The University of Texas at San Antonio
UTSA Digital Collections (The University of Texas at San Antonio)Not a member yet
42217 research outputs found
Sort by
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; Electric discharge machining drill (EDM-drill) is a process to machine fine holes. EDM-drill is specially used for drilling of difficult-to-cut materials such as high-strength steel, titanium and cemented carbide processing. The machining parameters of EDM-drill include voltage, PWM (pulse width modulation), machining speed, discharge capability, flushing rate, etc. These machining parameters affect the accuracy of the work-piece, the wear consumption of electrodes, surface roughness and machining time. As there are many machining parameters and machined results of work-pieces, it is difficult to establish the relations between the machining parameters and the results. A fuzzy system is presented to predict the relations. The machining parameters and results are normalized using the principal component analysis (PCA) and expressed as membership function (MF). Fuzzy rules are created by fuzzy rule-base which from MF and fuzzy inference is carried out. By this method, it is possible to obtain reasonably the machining parameters, which is necessary for the required machined results. On other hands, the machined results can also be predicted with the given machining parameters. The proposed fuzzy system is implemented using MATLAB. Actual experiments and Fuzzy inference are carried out in same machining conditions and the results are compare
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; This paper presents a direct fabrication of highly conductive silver tracks with sub-20 μm microstructures on glass substrates using electrohydrodynamic jet printing (EHD) based on alternative current (AC) voltage. A new AC-modulated EHD technique is presented and used in directly printing by generating a fine jet through a large electrical potential between the nozzle and substrate. In the presented technique of AC-modulated EHD, when charge accumulates on the ink meniscus at the nozzle, a fine jet down to nano scale can be generated. The variables of fabrication process, like plotting speeds, curing temperature and number of layers, were investigated to achieve reliable jet printing of conductive silver tracks. Topography and electrical property of printed tracks were characterized and verified. By using modulated AC-pulsed voltage, we are capable of printing high resolution continuous patterns on insulating substrates. In the study, we successfully applied EHD for fabrication of highly conductive silver tracks on glass substrate. It was the first time that sub-20 μm silver tracks were demonstrated with resistivity about 3.16 times than bulk silver. The presented technique can be used for direct printing of micro scale electronic circuits and device
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; Manufacturing has traditionally been the domain of centralised factories, set-up and optimised to make a sole product or product-range. Whilst the in-house efficacy of such systems has been greatly improved due to lean six-sigma methodologies, the wider scale distribution and supply system has undergone little to no transformation over the past century. Previous research pertaining to distributed manufacture, cloud-manufacture and reconfigurable systems has provided several decentralised models using traditional manufacturing capabilities. Though to date they have not been fully realised due to practical limitations. This paper outlines the scope for hybrid manufacturing platforms – using a plurality of processes on a single motion platform, to manufacture products in a decentralised network. Social, economic and environmental ramifications of adopting such a system under particular use cases, such as in shops, community areas or in individual households, are highlighted. The combination of several key processes, including measurement, will allow users to manufacture parts with minimal intervention. A move to personal fabrication would allow greater personalisation and convenience for the end user; however, the issues of copyright and loss of economy of scale would inhibit mass uptake in the near-future. Growing interest by enthusiasts and early adopters will continue to make an impact on the way the populace views manufacture
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; In recent times there has been an increasing trend, among organisations, towards the adoption of business excellence models (i.e. EFQM, Malcolm Baldrige, and Deming) as a strategic approach to drive improvements. This research takes a divergent-empirical perspective from the narrow view of current studies by investigating the challenges that organisations face when implementing the EFQM model. Semistructured qualitative interviews were conducted with a group of five EFQM experts that comprised quality management consultants and business managers. Primary data from participants was analysed using the qualitative content analysis approach, to collect, organise and extract key contents from the interview data. The results of the study suggest that, like with most TQM initiatives, the challenges that organisations face when implementing the EFQM model include factors such as: lack of viable leadership and top management support, lack of adequate planning, lack of skills, resources intensity, closed vertical communication, focus on short-term objectives (quick results), lack of employee commitment, and organisational structure, size and sector. The results of this empirical study can help managers and business excellence professionals understand the barriers and challenges that can impede the effective implementation of the EFQM model. It is only by understanding these challenges that best practises can be developed in order to mitigate the
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; In this research, discrete-event simulation is used to study the I/O drawer test process in a high-end server manufacturing environment in order to identify the optimal batch size of the I/O drawers to be tested per driver. High-end server manufacturing environment is characterized by fast lead time and lengthy build process. Therefore, main components of the server, such as I/O drawers, are tested ahead of time (fabrication test) and stored in inventory as tested parts to be ready for prompt fulfilment of the customer orders. In this research, a simulation model is developed for the I/O drawer test process with a focus on batching the I/O drawers on testing. Different scenarios for the batch size are considered and a statistical comparison is performed against the current scenario used by the I/O drawer Fab test operators. Unlike the “one-piece flow” lean concept which encourages small batch size (even one), the results show significant savings in cycle time and energy consumption when the batch size is increased. This is attributed to the lengthy setup time of the I/O drawer testing process as using small batch sizes requires very short set-up time. The optimal batch size scenario results in cycle time savings by 20% which is equivalent to 8116 hours per year. Other savings include: electrical energy and less consumption of chilled water for the cooling unit
