862 resultados para Manufacturing robotics
Resumo:
The practice of robotics and computer vision each involve the application of computational algorithms to data. The research community has developed a very large body of algorithms but for a newcomer to the field this can be quite daunting. For more than 10 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This new book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes over 1000 MATLAB® and Simulink® examples and figures. The book is a real walk through the fundamentals of mobile robots, navigation, localization, arm-robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and multi-view geometry, and finally bringing it all together with an extensive discussion of visual servo systems.
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The purpose of this study is to identify the most common manufacturing practices adopted by the Malaysian manufacturers, company performance factors and relationship between practices and performances. To fulfil the study objectives, 400 manufacturers were surveyed by a standard 400 questionnaire. Three research methodologies such as descriptive analysis, ANOVA and regression analysis have been employed in this study. The analysis revealed that Malaysian manufacturers focus on optimizing three critical performance factors: product development, less customer return rate and on time delivery (OTD). The most important competitive factor was found to be company reputation and design and manufacturing capacity is the least important factor. The findings also proved that manufacturing practices significantly influence company performances
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Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.
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In this paper, the level of lean manufacturing implementation by Saudi manufacturing companies is investigated, the extent of application of lean manufacturing practice is identified and the benefits and barriers of Lean implementation are evaluated. The results reported in this paper are based on data collected from a survey using a standard questionnaire administered to 120 manufacturers in Saudi Arabia. Evidence indicates that large size companies are more likely to implement and gain the advantages of lean manufacturing than small and medium size companies. The most implemented lean manufacturing tools are Computerized Planning Systems, TQM, Maintenance Optimization and CIP. Main barriers against lean manufacturing implementation include the organization culture, lack of management commitment and lack of skilled workers. Results also show that benefits gained from lean manufacturing implementation are significant and are correlated with the level of implementation of lean strategies.
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Purpose: Important performance objectives manufacturers sought can be achieved through adopting the appropriate manufacturing practices. This paper presents a conceptual model proposing relationship between advanced quality practices, perceived manufacturing difficulties and manufacturing performances. Design/methodology/approach: A survey-based approach was adopted to test the hypotheses proposed in this study. The selection of research instruments for inclusion in this survey was based on literature review, the pilot case studies and relevant industrial experience of the author. A sample of 1000 manufacturers across Australia was randomly selected. Quality managers were requested to complete the questionnaire, as the task of dealing with the quality and reliability issues is a quality manager’s major responsibility. Findings: Evidence indicates that product quality and reliability is the main competitive factor for manufacturers. Design and manufacturing capability and on time delivery came second. Price is considered as the least important factor for the Australian manufacturers. Results show that collectively the advanced quality practices proposed in this study neutralize the difficulties manufacturers face and contribute to the most performance objectives of the manufacturers. The companies who have put more emphasize on the advanced quality practices have less problem in manufacturing and better performance in most manufacturing performance indices. The results validate the proposed conceptual model and lend credence to hypothesis that proposed relationship between quality practices, manufacturing difficulties and manufacturing performances. Practical implications: The model shown in this paper provides a simple yet highly effective approach to achieving significant improvements in product quality and manufacturing performance. This study introduces a relationship based ‘proactive’ quality management approach and provides great potential for managers and engineers to adopt the model in a wide range of manufacturing organisations. Originality/value: Traditional ways of checking product quality are different types of testing, inspection and screening out bad products after manufacturing them. In today’s manufacturing where product life cycle is very short, it is necessary to focus on not to manufacturing them first rather than screening out the bad ones. This study introduces, for the first time, the idea of relationship based advanced quality practices (AQP) and suggests AQPs will enable manufacturers to develop reliable products and minimize the manufacturing anomalies. This paper explores some of the attributes of AQP capable of reducing manufacturing difficulties and improving manufacturing performances. The proposed conceptual model contributes to the existing knowledge base of quality practices and subsequently provides impetus and guidance towards increasing manufacturing performance.
