Duck Bong Kim

Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC

The ability to predict performance of manufacturing equipment during early stages of process planning is vital for improving efficiency of manufacturing processes. In the metal cutting industry, measurement of machining performance is usually carried out by collecting machine-monitoring data that record the machine tool’s actions (e.g. coordinates of axis location and power consumption). Understanding the impacts of process planning decisions is central to the enhancement of the machining performance. However, current methodologies lack the necessary models and tools to predict impacts of process planning decisions on the machining performance. This paper presents the development of a virtual machining model (called STEP2M model) that generates machine-monitoring data from process planning data. The STEP2M model builds upon a physical model-based analysis for the sources of energy on a machine tool, and adopts STEP-NC and MTConnect standardised interfaces to represent process planning and machine-monitoring data. We have developed a prototype system for 2-axis turning operation and validated the system by conducting an experiment using a Computer Numerical Control lathe. The virtual machining model presented in this paper enables process planners to analyse machining performance through virtual measurement and to perform interoperable data communication through standardised interfaces.

Decision guidance methodology for sustainable manufacturing using process analytics formalism

Sustainable manufacturing has significant impact on a company’s business performance and competitiveness in today’s world. A growing number of manufacturing industries are initiating efforts to address sustainability issues; however, to achieve a higher level of sustainability, manufacturers need methodologies for formally describing, analyzing, evaluating, and optimizing sustainability performance metrics for manufacturing processes and systems. Currently, such methodologies are missing. This paper introduces a systematic decision-guidance methodology that uses the sustainable process analytics formalism (SPAF) developed at the National Institute of Standards and Technology. The methodology provides step-by-step guidance for users to perform sustainability performance analysis using SPAF, which supports data querying, what-if analysis, and decision optimization for sustainability metrics. Users use data from production, energy management, and a life cycle assessment reference database for modeling and analysis. As an example, a case study of investment planning for energy management systems has been performed to demonstrate the use of the methodology.

Developing a decision support system for improving sustainability performance of manufacturing processes

It is difficult to formulate and solve optimization problems for sustainability performance in manufacturing. The main reasons for this are: (1) optimization problems are typically complex and involve manufacturing and sustainability aspects, (2) these problems require diversity of manufacturing data, (3) optimization modeling and solving tasks require specialized expertise and programming skills, (4) the use of a different optimization application requires re-modeling of optimization problems even for the same problem, and (5) these optimization models are not decomposed nor reusable. This paper presents the development of a decision support system (DSS) that enables manufacturers to formulate optimization problems at multiple manufacturing levels, to represent various manufacturing data, to create compatible and reusable models and to derive easily optimal solutions for improving sustainability performance. We have implemented a DSS prototype system and applied this system to two case studies. The case studies demonstrate how to allocate resources at the production level and how to select process parameters at the unit-process level to achieve minimal energy consumption. The research of this paper will help reduce time and effort for enhancing sustainability performance without heavily relying on optimization expertise.

Integrating views of properties in models of unit manufacturing processes

This paper investigates the potential advantages and difficulties of integrating predictive model equations in models of unit manufacturing processes. The method described uses metamodels and semantic web technology to relate equations, as objects, to downstream activities. The potential advantages of this include enhanced knowledge refinement and reuse, traceability, model verification and agility in production activities. In an example usage, the authors apply the method to the development and downstream usage of predictive models of a selective laser sintering process. Use of equations as objects enables linking them with supporting evidence, property definitions and dimensions in an engineering notebook paradigm. Model-based interpretation of the equations enables composition in trade studies and mapping to downstream process parameter optimisation.

A model-based approach to refine process parameters in smart manufacturing

This article describes a model-based approach for defining and refining process parameters in dynamically changing, smart manufacturing environments. This approach uses equation-based models to predict how part quality will respond to changes in that environment. The results from these models provide the major inputs into a process-parameter-optimization technique, which is used to set the values for various process parameters. In developing these models, we integrated various concepts from process improvement frameworks, such as Define–Measure–Analyze–Improve–Control and Monitor–Analyze–Plan–Execute–Knowledge, with techniques from model-based engineering. After describing the approach, we demonstrate its use in an additive manufacturing process example.

