64%, and the maximum score of the knowledge management capability maturity score of the sample enterprise was 83.26 points. Relying on multiple types of technology for enterprise knowledge blending, and combined with knowledge management evaluation, to help enterprises better formulate knowledge management systems, thus enhancing their competitiveness.

A Study of Minority Culture Outreach Translation Based on Cognitive Mapping Construction

Abstract In this paper, we first construct a cognitive neural network and use the knowledge representation learning technique for knowledge complementation and relation extraction to semantically match head-tail entity pairs with relations and accurately model the semantic relationships between entities and relations. Then, the vectors of each entity are mapped onto the hyperplane of a given relation by the cognitive mapping translation model, and vector translation operations are performed on the hyperplane. Finally, this paper’s translation model is compared with other models to compare the semantic representation translation, task processing function, and model complexity of each model so as to verify the superiority of this paper’s model for minority culture outreach translation. The results show that this paper’s method increases 3.4 and 1.2 BLEU values on average in the four translation tasks compared to the Imagination model and VAG model, respectively. When the number of iterations is 40, the MRR value of this paper’s model is 0.345, which is at least 0.1-0.5 more than the other models, thus indicating that this paper’s model has a better performance in outreach translation.

MATLAB Modeling of Component in Electrical Discharge Machining (EDM) Pulses

Electrical Discharge Machining (EDM), is a machining technique for working with conductive materials. During the EDM process, electrical discharge energy is transformed into thermal energy, leading to the erosion of the workpiece. The energy utilized by the EDM process is represented by the time-dependent current, which determines the energy density employed for workpiece erosion. Ideally, during a discharge event, the current pulse should exhibit a square wave shape. However, in practice, EDM circuits often incorporate parasitic components that lead to non-square waveforms or transient currents. In this paper, we describe the simulation of parasitic components using MATLAB, revealing that these components alter the signal waveform and affect the achievement of a square pulse wave in MRR. The presence of parasitic components results in transient current patterns during the discharge phase and, consequently, a reduction in MRR. The implementation of a square wave current, however, enhances the MRR value and increases the efficiency of the EDM process

An ethylenediaminetetraacetic acid (EDTA) surface-functionalized CeO2 composite abrasives with the effective improvement of the removal rate on glass CMP

With the increasing demand for surface processing, there has been a growing interest in enhancing the performance of cerium oxide (CeO2) abrasives for glass chemical mechanical polishing (CMP). To enhance the CeO2 abrasives’ CMP performance, we use the surface modification method to combine inorganic CeO2 with organic ethylenediaminetetraacetic acid (EDTA) molecules, thus utilizing the chemical reaction activity of organic substances to raise the chemical reaction between CeO2 and glass. A series of ethylenediaminetetraacetic acid grafted cerium oxide (CeO2-EDTA) composite abrasives were synthesized and characterized. The CMP experiment indicates that CeO2-EDTA with the EDTA content of 8.33 wt%, called CeO2-EDTA(8.33 %), had a splendid polishing performance. Compared with CeO2, the material removal rate of CeO2-EDTA(8.33 %) could be increased by about 40 % while the surface quality concurrently ameliorated. X-ray photoelectron spectroscopy analysis, dynamic friction test, and contact angle test were further used to probe the removal mechanism of composite abrasives. As a result, the correlation between MRR value and the concentration of cerium trivalent ion, particle size, dynamic friction coefficient, and contact angle was found. This provides a reference for the modification of cerium oxide in the future.

GuaRD: Guaranteed robustness of image retrieval system under data distortion turbulence.

