Manufacturing data analytics using a virtual factory representation.

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Soliton-dark pulse pair formation in birefringent cavity fiber lasers through cross phase coupling.

We report on the experimental observation of soliton-dark pulse pair formation in a birefringent cavity fiber laser. Temporal cavity solitons are formed in one polarization mode of the cavity. It is observed that associated with each of the cavity solitons a dark pulse is induced on the CW background of the orthogonal polarization mode. We show that the dark pulse formation is a result of the incoherent cross polarization coupling between the soliton and the CW beam and has a mechanism similar to that of the polarization domain formation observed in the fiber lasers.

Induced dark solitary pulse in an anomalous dispersion cavity fiber laser.

We report on the formation of induced dark solitary pulses in a net anomalous dispersion cavity fiber laser. In a weak birefringence cavity fiber laser simultaneous laser oscillation along the two orthogonal polarization directions of the cavity could be achieved. Under suitable conditions bright cavity solitons could be formed along one polarization direction while CW emission occurs along the orthogonal polarization direction. In a previous paper we have shown that under incoherent polarization coupling a bright soliton always induces a broad dark pulse on the CW beam. In the paper we further show that under coherent polarization coupling a bright soliton could further induce either a weak bright or a dark solitary pulse on the bottom of the broad dark pulse. Numerical simulations have also well reproduced the experimental observations, and further show whether a weak dark or bright solitary pulse is induced is determined by the presence or absence of a phase jump in the induced pulse.

Carbon footprint of maize-wheat cropping system after 40-year fertilization.

Two main challenges which human society faces for sustainable development goals are the maintenance of food security and mitigation of greenhouse gas (GHG) emissions. Here, we examined the impacts of six fertilization treatments including unfertilized control (CK), mineral nitrogen (N, 90 kg N ha-1), mineral N plus 30 kg P ha-1 phosphorus (NP), NP combined with 3.75 Mg ha-1 straw (NP + Str), farmyard manure (Man, 75 Mg ha-1), and NP combined with manure (NP + Man) on crop productivity and carbon emissions (soil GHG emission; GHGI, yield-based GHG intensity; NGHGB, net GHG balance; carbon footprint, CF) in a maize-wheat cropping system during two years (April 2018-June 2020) in a semi-arid continental climate after 40 years of fertilization in the Northwest China. Manure and straw increased total GHG by 38-60 % compared to the mineral fertilizers alone, which was mainly due to the 49-80 % higher direct emissions of carbon dioxide (CO2) rather than nitrous oxide (N2O). Compared to the N fertilizer alone, organic amendments and NP increased cumulative energy yield by 134-202 % but decreased GHGI by 38-55 %, indicating that organic fertilizers increased crop productivity at the cost of higher GHG emissions. When the soil organic carbon changes (ΔSOC) were accounted for in the C emission balance, manure application acted as a net C sink due to the NGHGB recorded with -123 kg CO2-eq ha-1 year-1. When producing the same yield and economic benefits, the manure and straw addition decreased the CF by 59-85 % compared to N fertilization alone. Overall, the transition from mineral to organic fertilization in the semi-arid regions is a two-way independent solution to increase agricultural productivity along with the reduction of C emissions.

Framework for a Digital Twin in Manufacturing: Scope and Requirements.

Digital twin has the potential to be an important concept for achieving smart manufacturing. However, there remains a lot of confusion about the concept and how it can be implemented in real manufacturing systems, especially among small-to-medium-sized enterprises. This paper synthesizes the different perspectives that have been reported on the digital twin to identify the key characteristics that must be understood when developing a digital twin for a specific use case. Example applications are provided and the need for a standardized framework, such as the one under development as ISO 23247 (Digital Twin Manufacturing Framework), is motivated. This framework can enable context-dependent implementations and promote composability and reusability of digital twin components.

Leguminous supplementation increases the resilience of soil microbial community and nutrients in Chinese fir plantations.

