Smart Sensing in Advanced Manufacturing Processes: Statistical Modeling and Implementations for Quality Assurance and Automation

With recent breakthroughs in sensing technology, data informatics and computer networks, smart manufacturing with intertwined advanced computation, communication and control techniques promotes the transformation of conventional discrete manufacturing processes into the new paradigm of cyber-physical manufacturing systems. The cybermanufacturing systems should be predictive and instantly responsive to incident prevention for quality assurance. Thus, providing viable in-process monitoring approaches for real-time quality assurance is one essential research topic in cybermanufacturing system to allow a closed-loop control of the processes, ensure the quality of products, and consequently improve the whole shop floor efficiency. However, thus far, such in-process monitoring tools are still underdeveloped on the following counts: • For precision/ultraprecision machining processes, most sensor-based change detection approaches are reticent to the anomalies since they largely root in the stationary assumption whilst the underlying dynamics under precision machining processes exhibit intermittent patterns. Therefore, existing approaches are feeble to detect subtle variations which are detrimental to the process; • For shaping processes that realize complicated geometries, currently there is no viable tool to allow a noncontact monitoring on surface morphology evolution that measures critical dimensioning criteria in real time. • For precision machining processes, we aim to present advanced smart sensing approaches towards characterizations of the process, specifically, microdynamics reflecting the fundamental cutting mechanisms as well as variations of microstructure of the material surfaces. To address these gaps, this dissertation achieves the following contributions: • For precision and ultraprecision machining processes, an in-situ anomaly detection approach is provided which allows instant prevention from surface deterioration. The method could be applied to various (ultra)precision processes of which most underlying systems are unknow and always exhibit intermittency. Extensive experimental studies suggest that the developed model can detect in-situ anomalies of the underlying dynamic intermittency; • For shaping processes that require noncontact in-process monitoring, a vision-based monitoring approach is presented which rapidly measures the geometric features during forming process on sheet-based workpieces. Investigations into laser origami sheet forming processes suggest that the presented approach can provide precise geometric measurements as feedback in real time for the control loop of the sheeting forming processes in cybermanufacturing systems. • As for smart sensing for precision machining, an advanced in-process sensing/ monitoring approach [including implementations of Acoustic Emission (AE) sensor, the associated data acquisition system and developed advanced machine/deep learning methods] is introduced to connect the AE characteristics to microdynamics of the precision machining of natural fiber reinforced composites. The presented smart sensing framework shows potentials towards real-time estimations/predictions of microdynamics of the machining processes using AE features

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Manufacturing approach to production changeover complexity reduction and its implementation through smart surfaces and process optimization

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonManufacturing approach to complexity reduction and its implementation through smart s and process optimizationIn the last two decades or so, we have seen rapid development in sustainable manufacturing and technologies, which have come to play an increasingly vital role in the manufacturing process improvement and agility enhancement at a manufacturing company. Manufacturers have to responsively compete in the market with a sustainable manner. The main aims of most production facilities is to minimize manufacturing costs while addressing the variety and quality needs of the products. This necessity endorses the importance of flexibility, reconfiguration and responsiveness. To be responsive to the customers’ dynamic needs and reduce production costs, manufacturers often have to produce a variety of products on a single production system but further supported with technical means on agility and sustainability. It often takes time and resources to switch from one product to another on the same production system. Producing a variety of products on a single production system also increases the manufacturing complexity associated with the system and processes. Modern complex products or equipment may have thousands of parts and take a tedious number of manufacturing/assembly steps to make these products. The setup time and resources used in the changeover process are a completely loss while there is no production taking place. For sustainable manufacturing there is an immediate need to eliminate or minimize these loses due to cleaning, changeover and setup while the manufacturing system and production line being shut down. This can be overcome by scientific analysis and understanding of each of the steps of production setup, and some sustainable techniques can be applied to reduce setup time/changeover and improve sustainability of the manufacturing system/process. It is essentially important to investigate the design of a sustainable manufacturing system and the underlying complexity in a scientific manner, so to minimize the production changeover and greatly enhance the productivity and efficiency from the view point of sustainability while supported by multiscale modelling and analysis. This doctoral research aims to investigate the key bottleneck issues in a food packaging manufacturing system through the multiscale sustainable manufacturing approach and the associated implementation perspectives. The approach is described in details in chapter 3 and chapter 4. The research is focused on design of smart surfaces applicable to the packaging equipment and its impact on reducing production changeover and complexity towards a sustainable manufacturing system, which is thoroughly undertaken in light of multiscale modelling and analysis and system engineering simulations. A food manufacturing case study is conducted and actual data is used in liaison with an industrial partner company. In the study, three aspects are considered in production changeover both qualitatively and quantitatively, including reduction of complexity, cleaning of the equipment/machines, and sustainability. Different aspects of the complexity are discussed and explored, and corresponding experiments carried out but focused on using different micro surface structures. Most of the time consumed during changeover is on the cleaning of metal conveyors or machines. Therefore, metal surface structures in micro scale are studied in-depth. A self-cleaning property of ultra-hydrophobic surfaces is investigated and applied to reduce the frequency of the cleaning on the conveyor panel surfaces and thus to reduce the time consumed on their cleaning. Process mapping and facility layout are also studied and discussed during this doctoral study to improve the production changeover process at the macro scale. Additionally, recommendations for automation are made and explored to improve the manufacturing facility performance. A new simulation model is developed for the dedicated food packaging manufacturing system, which can be used as a ‘virtual factory’ and to help model the existing production setup and the process optimization while with the underlying thinking on the scales of both macro and micro combined in a sustainable manufacturing manner