Image search systems could be endangered by adversarial attacks and data perturbations. The image retrieval system can be compromised either by distorting the query or hacking the ranking system. However, existing literature primarily discusses attack methods, whereas the research on countermeasures to defend against such adversarial attacks is rare. As a defense mechanism against the intrusions, quality assessment can complement existing image retrieval systems. "GuaRD" is proposed as an end-to-end framework that uses the quality metric as a weighted-regularization term. Proper utilization and balance of the two features could lead to reliable and robust ranking; the original image is assigned a higher rank while the distorted image is assigned a relatively lower rank. Meanwhile, the primary goal of the image retrieval system is to prioritize searching the relevant images. Therefore, the use of leveraged features should not compromise the accuracy of the system. To evaluate the generality of the framework, we conducted three experiments on two image quality assessment(IQA) benchmarks (Waterloo and PieAPP). For the first two tests, GuaRD achieved enhanced performance than the existing model: the mean reciprocal rank(mRR) value of the original image predictions increased by 61%, and the predictions for the distorted input query decreased by 18%. The third experiment was conducted to analyze the mean average precision (mAP) score of the system to verify the accuracy of the retrieval system. The results indicated little deviation in performance between the tested methods, and the score was not effected or slightly decreased by 0.9% after the GuaRD was applied. Therefore, GuaRD is a novel and robust framework that can act as a defense mechanism for data distortions.

Optimization with artificial intelligence of the machinability of Hardox steel, which is exposed to different processes

In this study, different process types were processed on Hardox 400 steel. These processes were carried out with five different samples as heat treatment, cold forging, plasma welding, mig-mag welding and commercial sample. The aim here is to determine the changes in properties such as microstructure, microhardness and conductivity that occur in the structure of hardox 400 steel when exposed to different processes. Then, the samples affected by these changes were processed in WEDM with the box-behnken experimental design. Ra, Kerf, MRR and WWR results were analyzed in Minitab 21 program. In the continuation of the study, using these data, a prediction models were created for Ra, Kerf, MRR and WWR with Deep Learning (DL) and Extreme Learning Machine (ELM). Anaconda program Python 3.9 version was used as a program in the optimization study. In addition, a linear regression models are presented to comparison the results. According to the results the lowest Ra values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best Ra (surface roughness) value of 1.92 µm was obtained in the heat treated sample and in the experiment with a time off of 250 µs. Model F value in ANOVA analysis for Ra is 86.04. Model for Ra r2 value was obtained as 0.9534. The lowest kerf values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best kerf value of 200 µ was obtained in the heat treated sample and in the experiment with a time off of 200 µs. Model F value in ANOVA analysis for Kerf is 90.21. Model for Kerf r2 value was obtained as 0.9555. Contrary to Ra and Kerf, it is desirable to have high MRR values. On average, the highest MRR values were obtained in mig-mag welded, plasma welded, cold forged, master sample and heat-treated processes, respectively. The best mrr value of 200 g min−1 was obtained in the mig-mag welded sample and in the experiment with a time off of 300 µs. Model for MRR r2 value was obtained as 0.9563. The lowest WWR values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best wwr value of 0.098 g was obtained in the heat treated sample and in the experiment with a time off of 200 µs. Model F value in ANOVA analysis for WWR is 92.12. Model for wwr r2 value was obtained as 0.09561. In the analysis made with artificial intelligence systems; The best test MSE value for Ra was obtained as 0.012 in DL and the r squared value 0.9274. The best test MSE value for kerf was obtained as 248.28 in ELM and r squared value 0.8676. The best MSE value for MRR was obtained as 0.000101 in DL and the r squared value 0.9444. The best MSE value for WWR was obtained as 0.000037 in DL and the r squared value 0.9184. As a result, it was concluded that different optimization methods can be applied according to different outputs (Ra, Kerf, MRR, WWR). It also shows that artificial intelligence-based optimization methods give successful estimation results about Ra, Kerf, MRR, WWR values. According to these results, ideal DL and ELM models have been presented for future studies.