Activity model for homogenization of data sets in laser-based powder bed fusion

Purpose Additive manufacturing (AM) processes are the integration of many different science and engineering-related disciplines, such as material metrology, design, process planning, in-situ and off-line measurements and controls. Major integration challenges arise because of the increasing complexity of AM systems and a lack of support among vendors for interoperability. The result is that data cannot be readily shared among the components of that system. In an attempt to better homogenization this data, this paper aims to provide a reference model for data sharing of the activities to be under-taken in the AM process, laser-based powder bed fusion (PBF). Design/methodology/approach The activity model identifies requirements for developing a process data model. The authors’ approach begins by formally decomposing the PBF processes using an activity-modeling methodology. The resulting activity model is a means to structure process-related PBF data and align that data with specific PBF sub-processes. Findings This model in this paper provides the means to understand the organization of process activities and sub-activities and the flows among them in AM PBF processes. Research limitations/implications The model is for modeling AM activities and data associated with these activity. Data modeling is not included in this work. Social implications After modeling the selected PBF process and its sub-processes as activities, the authors discuss requirements for developing the development of more advanced process data models. Such models will provide a common terminology and new process knowledge that improve data management from various stages in AM. Originality/value Fundamental challenges in sharing/reusing data among heterogeneous systems include the lack of common data structures, vocabulary management systems and data interoperability methods. In this paper, the authors investigate these challenges specifically as they relate to process information for PBF – how it is captured, represented, stored and accessed. To do this, they focus on using methodical, information-modeling techniques in the context of design, process planning, fabrication, inspection and quality control.

Sustainable Process Analytics Formalism: A Case Study of Book Binding System for Energy Optimization

Energy is considered as one of the important factors for manufacturers to achieve the sustainability objective. To improve energy efficiency in manufacturing, optimization techniques are essential to provide decision support. However, formulating and solving energy optimization in manufacturing is still time-consuming and difficult due to its complexity with a broad scope. In addition, it is a challenging task since it requires substantial development efforts and modeling expertise. To address this drawback, Sustainable Process Analytics Formalism (SPAF) is proposed to facilitate the modeling and optimization. In this paper, SPAF will be applied to a case study of energy optimization for a book binding production system for its feasibility validation. The knowledge of process flow, data, and metrics of the case study is represented using SPAF, and a preliminary analysis of optimization results was performed.Copyright

AN OVERVIEW OF SUSTAINABILITY INDICATORS AND METRICS FOR DISCRETE PART MANUFACTURING

Sustainable manufacturing has become an emerging environmental, economic, societal, and technological challenge to the industry, the academia, and the government entities. Numerous research and development (R&D) efforts have been launched, and many global and domestic efforts have been initiated toward a long-term sustainable world. This paper provides an overview of R&D efforts in the measurement of manufacturing sustainability, based on an intensive literature search. It focuses on sustainability metrics that apply to unit machining processes for discrete part manufacturing. The authors present results from assessing the scope of indicators that exist for sustainability measurement in general, with a quick visit to the taxonomy of manufacturing activities and different classifications of existing SM metrics by unit machining processes. Most metrics at the unit machining level were developed to measure environmental impacts with respect to energy, materials, water, wastes, and air emissions, while a relatively smaller effort was developed to gauge societal or economic impacts. We report on an analysis of energy metrics available for various unit machining processes at the sub-device and sub-unit process level.Copyright

Fundamental Requirements for Data Representations in Laser-Based Powder Bed Fusion

Additive Manufacturing (AM) processes intertwine aspects of many different engineering-related disciplines, such as material metrology, design, in-situ and off-line measurements, and controls. Due to the increasing complexity of AM systems and processes, data cannot be shared among heterogeneous systems because of a lack of a common vocabulary and data interoperability methods. This paper aims to address insufficiencies in laser-based Powder Bed Fusion (PBF), a specific AM process, data representations to improve data management and reuse in PBF. Our approach is to formally decompose the processes and align PBF process-specifics with information elements as fundamental requirements for representing process-related data. The paper defines the organization and flow of process information. After modeling selected PBF processes and sub-processes as activities, we discuss requirements for the development of more advanced process data models that provide common terminology and process knowledge for managing data from various stages in AM.Copyright