A control methodology for automated manufacturing

The application of computers in the manufacturing industry has substantially altered the control procedures used to program a whole manufacturing process. Currently, one the problems which automated manufacturing systems are experiencing is the lack of a good overall control system. The subject of this research has been centred on the identification of the problems involved in current methods of control and their advantages and disadvantages in an automated manufacturing system. As a result, a different type of control system has been proposed which distributes both the control and the decision making. This control model is an hybrid of hierarchical and hierarchical control systems which takes advantage of the best points offered by both types of control structures. The Durham FMS rig has been used as a testbed for an automated manufacturing system to which the hybrid control system has been applied. The implementation of this control system would not have been possible without the design and development of a System Integration Tool (SIT). The system is capable of real-time scheduling of the system activities. Activities within the system are monitored in real-time and a recording of the system events is available, which allows the user to analyse the activities of the system off-line. A network independent communication technique was developed for the Durham FMS which allowed the manufacturing cells to exercise peer-to-peer communication. The SIT also allowed the integration of equipment from different vendors in the FMS

CNN YOLO를 이용한 열화상기반 영상과 점구름 기반 엔드밀 감시 시스템

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022. 8. 안성훈.As adoption of smart-factory system in manufacturing becoming inevitable, autonomous monitoring system in the field of machining has become viral nowadays. Among various methods in autonomous monitoring, vision-based monitoring is the most sought-after. This system uses vision sensors integrated with detection models developed through deep learning. However, the disadvantage of being greatly affected by optical conditions, such as ambient lighting or reflective materials, critically affects the performance in terms of monitoring. Instead of vision sensors, LiDAR, which provides depth map by measuring light returning time using infrared radiation (IR) directly to the object, can be complementary method. The study presents a LiDAR ((Light Detection and Ranging)-based end mill state monitoring system, which renders strengths of both vision and LiDAR detecting. This system uses point cloud and IR intensity data acquired by the LiDAR while object detection algorithm developed based on deep learning is engaged during the detection stage. The point cloud data is used to detect and determine the length of the endmill while the IR intensity is used to detect the wear present on the endmill. Convolutional neural network based You Only Look Once (YOLO) algorithm is selected as an object detection algorithm for real-time monitoring. Also, the quality of point cloud has been improved using data prep-processing method. Finally, it is verified that end mill state has been monitored with high accuracy at the actual machining environment.제조 분야에서 스마트 팩토리 시스템의 도입으로 인해 가공 과정의 무인 모니터링 시스템이 필연적으로 도입되고 있다. 무인 모니터링의 다양한 방법 중 비전 기반 모니터링이 가장 많이 쓰이고 있다. 해당 비전 기반 시스템의 경우 딥 러닝을 통해 개발된 감지 모델과 통합된 비전 센서를 사용한다. 하지만 주변 조명이나 반사 물질과 같은 광학적 조건에 크게 영향을 받는 단점은 모니터링 측면에서 성능에 치명적인 영향을 미치기에 이를 보완하는 대안이 필요하다. 이 연구에서는 비전 센서 대신 적외선(IR)을 물체에 직접 조사하여 빛의 왕복 시간을 측정하여 깊이 정보를 측정하는 LiDAR를 이용하여 비전 센서의 한계를 보완하는 시스템을 소개한다. 또한 비전과 LiDAR 감지의 장점을 모두 제공하는 LiDAR 기반 엔드밀 상태 모니터링 시스템을 제시한다. 이 시스템은 LiDAR에서 획득한 점 구름 정보 및 IR 강도 데이터를 사용하며, 딥 러닝을 기반으로 개발된 객체 감지 알고리즘은 감지 단계와 엔드밀의 길이를 감지하고 측정하는 데 사용되며 IR 강도는 엔드밀에 존재하는 마모 혹은 파손 정보를 감지하는 데 사용된다. 실시간 모니터링을 위한 객체 감지 알고리즘으로 YOLO(You Only Look Once) 알고리즘을 기반으로 하는 컨볼루션 신경망이 선택되었으며 데이터 전처리를 통해 포인트 클라우드의 품질을 향상시켰다. 마지막으로 실제 가공 환경에서 엔드밀 상태를 높은 정확도로 모니터링하는 과정을 진행하였다.1. Introduction . 1 1.1 Tool monitoring in CNC machines 1 1.2 LiDAR and point cloud map. 5 1.3 IR intensity application 7 2. System modelling 9 2.1 End mill monitoring system overview 9 2.2 Hardware setup . 11 2.3 End mill failure monitoring 15 2.4 YOLO setup 18 3. Data processing . 19 3.1 Confidence score. 19 3.2 Noise removal 20 3.3 Point cloud accumulation. 22 3.4 IR intensity monitoring 26 4. Experiments and results . 28 4.1 Data gathering 28 4.2 Training 30 4.3 Results . 32 5. Conclusion . 39 Reference 41 Abstract (In Korean) 43석

