A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing

Manufacturing is usually performed as a sequence of operations such as forming, machining, inspection, and assembly. A new challenge in manufacturing is to move towards Industry 4.0 (the fourth Industrial revolution) concerning the full integration of machines and production systems with machine learning methods to enable for intelligent multistage manufacturing. This paper discusses Multistage Manufacturing Processes (MMPs) and develops a probabilistic model based on Bayesian linear regression to estimate the results of final inspection associated with comparative coordinate measurement given in-process measured coordinates. The results of two case studies for flatness tolerance evaluation demonstrate the effectiveness of the probabilistic model which aims at being part of a larger metrology informatics system to be developed for predictive analytics and agent-based advanced control in multistage manufacturing. This solution relying on accurate models can minimise post-process inspection in mass production with independent measurements

Six-Sigma Quality Management of Additive Manufacturing

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps—define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM

Architecting Networked Engineering Systems

The primary goal in this dissertation is to create a new knowledge, make a transformative influence in the design of networked engineering systems adaptable to ambitious market demands, and to accommodate the Industry 4.0 design principles based on the philosophy that design is fundamentally a decision making process. The principal motivation in this dissertation is to establish a computational framework that is suitable for the design of low-cost and high-quality networked engineering systems adaptable to ambitious market demands in the context of Industry 4.0. Dynamic and ambitious global market demands make it necessary for competitive enterprises to have low-cost manufacturing processes and high-quality products. Smart manufacturing is increasingly being adopted by companies to respond to changes in the market. These smart manufacturing systems must be adaptable to dynamic changes and respond to unexpected disturbances, and uncertainty. Accordingly, a decision-based design computational framework, Design for Dynamic Management (DFDM), is proposed as a support to flexible, operable and rapidly configurable manufacturing processes. DFDM has three critical components: adaptable and concurrent design, operability analysis and reconfiguration strategies. Adaptable and concurrent design methods offer flexibility in selection of design parameters and the concurrent design of the mechanical and control systems. Operability analysis is used to determine the functionality of the system undergoing dynamic change. Reconfiguration strategies allow multiple configurations of elements in the system. It is expected that proposed computational framework results in next generation of networked engineering systems, where tools and sensors communicate with each other via the Internet of Things (IoT), sensors data would be used to create enriched digital system models, adaptable to fast-changing market requirements, which can produce higher quality products over a longer lifetime and at a lower cost. The computational framework and models proposed in this dissertation are applicable in system design, and/or product-service system design. This dissertation is a fundamental research and a way forward is DFDM transition to the industry through decision-based design platform. Decision-based design platform is a step toward new frontiers, Cyber-Physical-Social System Design, Manufacturing, and Services, contributing to further digitization

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments

System-level Quality Planning and Diagnosis for Complex Multistage Manufacturing Processes.

The performance of a multistage manufacturing process (MMP) can be measured by quality, productivity and cost. All these measures are influenced by the variation of the key product characteristics (KPC’s). To remain competitiveness, variation of KPC’s should be reduced to ensure efficient delivery of quality products. However, the unprecedentedly high requirements on quality make variation reduction a very challenging problem. To reduce KPC variation, massive data are generated and collected from different phases of product realization, including quantitative data and qualitative data. The heterogeneous data poses great challenges to traditional quality assurance methodologies, which emphasize monitoring of manufacturing processes but provide limited diagnostic information. Taking advantage of readily available data, this research focuses on system-level methodology for effective quality assurance of MMP’s in the following aspects: (i) A mathematical variation propagation model is developed to describe the process induced variation and its propagation along multiple manufacturing stages. The generic formulation makes it capable to model a wide variety of processes where 3-D dimensional variation is of interest. The modeling concept and techniques can be extended and applied in early phases of product realization to effectively evaluate product and process design alternatives. (ii) A quality assured setup planning methodology is developed to address the quality assurance in the process design phase of product realization. Setup planning is formulized as an optimal sequential decision making problem and is solved based on analytical evaluation. This research creates the potential for future works on concurrent development of system-level setup and fixture planning. The setup planning results can be further utilized for process diagnosis in the manufacturing phase of product realization. (iii) An engineering-driven factor analysis methodology is developed to diagnose an MMP based on qualitative rather than quantitative representation of product/process interactions. By using the qualitative indicator vectors to direct the estimation of true spatial patterns from multivariate measurement data, the variation sources are identified. The diagnostic results are robust to unknown process changes. The proposed methodologies represent the initial research efforts in a general framework of unified methodology for quality assurance of MMP’s. Based on them, future research directions are identified and discussed.Ph.D.Mechanical Engineering and Industrial and Operations EnginUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60659/1/jliuzz_1.pd

NASA thesaurus. Volume 1: Hierarchical Listing

There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems