Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model using the Generic Modeling Environment (GME) platform. Physics-based models, which are usually in the form of equations, are assumed to be in a text format. The data is also assumed to be available in a text format. The proposed methodology involves creating a meta-model for the Bayesian network using GME and a syntax representation for the conditional probability tables/ distributions. The actual Bayesian network is an instance model of the Bayesian network meta-model. We describe algorithms for automated BN construction and UQ analysis, which are implemented programmatically using the GME platform. We finally demonstrate the proposed techniques for quantifying the uncertainty in two example systems.

This paper describes a real-time data collection framework and an adaptive machining learning method for constructing a real-time energy prediction model for a machine tool. To effectively establish the energy consumption pattern of a machine tool over time, the energy prediction model is continuously updated with new measurement data to account for time-varying effects of the machine tool, such as tool wear and machine tool deterioration. In this work, a real-time data collection and processing framework is developed to retrieve raw data from a milling machine tool and its sensors and convert them into relevant input features. The extracted input features are then used to construct the energy prediction model using Gaussian Process (GP) regression. To update the GP regression model with real-time streaming data, we investigate the use of sparse representation of the covariance matrix to reduce the computational and storage demands of the GP regression. We compare computational efficiency of sparse GP to that of full GP regression model and show the effectiveness of the sparse GP regression model for tracking the variation in the energy consumption pattern of the target machine.

Recent advances in machine automation and sensing technology offer new opportunities for continuous condition monitoring of an operating machine. This paper describes an intelligent machine monitoring framework that integrates and utilizes data collection, management, and analytics to derive an adaptive predictive model for the energy usage of a milling machine. This model is designed using a Gaussian Process (GP) regression algorithm, which is a flexible regression method that also provides an uncertainty estimate. To improve computational efficiency, we propose a Collective Gaussian Process (CGP) in which the overall energy prediction is made by constructing local GP models weighted by probability distribution functions obtained using the Gaussian Mixture Model (GMM) technique. Finally, we demonstrate the ability of the proposed monitoring framework to construct an energy prediction model to predict the energy used to machine a part.

This paper is focused on decision analytics for smart manufacturing. We consider temporal manufacturing processes with stochastic throughput and inventories. We demonstrate the use of the recently proposed concept of the decision guidance analytics language to perform monitoring, analysis, planning, and execution tasks. To support these tasks we define the structure of and develop modular reusable process component models, which represent data, decision/control variables, computation of functions, constraints, and uncertainty. The tasks are then implemented by posing declarative queries of the decision guidance analytics language for data manipulation, what-if prediction analysis, decision optimization, and machine learning.

Data analytics is proving to be very useful for achieving productivity gains in manufacturing. Predictive analytics (using advanced machine learning) is particularly valuable in manufacturing, as it leads to production improvement with respect to the cost, quantity, quality and sustainability of manufactured products by anticipating changes to the manufacturing system states. Many small and medium manufacturers do not have the infrastructure, technical capability or financial means to take advantage of predictive analytics. A domain-specific language and framework for performing predictive analytics for manufacturing and production frameworks can counter this deficiency. In this paper, we survey some of the applications of predictive analytics in manufacturing and we discuss the challenges that need to be addressed. Then, we propose a core set of abstractions and a domain-specific framework for applying predictive analytics on manufacturing applications. Such a framework will allow manufacturers to take advantage of predictive analytics to improve their production.

Supporting different stakeholder viewpoints across the product lifecycle requires semantic richness to represent product-related information and enable multiview engineering simulations. This paper proposes a multilevel product modeling framework enabling stakeholders to define product models and relate them to physical or simulated instances. The framework is defined within the Model-Driven Architecture using the multilevel (data, model, metamodel) approach. The data level represents real-world products, the model level describes models (product models) of real-world products, and the metamodel level describes models of the product models. The metamodel defined in this paper is specialized from a web ontology language enabling product designers to express the semantics of product models in an engineering-friendly way. The interactions between these three levels are described to show how each level in the framework is used in a product engineering context. A product design scenario and user interface for the product metamodel is provided for further understanding of the framework.

