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Bearing is an important machine detail that appears in almost all mechanical systems. Estimating its operating condition online in order to hold the initiative in exploiting the systems, therefore, is one of the most urgent requirements. In this paper, we propose an online bearing damage identifying method named ASSBDIM based on adaptive neuro-fuzzy inference system (ANFIS), singular spectrum analysis (SSA), and sparse filtering (SF). It is an online process with offline and online phases. In the offline phase, by applying SSA and SF to the measured data stream typed big data with noise, both preprocessing data and extracting valuable information are implemented to build two offline databases signed Off_DaB and Off_testDaB. The ANFIS identifies the dynamic response of the mechanical system via Off_DaB. Based on Off_testDaB, the parameters of the ASSBDIM are then optimized. In the online phase, at each time, another database called On_DaB is built in the way similar to the one used for building the input space of the two offline databases. On_DaB participates as inputs of the ANFIS to estimate its outputs, which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In many real applications, building and updating adaptive neuro-fuzzy inference system (ANFIS) based on noisy measuring data sources need to be performed such that the filtering impulse noise (IN) from the initial datasets (IDSs) and establishing the ANFIS via the filtered IDS are carried out simultaneously. Focused on this purpose, in this paper, a novel recurrent mechanism as well as a solution for filtering IN based on Lyapunov stability theory is proposed to establish an adaptive online IN filter (AOINF). Using the AOINF, kernel fuzzy-C-means clustering method, and the least mean squares method, a cluster data space deriving from the filtered IDS is created to which the ANFIS is then formed. The recurrent mechanism executes filtering IN to build ANFIS and using the ANFIS as an updated-filter to filter IN synchronously until either the ANFIS converges to the desired accuracy or a stop condition is satisfied. Surveys, including identifying dynamic response of a magnetorheological damper via measuring datasets, are performed to evaluate the proposed method. [ABSTRACT FROM PUBLISHER]

A hot runner system can provide many advantages to plastic injection mold engineers for improving product quality. In edge gate systems in particular, the gate traces can appear on the side of products rather than the top. However, it is difficult to establish hot runner systems using edge gates because of their structural differences from conventional gate systems. This article presents the entire process of preparing a 48-cavity plastic injection molding system with edge gates. This process consists of 48-cavity injection mold design, structural analysis, verification of design plans, filling analysis of multi-cavity, cooling channel design on the basis of cooling analysis, fabrication of the mold system, and test injection. All presented computer-aided engineering analyses were conducted using ANSYS and MoldFlow. [ABSTRACT FROM AUTHOR]

This paper presents a methodology of machined surface error compensation based on an inspection database by using an on-machine measurement system in profile milling. First, we make compensation for the geometric errors of the machining center by using a closed-loop configuration for the improvement of machining and inspection accuracy. The probing errors are also taken into account. We manufacture a specimen workpiece and then inspect machined surface error distribution. In order to efficiently analyze the surface errors, two characteristic surface error parameters Werr and Derr are defined. Subsequently, it is possible to fit these parameters by using polynomial functions. These polynomial functions allow us to determine the surface errors in the domain of modeled cutting conditions. Based on these functional relationships between cutting conditions and surface errors, we try to correct the tool path in order to effectively reduce the errors by using an iterative algorithm. An iterative algorithm allows us to integrate changes of the cutting conditions according to the corrected tool path. To compensate for the surface errors, manufacturing tolerances are also taken into account. Experimentation is carried out in order to validate the approaches proposed in this paper. [Copyright &y& Elsevier]

Plastic worm wheel is widely used in the vehicle manufacturing field because it is favorable for weight lightening, vibration and noise reduction, as well as corrosion resistance. However, it is very difficult for general plastics to secure the mechanical properties that are required for vehicle gears. If the plastic resin is reinforced by glass fiber in the fabrication process of plastic worm wheel, it is possible to achieve the mechanical properties of metallic material levels. In this study, the mechanical characteristic analysis of the glass-reinforced plastic worm wheel, according to the contents of glass fiber, is performed by analytic and experimental methods. In the case of the glass fiber-reinforced resin, the orientation and contents of glass fibers can influence the mechanical properties. For the characteristic prediction of plastic worm wheel, computer-aided engineering (CAE) analysis processes such as structural and injection molding analysis were executed with the polyamide resin reinforcement glass fiber (25 wt %, 50 wt %). The injection mold for fabricating the prototype plastic worm wheel was designed and made to reflect the CAE analysis results. Finally, the durability of prototype plastic worm wheel fabricated by the injection molding process was evaluated by the experimental method and the characteristics according to the glass fiber contents.

For microfabrications of biochips with micro fluidic channels, a large number of microfabrication techniques based on silicon or glass-based Micro-Electro-Mechanical System (MEMS) technologies were proposed in the last decade. In recent years, for low cost and mass production, polymer-based microfabrication techniques by microinjection molding and micro hot embossing have been proposed. These techniques, which require a proper photoresist, mask, UV light exposure, developing, and electroplating as a preprocess, are considered to have some problems. In this study, we propose a new microfabrication technology which consists of micro end-milling and powder blasting. This technique could be directly applied to fabricate the metal mold without any preprocesses. The metal mold with micro-channels is machined by micro end-milling, and then, burrs generated in the end-milling process are removed by powder blasting. From the experimental results, micro end-milling combined with powder blasting could be applied effectively for fabrication of the injection mold of biochips with micro fluidic channels.

Micro drilled holes are utilized in many of today's fabrication processes.Precision production processes in industries are trending toward the use of smaller holeswith higher aspect ratios, and higher speed operation for micro deep hole drilling. However,undesirable characteristics related to micro drilling such as small signal-to-noise ratios,wandering drill motion, high aspect ratio, and excessive cutting forces can be observedwhen cutting depth increases. In this study, the authors attempt to minimize the thrustforces in the step-feed micro drilling process by application of the DOE (Design ofExperiment) method. Taking into account the drilling thrust, three cutting parameters,feedrate, step-feed, and cutting speed, are optimized based on the DOE method. Forexperimental studies, an orthogonal array L 27 (3 13 ) is generated and ANOVA (Analysis ofVariance) is carried out. Based on the results it is determined that the sequence of factorsaffecting drilling thrusts corresponds to feedrate, step-feed, and spindle rpm. Acombination of optimal drilling conditions is also identified. In particular, it is found in thisstudy that the feedrate is the most important factor for micro drilling thrust minimization.

Recently, the magnetorheological (MR) polishing process has been examined asa new ultra-precision polishing technology for micro parts in MEMS applications. In theMR polishing process, the magnetic force plays a dominant role. This method uses MRfluids which contains micro abrasives as a polishing media. The objective of the presentresearch is to shed light onto the material removal mechanism under various slurryconditions for polishing and to investigate surface characteristics, including shape analysisand surface roughness measurement, of spots obtained from the MR polishing process usingalumina abrasives. A series of basic experiments were first performed to determine theoptimum polishing conditions for BK7 glass using prepared slurries by changing the processparameters, such as wheel rotating speed and electric current. Using the obtained results,groove polishing was then performed and the results are investigated. Outstanding surfaceroughness of Ra=3.8nm was obtained on the BK7 glass specimen. The present resultshighlight the possibility of applying this polishing method to ultra-precision micro partsproduction, especially in MEMS applications.