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; Complex mechatronic systems, such as modern trains, demand interdisciplinary software tools in order to test systems as a whole and predict operational behavior. Caused by the intention of cost savings and pushing the market launch time, computer-aided modelling and simulating of the behavior of single devices, such as thermal effects, stresses, deformations and also electrical state variables, have long since become state-of-the-art. This method leads to well-matured products. The component integration into the system as a whole, however, often causes trouble due to the lack of computer and software assistance. Furthermore, the diverse engineering fields do not have the expertise to collaborate. Therefore, joined system testing and improving is difficult and uncommon. Since the different mechanical and electrical components are being developed and tested independently, overall system behavior can hardly be forecast. As a consequence, during the system assembly phase, unpredicted incompatibilities and system malfunctions can appear. In order to integrate the train subsystems into an overall system and allow for a better synchronization and proper system testing, the application of multi-domain modelling and simulating is recommended. In this regard, the benefits brought by multi-domain applications will be discussed in this publication, using the example of a modern train. Thereafter, a case study of a pneumatic train brake application follows. Its results are exemplarily being shown to demonstrate future perspectives. Models and simulation results of the brake and air supply components already turn out allowing for comparisons with the actual system. System start-up times of the simulation match actually measured times adequatel
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 objective of this research is to create an inexpensive, automated tool setter to reduce the overall tool set up time. The project focused on addressing the tool setting in the Z-axis (spindle axis), which is needed for each tool change, and accounts for majority of the total tool setting time. A fiber optic FS-V30M sensor from Keyence that is equipped with a light emitting element and receiving sensor was used. The sensor detects the position of the micro-tool by measuring the light intensity change as the tool crosses the emitted light beam. A bracket was designed and manufactured to mount the sensor onto the workpiece pallet to hold the fiber optic cable. A novel search/detection algorithm was developed and implemented in the CNC machine controller. Controlled experiments were conducted to test the performance of the tool setter. The system achieved 0.6 μm repeatability and 2 μm accuracy across different size of micro-tools. The execution of each tool setting takes about 10 seconds, which is a 80%~90% reduction from the manual tool settin
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 significant inconsistency problem exists in the quality of resistance spot welding, and yet it offers various
advantages in production. These inconsistent welding data can be eliminated using anomaly detection or instance selection methods. However, in the weldability prediction problem, this inconsistency we refer to as proper-inconsistency, may not be eliminated since it can be used to extract additional information. In this research, we examine the effects of this inconsistency on prediction performance using two machine learning methods, k-Nearest Neighbors (kNN) regression and Generalized Regression Neural Network, in order to identify an
approach towards tackling the proper-inconsistency problem in weldability prediction. We also propose a new prediction performance measure, Mean Acceptable Error (MACE), for prediction models in the presence of proper-inconsistency. The proposed method is tested with actual weldability test dat
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 main focus of this study is to apply the Lean concept and methods to help a compressed gas and liquid filling company find the root causes of slow production as well as identify where and how the gas and liquid are lost and suggest methods for improvement. A careful study of the production process was done by analyzing the setups and layout of current work stations, interviewing employees, conducting time study, drawing work flow charts, value stream mapping and measuring the input and output gas amount. Various wastes such as transportation, motion, and waiting were identified, and gas and liquid leaking stations were found. A new plant layout was provided to help the company improve the production efficiency. The results show that these lean manufacturing initiatives had led to a reduction of travel distance, production lead time and factory floor spac
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; To cope with today market challenges and guarantee adequate competitive performances, companies have been decreasing their products life cycles, as well as increasing the number of product varieties and respective services available on their portfolio. Consequently, it has been observed an increasing in complexity in all domains, from product and process development, factory and production planning to factory operation and management. This reality implies that organizations should be able to compile and analyze, in a more agile way, the immense quantity of data generated, as well as apply the suitable tools that, based on this knowledge, will supports stakeholders to take decision envisioning future performance scenarios. Aiming to pursuing this vision was developed a proactive performance management framework, composed by a performance thinking methodology and a performance estimation engine. While the methodology developed is an extension of the Systems Dynamics approach for complex systems’ performance management, on the other hand, the performance estimation engine is an innovative IT solution responsible by capturing lagging indicators, as well as estimate future performance behaviors. As main outcome of this research work, it was demonstrated that following a systematic and formal approach, it is possible
to identify the feedback loops and respective endogenous and exogenous variables responsible by hindering the systems behavior, in terms of a specific KPI. Moreover, based on this enhanced understanding about manufacturing systems behavior, it was proved to be possible to estimate with high levels of confidence not only the present but also future performance behavior. From the combination of both qualitative and quantitative approaches, it was explored an enhanced learning machine algorithm capable to specify the curve of behavior, characteristic from a specific manufacturing system, and thus estimate future behaviors based on a set of leading indicators. In order to achieve these objectives, both Neural Networks and Unscented Kalman Filter for nonlinear estimation were applied. Important results and conclusions were extracted from an application case performed within a real automotive plant, which demonstrated the feasibility of this research towards a more proactive
management approac