Adaptive control as applied to machine tool systems refers to control of the operating parameters based on measurement of the process characteristics. Even the simplest adaptive control system with feedrate control based on cutting force measurements, is quite complex. The adaptive control loop is of a sampled-data nature with a nonlinear variable gain which depends on the operating parameters themselves. Experiments have shown that an operating computer numerical control/adaptive control system can become unstable due to changes in depth-of-cut or spindle speed.
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Oren Masory is an Assistant Professor of Mechanical Engineering at Texas A&M University in College Station, Texas. He earned his B.S.M.E., M.S.M.E. and Ph.D. in 1974, 1977 and 1980 respectively, from the Technion, Israel Institute of Technology, Haifa, Israel. His current research and teaching activities are in the areas of computer control of manufacturing systems and robotics. Dr. Masory's professional affiliations include RI/SME and ASME.
With today's highly competitive global manufacturing marketplace, the pressure for right-first-time manufacture has never been so high. New emerging data standards combined with machine data collection methods, such as in-process verification lead the way to a complete paradigm shift from the traditional manufacturing and inspection to intelligent networked process control. Low-level G and M codes offer very limited information on machine capabilities or work piece characteristics which consequently, results in no information being available on manufacturing processes, inspection plans and work piece attributes in terms of tolerances, etc. and design features to computer numerically controlled (CNC) machines. One solution to the aforementioned problems is using STEP-NC (ISO 14649) suite of standards, which aim to provide higher-level information for process control. In this paper, the authors provide a definition for process control in CNC manufacturing and identify the challenges in achieving process control in current CNC manufacturing scenario. The paper then introduces a STEP-compliant framework that makes use of self-learning algorithms that enable the manufacturing system to learn from previous data and results in eliminating the errors and consistently producing quality products. The framework relies on knowledge discovery methods such as data mining encapsulated in a process analyser to derive rules for corrective measures to control the manufacturing process. The design for the knowledge-based process analyser and the various process control mechanisms conclude the paper.
Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have non-linear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. To solve this problem, a grey-theory algorithm was introduced into the TFC to predict the next output error of the system and the error change, rather than the current output error of the system and the current error change, as input variables of the TFC. This design of the grey prediction fuzzy controller (GPFC) cannot only simplify the TFC design, but also achieves the desired result in TFC implementation. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The GPFC has better control performance in constant cutting force control than does the TFC, as verified in experimental results.
Yoram Koren is Head of the Robotics Laboratory in the Faculty of Mechanical Engineering at the Technion, Israel Institute of Technology, Haifa, Israel. From 1980 to 1982, Dr. Koren served as the Goebel Professor of the College of Engineering, and as Director of the Integrated Design and Manufacturing Division at the University of Michigan, Ann Arbor, Michigan. His research interests are in the areas of robotics, adaptive control, and computerized manufacturing. Dr. Koren has written more than 50 papers in these areas, as well as having authored a textbook entitled Computer Control of Manufacturing Systems. He is a member of NAMRI/SME, RI/SME, ASME, and CIRP.
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The digitization of factory operations enabled by IoT technologies promises to make that happen. Digital tools will be able to monitor and control all tools of production, collecting data from thousands of sensors to create a digital image of the product being realized, usually referred to as a "digital shadow." Once a digital shadow has been created for a physical product and bears its specific DNA, it is possible to manufacture that product more efficiently and with a higher degree of quality in the digitized production facility. In this way, it is possible to optimize the manufacturing process, detect quality issues early to prevent defects at the end of the production line and make continuous improvements. It is also possible to carry out predictive and preventive maintenance.
Making good products is important for the success of a manufacturer, but it is not enough to be profitable and to sustain business. Production costs must be low enough for a suitable margin. This can be achieved by increasingly improving the efficiency of a manufacturing system. Automation is vital for that. Manufacturing systems require heavy investments and must be designed so that they remain profitable for the long term. If manufacturers are to remain competitive in an ever-changing marketplace, they must continuously improve both products and the production systems. Virtual commissioning is therefore necessary to continuously upgrade a production system with reasonable incremental investments. This requires a virtual (computer-based) environment that can simulate a manufacturing plant.
Virtual commissioning involves a virtual plant and a real controller. The simulated plant model has to be fully defined at the level of sensors and actuators. A major benefit of this is that it replaces the need for real commissioning with real plants and controllers, which is very expensive and time consuming. Instead, virtual commissioning allows for the identification of possible design defects and operational mistakes before investments are made in physical plant infrastructure. The digitization of the manufacturing plant allows its designers to enhance the efficiency of the production process, increase the automation density and optimize the handling of materials necessary to realize the products.
Finally, it is important to highlight the "zero-defect" concept. In some cases, relevant percentages of production can end up as scrap because of manufacturing defects. A "zero-defect" process requires automatic monitoring of the entire manufacturing process, from the quality of raw materials entering the production line to variances in tools and processes during each production run. As a closed-loop system, controllers are immediately alerted to any defects, and changes can be made immediately to eliminate the source of the problem. The approach has the potential to dramatically reduce scrap by detecting production errors instantly, eliminating the propagation of defects along the process stages. The manufacturing system could include knowledge-based loops, providing information and feedback to other levels of the manufacturing chain, to minimize failures via continuous optimization of the production process and the manufacturing system.
contributed to this article in his role as innovation manager at Comau, a multinational company specializing in industrial automation, where he has worked since 2012. He holds a Ph.D. in industrial production engineering from Parma University and an M.Sc. in computer science from the University of Milan. He has extensive experience in methodologies and tools for product and production system design. Since 2000, he has been involved in various international research projects in the product design and manufacturing area, ranging from methodologies for product design for manufacturing to process design for energy efficiency. From 2007 to 2012, he was responsible for a Fiat Group innovation research program on manufacturing purpose.
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