An approach to in-process surface texture condition monitoring

Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process.     

Calibration-based tool condition monitoring for repetitive machining operations

Reliable tool condition monitoring (TCM) system is essential for any machining process in mass production to control the part quality as well as reduce the machine tool downtime and maintenance costs. However, while various research studies have proposed their TCM systems, the complexity in setups with advanced decision-making algorithms and specificity in application to limited cutting conditions continue to complicate the implementation of these systems into practical scenarios. This study develops a very simple and flexible TCM system for repetitive machining operations. The proposed monitoring approach reduces the complexity of monitoring model by considering the important characteristic of repeatability in process which has been commonly found in the mass production scenario and implements the calibration procedure to improve the flexibility of the model application to actual machining processes with complex toolpath designs and variable cutting conditions. The selected cutting tools with specific tool conditions are used in the calibration phase to generate reference signals. In actual repetitive production, the collected signal generated by the cutting tool in each operation is compared with reference signals to identify the most similar condition of the reference tool through the proposed similarity analysis. To validate the performance, the current study demonstrates the application of proposed monitoring approach to monitor the tool wear in repetitive milling operations with complex toolpath, and the predicted tool wear progression is found to be in good agreement with experimental measurements during the machining of multiple parts over the entire tool life.     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

Machine vision system for curved surface inspection

This application-oriented paper discusses a non-contact 3D range data measurement system to improve the performance of the existing 2D herring roe grading system. The existing system uses a single CCD camera with unstructured halogen lighting to acquire and analyze the shape of the 2D shape of the herring roe for size and deformity grading. Our system will act as an additional system module, which can be integrated into the existing 2D grading system, providing the additional third dimension to detect deformities in the herring roe, which were not detected in the 2D analysis. Furthermore, the additional surface depth data will increase the accuracy of the weight information used in the existing grading system. In the proposed system, multiple laser light stripes are projected into the herring roe and the single B/W CCD camera records the image of the scene. The distortion in the projected line pattern is due to the surface curvature and orientation. Utilizing the linear relation between the projected line distortion and surface depth, the range data was recovered from a single camera image. The measurement technique is described and the depth information is obtained through four steps: (1) image capture, (2) stripe extraction, (3) stripe coding, (4) triangulation, and system calibration. Then, this depth information can be converted into the curvature and orientation of the shape for deformity inspection, and also used for the weight estimation. Preliminary results are included to show the feasibility and performance of our measurement technique. The accuracy and reliability of the computerized herring roe grading system can be greatly improved by integrating this system into existing system in the future.     

Tool condition monitoring using reflectance of chip surface and neural network

A wide variety of tool condition monitoring techniques has been introduced in recent years. Among them, tool force monitoring, tool vibration monitoring and tool acoustics emission monitoring are the three most common indirect tool condition monitoring techniques. Using multiple intelligent sensors, these techniques are able to monitor tool condition with varying degrees of success. This paper presents a novel approach for the estimation of tool wear using the reflectance of cutting chip surface and a back propagation neural network. It postulates that the condition of a tool can be determined using the surface finish and color of a cutting chip. A series of experiments has been carried out. The experimental data obtained was used to train the back propagation neural network. Subsequently, the trained neural network was used to perform tool wear prediction. Results show that the prediction is in good agreement with the flank wear measured experimentally.     

Tool condition monitoring using K-star algorithm

Cutting tools are required for day to day activities in manufacturing. Continuous machining operations lead tool to undergo wear. Worn out tools effect surface finish during machining. The dimensional accuracy of components is also compromised. Robust tool health is vital for better productivity. Hence, an online system condition monitoring of tools is the need of hour, promising reduction in maintenance cost with a greater productivity saving both time and money. This paper presents the classification performance of K-star algorithm. A set of statistical features extracted from vibration signals (good and faulty conditions) form the input to algorithm. In the present study, the K-star algorithm is able to achieve 78% classification accuracy.     

