Annals of Scientific Society for Assembly, Handling and Industrial Robotics

This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage

NASA Tech Briefs, August 1991

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Mastering Uncertainty in Mechanical Engineering

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering

Development of a machine-tooling-process integrated approach for abrasive flow machining (AFM) of difficult-to-machine materials with application to oil and gas exploration componenets

This thesis was submitted for the degree of Doctor of Engineering and awarded by Brunel UniversityAbrasive flow machining (AFM) is a non-traditional manufacturing technology used to expose a substrate to pressurised multiphase slurry, comprised of superabrasive grit suspended in a viscous, typically polymeric carrier. Extended exposure to the slurry causes material removal, where the quantity of removal is subject to complex interactions within over 40 variables. Flow is contained within boundary walls, complex in form, causing physical phenomena to alter the behaviour of the media. In setting factors and levels prior to this research, engineers had two options; embark upon a wasteful, inefficient and poor-capability trial and error process or they could attempt to relate the findings they achieve in simple geometry to complex geometry through a series of transformations, providing information that could be applied over and over. By condensing process variables into appropriate study groups, it becomes possible to quantify output while manipulating only a handful of variables. Those that remain un-manipulated are integral to the factors identified. Through factorial and response surface methodology experiment designs, data is obtained and interrogated, before feeding into a simulated replica of a simple system. Correlation with physical phenomena is sought, to identify flow conditions that drive material removal location and magnitude. This correlation is then applied to complex geometry with relative success. It is found that prediction of viscosity through computational fluid dynamics can be used to estimate as much as 94% of the edge-rounding effect on final complex geometry. Surface finish prediction is lower (~75%), but provides significant relationship to warrant further investigation. Original contributions made in this doctoral thesis include; 1) A method of utilising computational fluid dynamics (CFD) to derive a suitable process model for the productive and reproducible control of the AFM process, including identification of core physical phenomena responsible for driving erosion, 2) Comprehensive understanding of effects of B4C-loaded polydimethylsiloxane variants used to process Ti6Al4V in the AFM process, including prediction equations containing numerically-verified second order interactions (factors for grit size, grain fraction and modifier concentration), 3) Equivalent understanding of machine factors providing energy input, studying velocity, temperature and quantity. Verified predictions are made from data collected in Ti6Al4V substrate material using response surface methodology, 4) Holistic method to translating process data in control-geometry to an arbitrary geometry for industrial gain, extending to a framework for collecting new data and integrating into current knowledge, and 5) Application of methodology using research-derived CFD, applied to complex geometry proven by measured process output. As a result of this project, four publications have been made to-date – two peer-reviewed journal papers and two peer-reviewed international conference papers. Further publications will be made from June 2014 onwards.Engineering and Physical Sciences Research Council (EPSRC) and the Technology Strategy Board (TSB

International Workshop on MicroFactories (IWMF 2012): 17th-20th June 2012 Tampere Hall Tampere, Finland

This Workshop provides a forum for researchers and practitioners in industry working on the diverse issues of micro and desktop factories, as well as technologies and processes applicable for micro and desktop factories. Micro and desktop factories decrease the need of factory floor space, and reduce energy consumption and improve material and resource utilization thus strongly supporting the new sustainable manufacturing paradigm. They can be seen also as a proper solution to point-of-need manufacturing of customized and personalized products near the point of need

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202