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With the current curriculum focus on correlating classroom problem solving lessons to real-world contexts, are LEGO robotics an effective problem solving tool? This present study was designed to investigate this question and to ascertain what problem solving strategies primary students engaged with when working with LEGO robotics and whether the students were able to effectively relate their problem solving strategies to real-world contexts. The qualitative study involved 23 Grade 6 students participating in robotics activities at a Brisbane primary school. The study included data collected from researcher observations of student problem solving discussions, collected software programs, and data from a student completed questionnaire. Results from the study indicated that the robotic activities assisted students to reflect on the problem solving decisions they made. The study also highlighted that the students were able to relate their problem solving strategies to real-world contexts. The study demonstrated that while LEGO robotics can be considered useful problem solving tools in the classroom, careful teacher scaffolding needs to be implemented in regards to correlating LEGO with authentic problem solving. Further research in regards to how teachers can best embed realworld contexts into effective robotics lessons is recommended.
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Design for Manufacturing (DFM) is a highly integral methodology in product development, starting from the concept development phase, with the aim of improving manufacturing productivity and maintaining product quality. While Design for Assembly (DFA) is focusing on elimination or combination of parts with other components (Boothroyd, Dewhurst and Knight, 2002), which in most cases relates to performing a function and manufacture operation in a simpler way, DFM is following a more holistic approach. During DFM, the considerable background work required for the conceptual phase is compensated for by a shortening of later development phases. Current DFM projects normally apply an iterative step-by-step approach and eventually transfer to the developer team. Although DFM has been a well established methodology for about 30 years, a Fraunhofer IAO study from 2009 found that DFM was still one of the key challenges of the German Manufacturing Industry. A new, knowledge based approach to DFM, eliminating steps of DFM, was introduced in Paul and Al-Dirini (2009). The concept focuses on a concurrent engineering process between the manufacturing engineering and product development systems, while current product realization cycles depend on a rigorous back-and-forth examine-and-correct approach so as to ensure compatibility of any proposed design to the DFM rules and guidelines adopted by the company. The key to achieving reductions is to incorporate DFM considerations into the early stages of the design process. A case study for DFM application in an automotive powertrain engineering environment is presented. It is argued that a DFM database needs to be interfaced to the CAD/CAM software, which will restrict designers to the DFM criteria. Consequently, a notable reduction of development cycles can be achieved. The case study is following the hypothesis that current DFM methods do not improve product design in a manner claimed by the DFM method. The critical case was to identify DFA/DFM recommendations or program actions with repeated appearance in different sources. Repetitive DFM measures are identified, analyzed and it is shown how a modified DFM process can mitigate a non-fully integrated DFM approach.
Resumo:
Design for Manufacturing (DFM) is a highly integral methodology in product development, starting from the concept development phase, with the aim of improving manufacturing productivity. It is used to reduce manufacturing costs in complex production environments, while maintaining product quality. While Design for Assembly (DFA) is focusing on elimination or combination of parts with other components, which in most cases relates to performing a function and manufacture operation in a simpler way, DFM is following a more holistic approach. Common consideration for DFM are standard components, manufacturing tool inventory and capability, materials compatibility with production process, part handling, logistics, tool wear and process optimization, quality control complexity or Poka-Yoke design. During DFM, the considerable background work required for the conceptual phase is compensated for by a shortening of later development phases. Current DFM projects normally apply an iterative step-by-step approach and eventually transfer to the developer team. The study is introducing a new, knowledge based approach to DFM, eliminating steps of DFM, and showing implications on the work process. Furthermore, a concurrent engineering process via transparent interface between the manufacturing engineering and product development systems is brought forward.
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Digital human modelling (DHM) has today matured from research into industrial application. In the automotive domain, DHM has become a commonly used tool in virtual prototyping and human-centred product design. While this generation of DHM supports the ergonomic evaluation of new vehicle design during early design stages of the product, by modelling anthropometry, posture, motion or predicting discomfort, the future of DHM will be dominated by CAE methods, realistic 3D design, and musculoskeletal and soft tissue modelling down to the micro-scale of molecular activity within single muscle fibres. As a driving force for DHM development, the automotive industry has traditionally used human models in the manufacturing sector (production ergonomics, e.g. assembly) and the engineering sector (product ergonomics, e.g. safety, packaging). In product ergonomics applications, DHM share many common characteristics, creating a unique subset of DHM. These models are optimised for a seated posture, interface to a vehicle seat through standardised methods and provide linkages to vehicle controls. As a tool, they need to interface with other analytic instruments and integrate into complex CAD/CAE environments. Important aspects of current DHM research are functional analysis, model integration and task simulation. Digital (virtual, analytic) prototypes or digital mock-ups (DMU) provide expanded support for testing and verification and consider task-dependent performance and motion. Beyond rigid body mechanics, soft tissue modelling is evolving to become standard in future DHM. When addressing advanced issues beyond the physical domain, for example anthropometry and biomechanics, modelling of human behaviours and skills is also integrated into DHM. Latest developments include a more comprehensive approach through implementing perceptual, cognitive and performance models, representing human behaviour on a non-physiologic level. Through integration of algorithms from the artificial intelligence domain, a vision of the virtual human is emerging.
