The University of Texas at San Antonio
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Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Plastic injection moulding (PIM) process is used for converting plastics from its raw material into a kind of a semi-finish or finish product. PIM is a complex process due to the non-linear behaviour of controllable parameters available in producing high quality product. PIM usually used in a mass production line to support high demand and wide range of products, from as simple as electronic devices to as complicated as aerospace devices. Therefore, it is very important to control products from defects and to gain knowledge about parameters, which will influence to the whole PIM process. For this purpose, this paper presents an optimization of PIM process parameters in a fishing reel production via Data Mining methods. Our previous research done to optimize the parameters using Design of Experiments (DOE) methods proves that the PIM process can be optimized by running 16 experiments by two levels of fractional factorial design. In this paper, Data Mining will be utilized to provide optimal parameters of the PIM machine with desired accuracy. First, the important PIM machine parameter data in fishing reel production were collected. Then, the collected data are analysed using the REPTree Decision Tree method for classification. It is found that, this approach brings out the important decision of PIM machine parameters that are useful in obtaining the desired output results. The results from Data Mining method not only provides the same results as statistical method, but also introduces more efficient quality improvement activities with minimal cost and time consumptio
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Selective Laser Melting (SLM) is well established as a reliable manufacturing process for its speed of manufacture, resource savings and overall efficiency in processing ‘difficult to machine materials’ with users able to produce complex parts from various metallic alloys [1]. As the demand stronger and more wear resistant part grow particularly in aerospace and automotive industries, the challenge is then for SLM to produce fully dense homogeneous particulate-reinforced parts from dissimilar materials. Challenges in this instance include differential melting points, morphology of powder particles and homogeneity of the feed material. This work offers a solution to evenly distribute powder particles of dissimilar size reliably across a build platform prior to SLM. It also investigates melting parameters and the microstructure of the resultant parts produced. Results presented demonstrate that there is a significant improvement in delivery of dissimilar sized particles across the build area after alloying of the individual powders to produce a composite powder. Complete melting of the Aluminium alloy was achieved with the SiC solidified in the matrix and increased hardness observed in the composite. Some porosity was observed in the microstructure generated which was considered to be a result of the cooling gradient during the re-solidification of the matri
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Flow Forming Process is a manufacturing process to deform rotating axis symmetric work-piece with rotating rollers. It is difficult to predict the behaviour of work-piece during the process because the shear and compression deformation occurs simultaneously in the shape of helix along the axis. This study presents a model which can predict the deformed geometric shape and material properties using geometric shape change and material properties of work-piece. This model is based on finite deformation theory, and assumes that the structure of the material is isotropic lattice structure. To simulate the flow forming process, firstly the axial displacement is calculated using the stress-strain relation. And then the radial displacement is calculated using the volume constancy theory. The material properties of flow formed material are easily calculated with the deformed geometry. The presented model is verified by experimenting actually, and a tensile test demonstrates the predicted material propertie
Progress
No full textThis collection includes digitized publications of the Texas Biomedical Research Institute between 1953 and the present
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; A key challenge in production engineering projects is to achieve significant time and cost savings through early validation of manual assembly operations. Nevertheless, the use of digital human models for dynamic analyses is not very prevalent because of the high modeling complexity in the digital environment: with existing simulation tools, the worker's motions are either unrealistic or too time-consuming to program. Hence, further research is needed for developing a time-saving and realistic human motion simulation. In this article, we present an experimental setup for the early validation of manufacturing tasks through interactive simulation. We use a new hybrid motion-capture system interfaced with the digital environment, which facilitates the generation of realistic human model motion in real time. The software platform used is DELMIA V5. The article describes the relationship between optical and inertial tracking, and how the drift of the inertial sensors can be compensated by using a kinematic chain with a human model. Sequences of postures can be saved, both for the human model and tools, and later replayed synchronously. Finally, we detail the use of our setup in a real-world scenario within automotive manufacturing. This article acts as a practical contribution to simulation-based Manufacturing Ergonomics and Human Factors, illustrating the effectiveness of state-of-the-art technology for viable cost and time savings
