Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.     

Comments on “A novel dead time compensator for stable processes with long dead times” [Journal of Process Control 22 (2012) 612–625]

This short communication analyzes the results recently presented in the paper “On a novel dead time compensator for stable processes with long dead times” published in the Journal of Process Control. In the mentioned paper it is argued that the proposed strategy, called modified Smith predictor (MSP), gives better performance than the filtered Smith predictor (FSP) dead-time compensator for stable processes with dead time. In fact MSP has the same structure as FSP and only some specific tuning rules of the filters are proposed. Therefore, in this work some aspects of the comparative analysis and tuning rules presented in the referred paper are discussed to show that MSP is a particular case of FSP and that for some particular cases its tuning rule does not allow for a good closed-loop response. Some simulation case studies are used to illustrate these aspects.     

An efficient technique for implementing an image-compression neural algorithm on concurrent multiprocessor architectures

The paper describes a parallel implementation of a neural algorithm performing vector quantization for very low bit-rate video compression on toroidal-mesh multiprocessor systems. The neural model considered is a plastic version of the Neural Gas algorithm, whose features are suitable for implementations on toroidal mesh topologies. The architecture adopted, and the data-allocation strategy, enhance the method's scaling properties and remarkable efficiency. The parallel approach is supported by a theoretical analysis of the efficiency of the overall structure. Experimental results on a significant testbed and the fit between predicted and measured values confirm the validity of the parallel approach.     

GaiaDoc： A Tool Focused on Business Requirements for Code Documentation with a CMMI Compliant and RUP Based on Requirement Flow

Aiming to fill a gap in traditional methods of source code documentation, which focus mainly on the API （application programming interface） documentation for other programmers, this article presents a new approach for business requirements, mapping them through a set of annotations. These annotations, in turn, are interpreted by the GaiaDoc tool, which is specified in this paper and is able to generate documentation in form of use case specifications in a language and format easily understandable by the project stakeholders. Along with the GaiaDoc tool proposal, a RUP （rational unified process） based requirements flow is developed to fit with its needs and it is validated through CMMI （capability maturity model integration） requirement process areas and a case study of the proposed methodology＇s application is presented before the final considerations.     

Reducing the power of wireless terminals by adaptive baseband processing

In this paper, we illustrate specific power savings obtained from exploiting a reconfigurable mobile terminal under the 3GPP LTE standard. Building on traditional link adaptation towards maximum throughput and extended towards minimal power consumption, we add two flexible baseband components: the turbo decoder and the multiple input multiple output (MIMO) detector. Optimizing their configuration leads to larger power savings when compared to non-flexible systems only performing link adaptation. The gain observed strongly depends on the scenario. For low-activity set-ups with a few minutes of voice per day, the idle power dominates and the active data rate is relatively low. This makes analog front-end and time-domain processing dominant given their constant power consumption while MIMO detection and turbo decoding that scale with data rate play a smaller role. Still, because of its ability to improve the system spectral efficiency and hence reduce its duty cycle, an advanced MIMO detector can save 10% in power consumption, on the condition that the network requires to use MIMO. Otherwise single input single output is more power-efficient in downlink. In high-throughput scenarios, larger gains are obtained. The flexible MIMO detector can save up to 35% of average power consumption. The turbo decoding also brings some gain, saving up to 12% of power when the full bandwidth is allocated to a single user.     

Utility of Multivariate Analysis in Modeling the Effects of Raw Material Properties and Operating Parameters on Granule and Ribbon Properties Prepared in Roller Compaction

This article aimed to model the effects of raw material properties and roller compactor operating parameters (OPs) on the properties of roller compacted ribbons and granules with the aid of principal component analysis (PCA) and partial least squares (PLS) projection. A database of raw material properties was established through extensive physical and mechanical characterization of several microcrystalline cellulose (MCC) and lactose grades and their blends. A design of experiment (DoE) was used for ribbon production. PLS models constructed with only OP-modeled roller compaction (RC) responded poorly. Inclusion of raw material properties markedly improved the goodness of fit (R²?=?.897) and model predictability (Q²?=?0.72).     

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017