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The ninth release of the Toolbox, represents over fifteen years of development and a substantial level of maturity. This version captures a large number of changes and extensions generated over the last two years which support my new book “Robotics, Vision & Control”. The Toolbox has always provided many functions that are useful for the study and simulation of classical arm-type robotics, for example such things as kinematics, dynamics, and trajectory generation. The Toolbox is based on a very general method of representing the kinematics and dynamics of serial-link manipulators. These parameters are encapsulated in MATLAB ® objects - robot objects can be created by the user for any serial-link manipulator and a number of examples are provided for well know robots such as the Puma 560 and the Stanford arm amongst others. The Toolbox also provides functions for manipulating and converting between datatypes such as vectors, homogeneous transformations and unit-quaternions which are necessary to represent 3-dimensional position and orientation. This ninth release of the Toolbox has been significantly extended to support mobile robots. For ground robots the Toolbox includes standard path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadcopter flying robot.
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This paper seeks to explain the lagging productivity in Singapore’s manufacturing noted in the statements of the Economic Strategies Committee Report 2010. Two methods are employed: the Malmquist productivity to measure total factor productivity change and Simar and Wilson’s (J Econ, 136:31–64, 2007) bootstrapped truncated regression approach. In the first stage, the nonparametric data envelopment analysis is used to measure technical efficiency. To quantify the economic drivers underlying inefficiencies, the second stage employs a bootstrapped truncated regression whereby bias-corrected efficiency estimates are regressed against explanatory variables. The findings reveal that growth in total factor productivity was attributed to efficiency change with no technical progress. Most industries were technically inefficient throughout the period except for ‘Pharmaceutical Products’. Sources of efficiency were attributed to quality of worker and flexible work arrangements while incessant use of foreign workers lowered efficiency.
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The Six Sigma technique is one of the quality management strategies and is utilised for improving the quality and productivity in the manufacturing process. It is inspired by the two major project methodologies of Deming’s "Plan – Do – Check – Act (PDCA)" Cycle which consists of DMAIC and DMADV. Those two methodologies are comprised of five phases. The DMAIC project methodology will be comprehensively used in this research. In brief, DMAIC is utilised for improving the existing manufacturing process and it involves the phases Define, Measure, Analyse, Improve, and Control. Mask industry has become a significant industry in today’s society since the outbreak of some serious diseases such as the Severe Acute Respiratory Syndrome (SARS), bird flu, influenza, swine flu and hay fever. Protecting the respiratory system, then, has become the fundamental requirement for preventing respiratory deceases. Mask is the most appropriate and protective product inasmuch as it is effective in protecting the respiratory tract and resisting the virus infection through air. In order to satisfy various customers’ requirements, thousands of mask products are designed in the market. Moreover, masks are also widely used in industries including medical industries, semi-conductor industries, food industries, traditional manufacturing, and metal industries. Notwithstanding the quality of masks have become the prioritisations since they are used to prevent dangerous diseases and safeguard people, the quality improvement technique are of very high significance in mask industry. The purpose of this research project is firstly to investigate the current quality control practices in a mask industry, then, to explore the feasibility of using Six Sigma technique in that industry, and finally, to implement the Six Sigma technique in the case company to develop and evaluate the product quality process. This research mainly investigates the quality problems of musk industry and effectiveness of six sigma technique in musk industry with the United Excel Enterprise Corporation (UEE) Company as a case company. The DMAIC project methodology in the Six Sigma technique is adopted and developed in this research. This research makes significant contribution to knowledge. The main results contribute to the discovering the root causes of quality problems in a mask industry. Secondly, the company was able to increase not only acceptance rate but quality level by utilising the Six Sigma technique. Hence, utilising the Six Sigma technique could increase the production capacity of the company. Third, the Six Sigma technique is necessary to be extensively modified to improve the quality control in the mask industry. The impact of the Six Sigma technique on the overall performance in the business organisation should be further explored in future research.