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Energy efficiency is an important objective in manufacturing because of political, environmental and economic reasons. A variety of energy efficiency measures is applicable in factory systems, which makes it necessary to structure the measures. A methodical approach to identify and provide energy efficiency measures in manufacturing industry was developed and is briefly described in this paper. A main prerequisite for applying this approach lies in structuring criteria for energy efficiency information. Therefore, this paper describes structuring criteria for both energy efficiency measures and energy efficiency information based on a literature review. The results form a basis to select appropriate energy efficiency measures in different industrial application
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Maintenance is an important factor for the effectiveness of production processes. With the help of new information and communication technologies, the maintenance processes are supported and optimized. Cyber-physical systems
link physical objects with information equipment. Objects such as machines, parts or tools carry sensors, control
and communication devices to enhance their capabilities. In that way, they become smart and act as active
participants in intelligent information networks. Furthermore, they communicate directly with humans. Thus,
social factors (e.g. usability of devices, competencies or responsibilities of users) have to be involved in the design
of information flows. However, different information systems are used in a factory. The data flows of different
areas such as production planning and scheduling, staff planning, material management and machine monitoring
often run in parallel and the maintenance is often just involved marginally in the digital systems. For that reason,
a resource cockpit will be developed in the research project “Socio – Cyber-Physical Systems” (S-CPS). The
cockpit will bundle and provide relevant data and information about products and production resources for
maintenance processes, especially under consideration of social impacts. In the paper, the potentials of
socio-cyber-physical systems for maintenance and objectives and activities of the research project are presente
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Decreasing or entire eliminating all kinds of waste in production systems is one of the main goals of today´s companies implementing lean management. Wastes can be found in material warehouses or facilities, all production and logistic processes and in finished products warehouses. The 4th industrial revolution, widely known as Industrie 4.0, delivers many innovative technologies. All of these solutions are significantly helping to reduce waste and make the company lean. These have to be compared to existing technologies in cases of energetic or time efficiency. The contribution describes two logistics technologies – the AirMove system and Automatic Guided Vehicles. The paper further deals with the comparison of the two logistic technologies in crucial aspects of lean management quality-cost-time. Therefore measurements of energy consumption, time or volume aspects are the necessary tools which are providing needed information. The objective is not the discrediting of one of the technologies, but the relaying of the relevance of lean thinking, drawn by a fundamental comparison of the two technologies. The research will be performed in the experimental and digital factory at Chemnitz University of Technolog
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; The productivity of milling processes is limited by the occurrence of chatter vibrations. The correlation of the maximum stable cutting depth and the spindle speed can be shown in a stability lobe diagram (SLD). Today it is a great effort to estimate the SLD. Lot's of experiments are necessary to measure the SLD or derive a detailed mathematical model to calculate the SLD. Moreover not only cutting depth, but also the cutting width should be taken into account. This paper presents an approach to learn the multidimensional stability lobe diagram (MSLD) during the production based on continuously measured signals using a support vector machine. The support vector machine is extended to make it capable for continuous learning and time-variant systems. The process conditions are classified as stable or unstable. The learned MSLDs are very similar to the analytically calculated MSLDs. Changes over time in the system dynamics can also be learned by the proposed algorith
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; An unsupervised artificial neural network (ANN) based on the ART2-A algorithm is compared to a rule-based method for fault classification on an automated assembly machine. Machine data is collected using three greyscale sensors and two redundant limit switches for 11 different operating conditions. Descriptive features are extracted from the raw data and two data sets, each containing 180 feature vectors, are created for testing both methods. The first data set contains ‘real’ feature vectors obtained from the original sensor signals, and the second data set contains ‘simulated’ feature vectors obtained by scaling the ‘real’ feature vectors. The second data set is used to test the performance of each system when variations are present in the input space. During testing, the rule-based system correctly classified 98.3% of all feature vectors, but its classification thresholds needed to be manually adjusted to accommodate the ‘simulated’ data set. The ART2-A network perfectly classified the 'real' data set into 13 clusters, and then correctly classified the 'simulated' data into the same 13 clusters without any modification to the algorithm's tuning parameter, vigilanc