Investigating the effect of tool material on performance parameters in electric discharge machining through FEM

The study aims to fill the gap in the existing literature by investigating the effects of various tool materials, including copper, tungsten carbide, and brass, on performance parameters in electric discharge machining (EDM) simulations. The authors developed a modified gaussian heat flux equation that incorporates the electrical resistivity of both the tool and workpiece, based on prior experimental investigations. The authors further developed a finite element model that incorporates the modified Gaussian heat flux equation, spark radius, and fraction of heat transferred to workpiece as a function of pulse on time and pulse current, latent heat, specific heat values, and thermal conductivity properties. The outcomes of the simulation indicated that the Cu tool exhibited the minimum MRR and SR values when subjected to a current of 9 amps and a duty cycle of 0.4, as well as a current of 12 amps and a duty cycle of 0.6. The results also indicate that the brass tool displayed marginally elevated MRR and SR, whereas the Tungsten carbide tool exhibited the highest MRR and SR. The simulation results for all three tool materials showed similar trends, consistent with prior research. Also, an increase in gap voltage, pulse current and duty cycle was observed to correspond with increased MRR and SR. Authors also observed that as electrode material electrical conductivity increased, so did workpiece material residual stresses. These results assist with the choice of tool materials for EDM processes, allowing for finer control over performance parameters and greater machining efficiency.

Optimisation on machining parametres by EDM of TiN coated Ti6Al4V alloys

ABSTRACT This study investigated the effects of electro-erosion (EDM) processing parameters on TiN-coated Ti6Al4V alloy by the physical vapour deposition (PVD) technique. Electrode wear rate (TWR), workpiece machining rate (MRR), and surface roughness (Ra) values were determined by including discharge current, pulse time (ton), and two different tool electrodes (E-Cu and CuBe) into the EDM process parameters. Variables affecting the experimental study (ANOVA) were performed depending on the analysis of variance method. For the analysed (ANOVA) TiN-coated Ti6Al4V samples, the most optimal parameters of MRR value were determined using current 12 A, ton 100, and Cu electrodes. For the TWR value, the optimum parameters were examined by using current 6 A, ton 100, and CuBe electrodes. Finally, 12 A current, ton 100, and electrode E-Cu were analysed for the lowest Ra value. The results showed a better result in MRR and Ra with increasing discharge current, while a better result was obtained in TWR with increasing pulse duration and decreasing discharge current. In addition, it was observed that the E-Cu electrode performed better than the CuBe electrode in processing TiN-coated Ti6Al4V.

Microvascular Resistance Reserve to Assess Microvascular Dysfunction in ANOCA Patients

BackgroundMicrovascular resistance reserve (MRR) is a new index to assess coronary microvascular (dys)function, which can be easily measured invasively using continuous thermodilution. In contrast to coronary flow reserve (CFR), MRR is independent of epicardial coronary disease and hemodynamic variations. Its measurement is accurate, reproducible, and operator independent. ObjectivesThe aim of this study was to establish the range of normal values for MRR and to determine an optimal cutoff point. MethodsIn this exploratory study in 214 patients with angina and no obstructive coronary artery disease, after excluding significant epicardial disease, all physiological parameters, such as fractional flow reserve, index of microvascular resistance, CFR, absolute blood flow, absolute microvascular resistance, and MRR, were measured. On the basis of concordant positive or concordant negative results of index of microvascular resistance and CFR, subgroups of patients were defined with high probability of either normal (n = 122) or abnormal (n = 24) microcirculatory function, and MRR was studied in these groups. ResultsMean MRR in the “normal” group was 3.4 compared with a mean MRR of 1.9 in the “abnormal” group; these values were significantly different between the groups. MRR >2.7 ruled out coronary microvascular dysfunction (CMD) with a certainty of 96%, whereas MRR <2.1 indicated the presence of CMD with a similar high certainty of 96%. ConclusionsMRR is a suitable index to distinguish the presence or absence of CMD in patients with angina and no obstructive coronary artery disease. The present data indicate that an MRR of 2.7 virtually excludes the presence of CMD, while an MRR value <2.1 confirms its presence.