Smart machining system platform for CNC milling with the integration of a power sensor and cutting model

Novel techniques and strategies are investigated for dynamically measuring the process capability of machine tools and using this information for Smart Machine System (SMS) research. Several aspects of the system are explored including system integration, data acquisition, force and power model calibration, feedrate scheduling and tool condition monitoring. A key aspect of a SMS is its ability to provide synchronization between process measurements and model estimates. It permits real time feedback regarding the current machine tool process. This information can be used to accurately determine and keep track of model coefficients for the actual tooling and materials in use, providing both a continued improvement in model accuracy as well as a way to monitor the health of the machine and the machining process. A cutting power model is applied based on a linear tangential force model with edge effect. The robustness of the model is verified through experiments with a wide variety of cutting conditions. Results show good agreement between measured and estimated power. A test platform has been implemented for performing research on Smart Machine Systems. It uses a commercially available OAC from MDSI, geometric modeling software from Predator along with a number of modules developed at UNH. Test cases illustrate how models and sensors can be combined to select machining conditions that will produce a good part on the first try. On-line calibration allows the SMS to fine tune model coefficients, which can then be used to improve production efficiency as the machine learns its own capabilities. With force measurements, the force model can be calibrated and resultant force predictions can be performed. A feedrate selection planner has been created to choose the fastest possible feedrates subject to constraints which are related to part quality, tool health and machine tool capabilities. Monitoring tangential model coefficients is shown to be more useful than monitoring power ratio for tool condition monitoring. As the model coefficients are independent of the cutting geometry, their changes are more promising, in that KTC will increase with edge chipping and breakage, while KTE will increase as the flank wearland expands

Analysis of manufacturing operations using knowledge- Enriched aggregate process planning

Knowledge-Enriched Aggregate Process Planning is concerned with the problem of supporting agile design and manufacture by making process planning feedback integral to the design function. A novel Digital Enterprise Technology framework (Maropoulos 2003) provides the technical context and is the basis for the integration of the methods with existing technologies for enterprise-wide product development. The work is based upon the assertion that, to assure success when developing new products, the technical and qualitative evaluation of process plans must be carried out as early as possible. An intelligent exploration methodology is presented for the technical evaluation of the many alternative manufacturing options which are feasible during the conceptual and embodiment design phases. 'Data resistant' aggregate product, process and resource models are the foundation of these planning methods. From the low-level attributes of these models, aggregate methods to generate suitable alternative process plans and estimate Quality, Cost and Delivery (QCD) have been created. The reliance on QCD metrics in process planning neglects the importance of tacit knowledge that people use to make everyday decisions and express their professional judgement in design. Hence, the research also advances the core aggregate planning theories by developing knowledge-enrichment methods for measuring and analysing qualitative factors as an additional indicator of manufacturing performance, which can be used to compute the potential of a process plan. The application of these methods allows the designer to make a comparative estimation of manufacturability for design alternatives. Ultimately, this research should translate into significant reductions in both design costs and product development time and create synergy between the product design and the manufacturing system that will be used to make it. The efficacy of the methodology was proved through the development of an experimental computer system (called CAPABLE Space) which used real industrial data, from a leading UK satellite manufacturer to validate the industrial benefits and promote the commercial exploitation of the research

Process Comprehension for Interoperable CNC Manufacturing

Over the last 40 years manufacturing industry has enjoyed a rapid growth with the support of various computer-aided systems (CAD, CAPP, CAM etc.) known as CAx. Since the first Numerically Controlled (NC) machine appeared in 1952, there have been many advances in CAx resource capabilities. The information integration and interoperability between different manufacturing resources has become an important and popular research area over the last decade. Computer Numerically Controlled (CNC) machines are an important link in the manufacturing chain and the major contributor to the production capacity of manufacturing industry today. However, most of the research has focused on the information integration of upper systems in the CAD/CAPP /CAM/CNC manufacturing chain, leaving the shop floor as an isolated information island. In particular, there is limited opportunity to capture and feed shopfloor knowledge back to the upper systems. Furthermore, the part programs for the machines are not exchangeable due to the. machine specific postprocessors. Thus there is a further need to consider information interoperability between different CNC machine and other systems. This research investigates the reverse transformation of the CNC part programmes into higher level of process information, entitled process comprehension, to enable the shopfloor interoperability. A novel framework of universal process comprehension is specified and designed. The framework provides a reverse direction of information flow from the CNC machine to upper CAx systems, enabling the interoperability and recycling of the shopfloor knowledge. A prototype implementation of the framework is realised and utilised to demonstrate the functionalities through three industrially inspired test components. The major contribution of this research to knowledge is the new vision of the shopfloor interoperability associated with process knowledge capture and reuse. The research shows that process comprehension of part programmes can provide an effective solution to the issues of the shopfloor interoperability and knowledge reuse in manufacturing industries.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