Tolerances impose restrictions on the possible deviations of features from their nominal sizes/shapes. These variations of size/shape could be thought of as deviations of a set of generalized coordinates defined at some convenient point on a feature. Any tolerance specification for a feature imposes some kind of restrictions or constraints on its deviation parameters. These constraints, in general, define a bounded region in the deviation space. In this paper, a method has been presented for converting tolerance specifications as per maximum material condition (MMC)/least material condition (LMC)/regardless of feature size (RFS) material conditions for standard mating features (planar, cylindrical, and spherical) into a set of inequalities in a deviation space. Both virtual condition boundaries Virtual condition boundary: A constant boundary generated by the collective effects of a size feature's specified MMC or LMC material condition and the geometric tolerance for that material condition. (VCB) and tolerance zones are utilized for these mappings. The mapping procedures have been illustrated with an example. Note to Practitioners-This paper deals with methods to convert tolerance specification as per ASME Y14.5M into a set of generalized deviation of features and vice-versa. These are intermediate relationships that are required for use in deviation-based tolerance synthesis methods. In this work, different examples have been presented to show how different tolerance specifications (such as positional tolerance at maximum material condition (MMC), least material condition (LMC), etc.) applied to different features could be treated on a generalized basis for tolerancing of manufactured parts.

In this paper, a generic model for the synthesis of tolerances for manufactured parts is presented. The model uses a method of transforming traditional tolerance specifications (as defined in ASME Y14.5M) to a generalized coordinate system (hereinafter referred to as deviation space). Small displacement torsors (SDTs) have been used for representing the deviations. The tolerance synthesis method is formulated as a constrained nonlinear optimization process. Three different types of constraints have been considered for the optimization process: 1) representation of assemblability; 2) mapping of tolerance specification to deviations; and 3) functional requirements. A new deviation-based cost of manufacturing model has been proposed. A working module of the scheme has been implemented and the process has been elaborated with two examples. The possibility of extension of the model and scope for further generalization have been discussed. Note to Practitioners-This paper presents an optimization method for finding tolerance values for different features of an assembly of manufactured parts. Determination of different types of tolerances and their exact values for critical features of any part has been an ad-hoc process; it has been mostly experience-based till recent time. In this paper, efforts have been made to establish a strong mathematically oriented method for synthesis of tolerances. The procedure attempts to minimize cost of manufacturing while the functionality and assemblability are satisfied. It is a generalized method and could be applied for designing rigid manufacture parts. For practical usage, this method should be integrated with three-dimensional computer-aided design packages as a tolerance synthesis module for integrated tolerance design.

Tolerancing decisions can profoundly impact the quality and cost of electro-mechanical assemblies. Existing approaches to tolerance analysis and synthesis in design entail detailed knowledge of geometry of the assemblies and are mostly applicable during advanced stages of design, leading to a less than optimal design process. During the design process of assemblies, both the assembly structure and associated tolerance information evolve continuously. Therefore, significant gains can be achieved by effectively using this information to influence the design of the assembly. Motivated by this, we identify and explore two goals for future research that we believe can enhance the scope of tolerancing for the entire design process. The first goal is to advance tolerancing decisions to the earliest possible stages of design. This issue raises the need for effective representation of tolerancing information during different stages of design and for effective assembly modeling. The second goal addresses the appropriate, synergistic use of available methods and best practices for tolerance analysis and synthesis, at successive stages of design. Pursuit of these goals leads to the definition of a multilevel approach that enables tolerancing to be addressed at successive stages of design in an incremental fashion. The resulting design process, which we call the design for tolerance process, integrates three important domains: (1) design activities at successive stages of design; (2) assembly models that evolve continuously through the design process; (3) methods and best practices for tolerance analysis and synthesis. We demonstrate major steps of our proposed approach through a simple, yet illustrative, example.

Tolerancing decisions during the design of electromechanical products profoundly affect cost and quality. Existing approaches to tolerance analysis and synthesis entail detailed knowledge of geometry of the assemblies and are mostly applicable during advanced stages of design, leading to a less than optimal design process. During design, both the assembly structure and associated tolerance information evolve continuously and significant gains can be achieved by effectively using this information to influence the design. We explore two goals of research to expand the scope of tolerancing to the entire design process. The first goal is to advance tolerancing decisions to the earliest possible stages of design. This issue raises the need for effective representation of tolerancing information during early stages. The second goal addresses the appropriate use of industry best practices and efficient computational approaches. Pursuit of these goals leads to the definition of a multilevel approach that enables tolerancing to be addressed at successive stages of design in an incremental, continuous ongoing fashion. The resulting design process, which we call the design-for-tolerance process, integrates three important domains: (1) design activities at successive stages of design; (2) assembly models that evolve continuously through the design process; and (3) methods and best practices for tolerance analysis and synthesis. We demonstrate major steps of our proposed approach through a simple, yet illustrative, example.

A novel approach to the numerical solution of the vector field associated with constrained mechanical systems, called the perturbation approach, is introduced. It is a specialization of a method of solving general vector fields due to Shampine. The perturbation approach has several advantages over other approaches. The performance of the approaches on a constrained robot problem is used to show the efficacy of the new approach.>