Assessing the condition of a plant

How can we distinguish images of a plant that needs watering from images of the plant in good condition? We show that simple geometric and colorimetric methods can measure stem flaccidity and leaf pallor, which can indicate the thirstiness of a plant.     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.     

Transformation of dynamic facial image sequences using static 2D prototypes

Published online: 15 March 2002     

Widening the dynamic range of pictures using a minimum spanning tree algorithm

A novel method for widening the dynamic range of pictures, without requiring additional frame times or additional storage area in the pixel, is proposed. The sensor used enables local control over the integration time such that a suitable exposure could be defined for each area, and a new method based on the Minimum Spanning Tree algorithm is then incorporated for restoring the additional information representing the specific integration times. These restored integration times are used for reconstructing a wide dynamic range picture. Received: 30 September 1998 / Accepted: 5 October 1999     

Document skew estimation without angle range restriction

The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based on determination of the first eigenvector of the data covariance matrix. The solution comprises image resolution reduction, connected component analysis, component classification using a fuzzy approach, and skew estimation. Experiments on a large set of various document images and performance comparison with two Hough transform-based methods show a good accuracy and robustness for our method. Received October 10, 1998 / Revised version September 9, 1999     

Geometry-driven-diffusion filtering of magnetic resonance images using model-based conductance

The paper deals with the problems of staircase artifacts and low-contrast boundary smoothing in filtering (magnetic resonance MR) brain tomograms that is based on geometry-driven diffusion (GDD). A novel method of the model-based GDD filtering of MR brain tomograms is proposed to tackle these problems. It is based on a local adaptation of the conductance that is defined for each diffusion iteration within the variable limits. The local adaptation uses a neighborhood inhomogeneity measure, pixel dissimilarity, while gradient histograms of MR brain template regions are used as the variable limits for the conductance. A methodology is developed for implementing the template image selected from an MR brain atlas to the model-based GDD filtering. The proposed method is tested on an MR brain phantom. The methodology developed is exemplified on the real MR brain tomogram with the corresponding template selected from the Brainweb. The performance of the developed algorithms is evaluated quantitatively and visually. Received: 1 September 1998 / Accepted: 20 August 2000     

Hierarchical formal verification using a hybrid tool

We describe a hybrid formal hardware verification tool that links the HOL interactive proof system and the MDG automated hardware verification tool. It supports a hierarchical verification approach that mirrors the hierarchical structure of designs. We obtain the advantages of both verification paradigms. We illustrate its use by considering a component of a communications chip. Verification with the hybrid tool is significantly faster and more tractable than using either tool alone. Published online: 19 November 2002     

Verification of arithmetic circuits using binary moment diagrams

Binary moment diagrams (BMDs) provide a canonical representation for linear functions similar to the way binary decision diagrams (BDDs) represent Boolean functions. Within the class of linear functions, we can embed arbitrary functions from Boolean variables to real, rational, or integer values. BMDs can thus model the functionality of data path circuits operating over word-level data. Many important functions, including integer multiplication, that cannot be represented efficiently at the bit level with BDDs, have simple representations at the word level with BMDs. Furthermore, BMDs can represent Boolean functions as a special case. We propose a hierarchical approach to verifying arithmetic circuits, where component modules are first shown to implement their word-level specifications. The overall circuit functionality is then verified by composing the component functions and comparing the result to the word-level circuit specification. Multipliers with word sizes of up to 256 bits have been verified by this technique. Published online: 15 May 2001     

An interactive tool for recognizing structured figures with variable shapes

In electric power supply, railway, and other companies with many facilities, facility management is a laborious task. To realize a computerized facility management system, numerous paper-based facility maps should be stored in a database. In this paper, we present a system that constructs a facility management database by interpretation of paper-based facility maps. This system can automatically recognize structured figures with variable shapes on maps, while conventional methods cannot recognize these figures. And this system can easily generate relational data between facilities and character strings on maps. We compare our recognition method of structured figures with variable shapes with a conventional recognition method, and show the effectiveness of our system. Received: 18 November 1996 / Accepted: 16 February 1998