Synthesis of Manufacturing and Facility Data for Sustainability Analysis

This paper discusses data synthesis of production and facility knowledge for sustainability analysis by applying the ISA 95 "Activity Models of Manufacturing Operations Management" (MOM) model. Presently, production and facility management are "silo" operations, which basically function independently of each other. This paper presents the addition of facility activities to the MOM model, in accordance with the needs for attaining a holistic view of sustainability analysis. Historically, production and facility data are represented in various forms, e.g., data bases, CAD, and spreadsheets, without a common unifying representation. This paper addresses the issue by introduced Core Manufacturing Simulation Data (CMSD) Standard. A case study of the data synthesis for a precision sand casting production facility is provided

Cost modeling and design for manufacturing guidelines for advanced composite fabrication

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references.Experience shows that the majority of costs are committed during the early stages of the development process. Presently, many cost estimation methods are available to the public for metal processing, but there are almost none (excluding proprietary) for advanced composite materials. Therefore, the central objective of this thesis is to provide a comprehensive overview of the costs of common composite production technologies such as Hand Layup, Resin Transfer Molding, Automated Tow Placement, Pultrusion, Forming, and Assembly. The work includes information on investment costs for production equipment and tooling as well as estimation guidelines for labor and material. Designers are presented with Design for Manufacturing guidelines (DFM) explaining how process selection and part design can lead to potential cost saving opportunities. Process based or technical cost models are well suited to quantify manufacturing costs and relate them to part design features, such as size and shape complexity. These physically based scaling principles can be easily adapted to changes in process technology and thereby reducing data requirements. In order to identify all relevant cost drivers, a detailed process plan is compiled for each composite manufacturing method. These processes can include up to 50 process steps and a total of 270 cost equations are used to calculate the cost contribution of each.(cont.) A number of case studies conducted in concert with our industrial sponsors clearly identifies the best point of each production process and for example help to explain the economic benefits of co-curing versus mechanical assembly. Users can therefore study the economic consequences of design changes in detail and consequently highlight any favorable design/process combinations. To further facilitate the comparison of process performance and to promote the feedback from industry all of the models are available on the Internet at http://web.mit.edu/lmp/www/composites/costmodel/.by Sascha M. Haffner.Ph.D

An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

Manufacturing technology has continuously evolved and advanced over the past century; this has led to an increase in the production of consumer and industrial goods driven by simultaneous growth in population and wealth. Despite the resulting economic and labor growth, environmental impacts of manufacturing have increased dramatically due to the dependence on exhaustible material and energy resources necessary to meet these growing product demands. Increasing awareness and concern over these impacts has encouraged sustainable thinking toward managing material resources, alternative energy sources, and advanced manufacturing technologies. However, the primary emphasis of manufacturing system design decision making has remained focused on the reduction of cost of goods sold (in discrete part production) and total production cost (in continuous production). Manufacturing system design decision makers face challenges in defining, evaluating, and implementing sustainable manufacturing practices, which include the time-intensive nature of complex system design and analysis, data integrity, and deficiencies in assessment methods. In particular, the challenges of collecting, curating, analyzing, and presenting environmental, economic, and social metrics and indicators (sustainability performance information) remains a barrier to operational decision-making. Existing assessment methods and tools are not well-suited to evaluating the sustainability performance of manufacturing processes and systems, as they tend to be product-focused and have limited ability to adapt to changes at the manufacturing process or system level. The objective of this dissertation research is to facilitate sustainable manufacturing system design decision making by integrating a systematic and structured information modeling framework with a manufacturing system design approach. To accomplish this goal, the research approach involves four steps: (1) Performing a review of recent literature to identify the existing challenges in the development and application of sustainable manufacturing methods, tool, models, algorithms, metrics, and indicators; (2) Introducing a functional and object-oriented information modeling methodology to characterize the sustainability performance of unit manufacturing processes (UMPs) using the concepts of abstraction and instantiation, which is demonstrated by reusing and extending a manual milling UMP model for two and a half-axis milling process; (3) Applying information modeling approaches in characterizing the sustainability performance of manufacturing process flows composed of UMPs, which is demonstrated for a discrete part manufacturing system; and (4) Synthesizing the results of the prior steps to provide an information modeling framework for sustainable manufacturing system design decision making. The framework is applied to discrete and continuous product manufacturing to demonstrate the flexibility of this system design approach. The framework provides an accessible approach for detailed analysis of the sustainability performance of manufacturing processes and systems by enabling the reuse, extension, and composability of new and previously developed UMP models. The coupling of information modeling concepts (e.g., abstraction, instantiation, and polymorphism) along with hierarchical, structured, and systematic manufacturing system design enables the framework to address the challenges stated above, namely: (1) Modeling complexity is simplified through a bottom-up approach for characterizing individual UMPs, which are built up for system-level characterization; (2) Model development, verification, and validation efforts are reduced by reusing and extending UMP models, thereby also reducing the time-intensity of modeling; (3) Data reliability is improved, since the framework is agnostic of existing process-specific data sources, rather than restricting data sources and types necessary for analysis; and (4) Multi-criteria decision-making is facilitated by using a hierarchical data structure for model-quantified metrics of interest, which supports analysis using decision trees. The research lays a foundation for developing an ontologies based decision support for sustainable manufacturing system design, as ontologies describe relationships and links between systems and sub-systems which enables the framework to have high-fidelity and understanding of the manufacturing system model and data