Geok Soon Hong

An Overview of 3D Printing Technologies for Food Fabrication

Different from robotics-based food manufacturing, three-dimensional (3D) food printing integrates 3D printing and digital gastronomy to revolutionize food manufacturing with customized shape, color, flavor, texture, and even nutrition. Hence, food products can be designed and fabricated to meet individual needs through controlling the amount of printing material and nutrition content. The objectives of this study are to collate, analyze, categorize, and summarize published articles and papers pertaining to 3D food printing and its impact on food processing, as well as to provide a critical insight into the direction of its future development. From the available references, both universal platforms and self-developed platforms are utilized for food printing. These platforms could be reconstructed in terms of process reformulation, material processing, and user interface in the near future. Three types of printing materials (i.e., natively printable materials, non-printable traditional food materials, and alternative ingredients) and two types of recipes (i.e., element-based recipe and traditional recipe) have been used for customized food fabrication. The available 3D food printing technologies and food processing technologies potentially applicable to food printing are presented. Essentially, 3D food printing provides an engineering solution for customized food design and personalized nutrition control, a prototyping tool to facilitate new food product development, and a potential machine to reconfigure a customized food supply chain.

Flank wear measurement by successive image analysis

In this paper, a system based on successive image analysis is proposed for periodic measurement of flank wear in milling. The successive images are captured while the spindle is rotating. The blur of the moving images is minimized by the use of a high-speed camera and low spindle speed during the image capture. A method based on image series to measure flank wear has been developed and successfully applied on these moving images. Its performance is compared with the method based on individual still or static images. The results show improved robustness of this system with high potential for industrial application to measure the flank wear in-cycle (between passes) without stopping the spindle.

Using neural network for tool condition monitoring based on wavelet decomposition

This paper presents a neural network application for on-line tool condition monitoring in a turning operation. A wavelet technique was used to decompose dynamic cutting force signal into different frequency bands in time domain. Two features were extracted from the decomposed signal for each frequency band. The two extracted features were mean values and variances of the local maxima of the absolute value of the composed signal. In addition, coherence coefficient in low frequency band was also selected as a signal feature. After scaling, these features were fed to a back-propagation neural network for the diagnostic purposes. The effect on tool condition monitoring due to the presence of chip breaking was studied. The different numbers of training samples were used to train the neural network and the results were discussed. The experimental results show that the features extracted by wavelet technique had a low sensitivity to changes of the cutting conditions and the neural network has high diagnosis success rate in a wide range of cutting conditions.

Sensor fusion for online tool condition monitoring in milling

The objective of this paper is to combine a direct sensor (vision) and an indirect sensor (force) to create an intelligent integrated tool condition monitoring (TCM) system for online monitoring of flank wear and breakage in milling, using the complementary strengths of the two types of sensors. For flank wear, images of the tool are captured and processed in-cycle using successive moving-image analysis. Two features of the cutting force, which closely indicate flank wear, are extracted in-process and appropriately pre-processed. A self-organizing map (SOM) network is trained in a batch mode after each cutting pass, using the two features derived from the cutting force, and measured wear values obtained by interpolating the vision-based measurement. The trained SOM network is applied to the succeeding machining pass to estimate the flank wear in-process. The in-cycle and in-process procedures are employed alternatively for the online monitoring of the flank wear. To detect breakage, two features in time domain derived from cutting force are used, and the thresholds for them are determined dynamically. Again, vision is used to verify any breakage identified in-process through the cutting force monitoring. Experimental results show that this sensor fusion scheme is feasible and effective for the implementation of online tool condition monitoring in milling, and is independent of the cutting conditions used.

An intelligent sensor system approach for reliable tool flank wear recognition

An intelligent sensor system approach for reliable flank wear monitoring in turning is described. Based on acoustic emission and force sensing, an intelligent sensor system integrates multiple sensing, advanced feature extraction and information fusion methodology. Spectral, statistical and dynamic analysis have been used to determine primary features from the sensor signals. A secondary feature refinement is further applied to the primary features in order to obtain a more correlated feature vector for the tool flank wear process. An unsupervised ART2 neural network is used for the fusion of AE and force information and decision-making of the tool flank wear state. The experimental results confirm that the developed intelligent sensor system can be reliably used to recognise the tool flank wear state over a range of cutting conditions.

Internal model control with enhanced robustness

Based on the conventional structure of internal model control (IMC), a novel IMC scheme with enhanced robustness is developed in this paper. The necessary structure modification for robustness enhancement is very simple as well as effective, and the synthesis of the additional components is not only very simple but also independent from the design of the original IMC controller. Both analytical and numerical studies have been presented to demonstrate the contribution of the structure modification to the robustness enhancement of IMC systems

Pattern generation for bipedal walking on slopes and stairs

Uneven terrain walking is one of the key challenges in bipedal walking. In this paper, we propose a motion pattern generator for slope walking in 3D dynamics using preview control of zero moment point (ZMP). In this method, the future ZMP locations are selected with respect to known slope gradient. The trajectory of the center of mass (CoM) of the robot is generated by using the preview controller to maintain the ZMP at the desired location. Two models of slope walking, namely upslope and downslope, are investigated. Continuous walking on slopes with different gradients is also studied to enable the robots to walk on uneven terrains. Since staircase walking is similar to slope walking, the slope walking trajectory generator can also be applied to the staircase walking. Simulation results show that the robot can walk on many types of slopes and stairs by using the proposed pattern generator.

Identification of feature set for effective tool condition monitoring by acoustic emission sensing

In tool condition monitoring systems, various features from suitably processed acoustic emission signals are utilized by researchers. However, not all of these features are equally informative in a specific monitoring system: certain features may correspond to noise, not information; others may be correlated or not relevant for the task to be realized. This study comprehensively takes all these known signal features and aims to identify the most effective set that can give robust and reliable identification of tool condition. In this paper, the aim is investigated through feature selection, in which automatic relevance determination (ARD) under a Bayesian framework and support vector machine (SVM) are coupled together to perform this task. In tool condition monitoring, this proposed method is able to identify the worst features according to their corresponding ARD parameters and delete them. Then the effectiveness of this pruning may be evaluated by a model validation. Finally, the effective feature set in the developed tool wear recognition system is obtained. The experimental results show that the AE feature set selected through this method is more effective and efficient to recognize tool status over various cutting conditions.

On-line cutting state recognition in turning Using a neural network

Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate.

Ultraprecision machining of micro-structured functional surfaces on brittle materials

Ultraprecision micro-structured functional surfaces on hard and brittle materials, e.g. ceramic and glass, are gaining increasing application in a range of areas such as engineering optics and semiconductor and biomedical products. However, due to their tendency of being damaged in brittle fracture in machining, it is challenging to achieve both a high surface finish and complex surface shapes. In this paper, ultraprecision machining of micro-structured functional surfaces on brittle materials by fast tool servo diamond turning is studied. A machining model has been developed to ensure ductile regime machining of the brittle material, in which the material is removed by both plastic deformation and brittle fracture, but the cracks produced are prevented from being extended into the finished surface. Based on the model, an iterative numerical method has been proposed to predict the maximum feed rate for producing crack-free micro-structured surfaces. Machining experiments on typical micro-structured functional surfaces have been carried out to validate the effectiveness of the proposed method for producing ultraprecision micro-structured functional surfaces.