Calibration of Ultra-high-precision Robots Operating in an Unsteady Environment

In recent years nanotechnology has become an enabling technology for the development and fabrication of new innovative products. The growth of micro- and nano-manufacturing lies in the ability of converting micro- and nano-fabrication techniques into mass-production industrial processes, where small-scale products can be economically manufactured in a short period of time. When dealing with nano-scale objects and industrial processes it is necessary to take into account the physics acting at this level of precision. Phenomena such as friction, heat transfer, and adhesion forces have far more dramatic effects on the deformation of the robot geometry at the nano-scale than at macro- and micro-scales, thus affecting the industrial process that the robot will perform. The development of micro- and nano-fabrication techniques thus requires a thorough understanding of the physics behind nanorobotics. Specifically, to enable sub-micrometer accuracy for ultra-high-precision robots it is necessary to acquire a complete knowledge of how all sources of inaccuracy deform the robots at nano-scale. Furthermore, a way to compensate for such effects to maintain an acceptable level of accuracy has to be found. In this thesis we fulfill these needs by proposing a new calibration procedure specifically designed for industrial nano-systems working in a thermally unstable environment, a method to evaluate and compensate for external forces acting on ultra-high-precision robots and a method to relate the calibration of several robots working together. This is done by measuring how each source of inaccuracy deforms the robot, modeling this effect and compensating it in real-time. To allow this modus operandi, we propose a new calibration procedure summarized in the following six steps: Step 0 A judicious design of the robot that takes into account the calibration problem and the pose measurement, Step 1 Study of the sources of inaccuracy linked to the robot and the industrial process that it will perform, Step 2 Measurement of several end-effector poses, Step 3 Identification of a function that describes the robot geometry and its behavior when subjected to the sources of inaccuracy identified in Step 1, Step 4 Implementation of the model found in Step 3 into the robot controller, Step 5 Validation and potential return to Step 1 or Step 0. The effectiveness of this calibration procedure is proven by testing it on three case studies, examined in order of complexity: A 1 DOF (degree(s)-of-freedom) ultra-high-precision linear axis was calibrated while thermal effects were deforming it. The 3 DOF ultra-high-precision parallel robot Agietron Micro-Nano was calibrated while thermal effects and an external force were acting on it. An ultra-high-precision 2-robot system was calibrated while thermal effects were acting on it. Thus, an exhaustive study on relating the references of the two robots was carried out. For each case we developed an appropriate ultra-high-precision measuring system used to acquire the pose of the robot end-effector. We measured the end-effector position throughout the workspace while the sources of inaccuracy were acting on the robot to map how they affect the robot geometry. We used the Stepwise Regression algorithm to identify a mathematical model able to describe the geometric features of the robot while all the sources of inaccuracy are acting on it. The model is then implemented in the robot controller and a validation of the calibration accuracy is performed. For every ultra-high-precision robot considered in this work we reached an absolute accuracy of ±100 nm. We finished the coverage of this thesis by analyzing the nano-indentation process as a calibration confirmation tool and as an industrial process. Furthermore, we describe how to use a multiple ultra-high-precision concurrent system of robots. This work was financed by the FNS (Swiss National Foundation for research)

Comparative study of international marketing strategy of Japanese and Taiwanese CNC machine tool industry in the Asean region

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