Understory vegetation plays a vital role in the flow of materials and nutrient cycling in plantation ecosystems. Introducing functional plants (one species or a group of plants that share similar characteristics and can play a similar role in an ecological environment) can quickly improve the environment of the soil of a plantation with a single-stand structure suffering from soil degradation. Five stands composed of Chinese fir plants of different ages (young, immature, near-mature, mature, and over-mature stand forests) were supplemented with leguminous plants to determine the effects on soil nutrients and microbial communities. We supplemented the five stands with five different combinations of four non-native plant species, Dalbergia balansae, Taxus chinensis, Spatholobus suberectus, and Kaempferia galangal, as treatments. After one year, plant growth was estimated, and soil samples were collected for laboratory experiments and high-throughput sequencing. Our results show that supplementing the stands with plants increased the nutrient content of the soil and promoted the growth and diversity of soil microbial communities in Chinese fir plantations. Furthermore, the effects of plant supplementation varied according to the age of the stand in the plantation; thus, the positive effects were stronger for young, immature, and near-mature stand forests than they were for mature and over-mature stand forests. Measurements of the microbial diversity in the soil revealed that supplementation increased diversity in the fungal community more than that in the bacterial community. A principal component analysis (PCA) of the five treatments and controls under different forest stands ages demonstrated that microbial communities differed significantly between treatments and controls and that supplementing Chinese fir plantations with leguminous plants had a greater influence on microbial communities than other plants did. Our study suggests that certain leguminous plants can increase soil nutrients and the diversity of soil microbial communities in one year.

Standards-based integration of advanced process control and optimization.

Integration of process control with optimization is critical to Smart Manufacturing (SM). Oftentimes, however, the process control solutions from one vendor do not interoperate with the optimization solutions of another. Incompatibilities among the representation and format used by the vendors can impede interoperability. Without this interoperability, it is impossible to achieve the higher level of decision support essential to SM. We believe that an emerging standard, ISO 15746, can facilitate semantic interoperability and enable the integration of process control with optimization. This paper reports the implementation and validation of ISO 15746, Automation systems and integration - Integration of advanced process control and optimization (APC-O) capabilities for manufacturing systems. Guided by the standard, we modelled major components of a typical APC-O system using tools from different vendors, implemented the information models defined in the standard, and integrated key system functions such as process optimization, process control, and user interface. A chemical process case based on the Tennessee-Eastman problem is used to demonstrate the implementation and validation of the standard. We developed a simulation of the chemical process and integrated it with the APC-O system. We discuss the standard validation experience and the findings will be used to guide advance development of the standard.

Modeling and Optimization of Manufacturing Process Performance using Modelica Graphical Representation and Process Analytics Formalism.

This paper concerns the development of a design methodology and its demonstration through a prototype system for performance modeling and optimization of manufacturing processes. The design methodology uses a Modelica simulation tool serving as the graphical user interface for manufacturing domain users such as process engineers to formulate their problems. The Process Analytics Formalism, developed at the National Institute of Standards and Technology, serves as a bridge between the Modelica classes and a commercial optimization solver. The prototype system includes (1) manufacturing model components' libraries created by using Modelica and the Process Analytics Formalism, and (2) a translator of the Modelica classes to Process Analytics Formalism, which are then compiled to mathematical programming models and solved using an optimization solver. This paper provides an experiment toward the goal of enabling manufacturing users to intuitively formulate process performance models, solve problems using optimization-based methods, and automatically get actionable recommendations.

Virtual Factory Framework for Supporting Production Planning and Control.

Developing optimal production plans for smart manufacturing systems is challenging because shop floor events change dynamically. A virtual factory incorporating engineering tools, simulation, and optimization generates and communicates performance data to guide wise decision making for different control levels. This paper describes such a platform specifically for production planning. We also discuss verification and validation of the constituent models. A case study of a machine shop is used to demonstrate data generation for production planning in a virtual factory.

Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models.

In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision-support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.

Implementing the ISO 15746 Standard for Chemical Process Optimization.

This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization - and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times - each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.

INTEGRATING DATA ANALYTICS AND SIMULATION METHODS TO SUPPORT MANUFACTURING DECISION MAKING.

Modern manufacturing systems are installed with smart devices such as sensors that monitor system performance and collect data to manage uncertainties in their operations. However, multiple parameters and variables affect system performance, making it impossible for a human to make informed decisions without systematic methodologies and tools. Further, the large volume and variety of streaming data collected is beyond simulation analysis alone. Simulation models are run with well-prepared data. Novel approaches, combining different methods, are needed to use this data for making guided decisions. This paper proposes a methodology whereby parameters that most affect system performance are extracted from the data using data analytics methods. These parameters are used to develop scenarios for simulation inputs; system optimizations are performed on simulation data outputs. A case study of a machine shop demonstrates the proposed methodology. This paper also reviews candidate standards for data collection, simulation, and systems interfaces.