Methodologies for CIM systems integration in small batch manufacturing

This thesis is concerned with identifying the problems and constraints faced by small batch manufacturing companies during the implementation of Computer Integrated Manufacturing (CIM). The main aim of this work is to recommend generic solutions to these problems with particular regard to those constraints arising because of the need for ClM systems integration involving both new and existing systems and procedures. The work has involved the application of modern computer technologies, including suitable hardware and software tools, in an industrial environment. Since the research has been undertaken with particular emphasis on the industrial implementor's viewpoint, it is supported by the results of a two phased implementation of computer based control systems within the machine shop of a manufacturing company. This involved the specific implementation of a Distributed Numerical Control system on a single machine in a group technology cell of machines followed by the evolution of this system into Cell and Machine Management Systems to provide a comprehensive decision support and information distribution facility for the foremen and uperators within the cell. The work also required the integration of these systems with existing Factory level manufacturing control and CADCAM functions. Alternative approaches have been investigated which may have been applicable under differing conditions and the implications that this specific work has for CIM systems integration in small batch manufacturing companies evaluated with regard not only to the users within an industrial company but also the systems suppliers external to the company. The work has resulted in certain generic contributions to knowledge by complementing ClM systems integration research with regard to problems encountered; cost implications; the use of appropriate methodologies including the role of emerging international standard methods, tools and technologies and also the importance of 'human integration' when implementing CIM systems in a real industrial situation

The Infrared Cloud Monitor for the MAGNUM Robotic Telescope at Haleakala

We present the most successful infrared cloud monitor for a robotic telescope. This system was originally developed for the MAGNUM 2-m telescope, which has been achieving unmanned and automated monitoring observation of active galactic nuclei at Haleakala on the Hawaiian island of Maui since 2001. Using a thermal imager and two aspherical mirrors, it at once sees almost the whole sky at a wavelength of $\lambda\sim 10\mu{\rm m}$. Its outdoor part is weather-proof and is totally maintenance-free. The images obtained every one or two minutes are analysed immediately into several ranks of weather condition, from which our automated observing system not only decides to open or close the dome, but also selects what types of observations should be done. The whole-sky data accumulated over four years show that 50$-$60 % of all nights are photometric, and about 75 % are observable with respect to cloud condition at Haleakala. Many copies of this system are now used all over the world such as Mauna Kea in Hawaii, Atacama in Chile, and Okayama and Kiso in Japan.Comment: 18 pages, 15 figures, 7 tables, accepted for publication in PAS

The Factory of the Future

A brief history of aircraft production techniques is given. A flexible machining cell is then described. It is a computer controlled system capable of performing 4-axis machining part cleaning, dimensional inspection and materials handling functions in an unmanned environment. The cell was designed to: allow processing of similar and dissimilar parts in random order without disrupting production; allow serial (one-shipset-at-a-time) manufacturing; reduce work-in-process inventory; maximize machine utilization through remote set-up; maximize throughput and minimize labor

Estimating Future Costs for Infrastructure in the Proposed Canadian Northern Corridor at Risk From Climate Change

This paper reviews current climate change projections for northern Canada and considers what these mean for infrastructure development in the proposed Canadian Northern Corridor (CNC). We focus on chokepoints along the corridor’s notional route and estimate future costs of infrastructure along the chokepoints. We draw upon climate change projections at the end of the century (2100) using information from several climate variables sourced on the CMIP6 and CMIP5 reports. Climate variables include means and extreme values for temperature, precipitation, wind and their indirect impacts on physical features: permafrost, freezing rain and wildfires. In terms of infrastructure costs, we investigate investment costs and the useful life of nine sectors within transportation, energy and buildings infrastructures. The findings of our analysis show that mean temperatures within the CNC area could increase by 10.9oC, and precipitation by 45 per cent by 2100. Climate change could create chokepoints along the CNC route, affecting key areas essential for transportation flow. Central regions of the corridor are projected to have a higher probability of receiving concomitant impacts on several chokepoints, including combined threats from the increasing frequency of wildfires, freezing rain and permafrost thaw. Adding a climatic layer to investment costs within CNC chokepoints can increase infrastructure costs by more than 101 per cent. Transportation engineering infrastructure, electric power infrastructure and the institutional buildings sectors are most likely to be impacted. Just considering a climate layer to current infrastructure increases costs by more than $12 billion for several hazards such as freezing precipitation (especially Alberta and BC),$7 billion for wildfires (especially BC) and more than $400 million for permafrost (especially Alberta and BC). Infrastructure built along the CNC route will need to be designed to remain functional under different climatic conditions that predominate today. Chokepoints will dictate how buildings and transportation infrastructure should be planned

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists