Sudarsan Rachuri

The advances in AI/ML, cloud infrastructure, sensor technologies, edge devices, digital twins, sustainability, product lifecycle engineering and management, circular manufacturing, and related enabling and emerging technologies, have made significant impacts on smart and sustainable manufacturing R&D, technologies, and innovation. We are taking this great opportunity to update and modify the scope of ASTM’s journal Smart and Sustainable Manufacturing Systems (SSMS), while not significantly disrupting the nature of the journal, to help the audience (researchers, students, industry practitioners, and possibly policy experts) and, more broadly, the community of researchers and stakeholders. We will ensure a balance between fundamental research and practical applications within the journal’s scope through peer-reviewed research papers, technical notes, review articles, case studies, and discussions. We will also continue to publish special issues that focus on specific topics of interest. We have scoped the journal under the following focused and interconnected domains and topics: Information, Knowledge, AI/ML Data Analytics, and Semantics ModelingSmart and Sustainable Manufacturing and InfrastructureEnabling Technologies, Computing, and Digital Transformation (Digital Thread and Digital Twins)Product Lifecycle Engineering and Management Our aim is to address the needs of different communities that make a significant impact on problems: Theories, experiments, products, processes, and systems, sustainable engineering, lifecycle engineering, manufacturing, and supply chainsComputer science, physical science, synthesis/processing science, sustainability science, and engineeringR&D, technology, commercial hardware and software solutions, system integration through standards Our hope is to create innovation through a cross-discipline domain approach. We propose to achieve “translational manufacturing” research aimed at translating (converting) results in basic research across disciplines into results that directly benefit humans. Like “bench to bedside” in medical research and “field to fork” in food processing, “translational manufacturing” translates breakthroughs in advanced research into commercial technologies, products, and applications, “lab to market”—linking science and engineering research to commercial outcomes. Thank you for exploring the Smart and Sustainable Manufacturing Systems journal. Please contact the editorial office if you have any questions about submitting a paper to the journal.

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data mimic operations and operational failure. In addition, the data can also be used to fill in real data sets with missing values from real-time data collection. The mimicking of real manufacturing systems in computer-based systems is called “virtual manufacturing”. The computer-based systems execute the manufacturing system models that represent real manufacturing systems. In this paper, we introduce a virtual machining model of milling operations. We developed a prototype virtual machining model that represents 3-axis milling operations. This model is a digital mock-up of a real milling machine; it can generate machine-monitoring data from a process plan. The prototype model provides energy consumption data based on physics-based equations. The model uses the standard interfaces of Step-compliant data interface for Numeric Controls and MTConnect to represent process plan and machine-monitoring data, respectively. With machine-monitoring data for a given process plan, manufacturing engineers can anticipate the impact of a modification in their actual manufacturing systems. This paper describes also how the virtual machining model is integrated into an agent-based model in a simulation environment. While facilitating the use of the virtual machining model, the agent-based model also contributes to the generation of more complex manufacturing system models, such as a virtual shop-floor model. The paper describes initial building steps towards a shop-floor model. Aggregating the data generated during the execution of a virtual shop-floor model allows one to take advantage of data analytics techniques to predict performance at the shop-floor level.

In the metal cutting industry, power consumption is an important metric in the analysis of energy efficiency since it relates to energy consumption of machine tools. Much of the research has developed predictive models that correlate process planning decisions with power consumption through theoretical and/or experimental modeling approaches. These models are created by using the theory of metal cutting mechanics and Design of Experiments. However, these models may lose their ability to predict results correctly outside the required assumptions and limited experimental conditions. Thus, they cannot accurately reflect a diversity of machining configurations; i.e., selections of machine tool, workpiece, cutting tool, coolant option, and machining operation for producing a part, which a machining shop has operated. This paper proposes a predictive modeling approach based on historical data collected from machine tool operations. The proposed approach can create multiple predictive models for power consumption, which can be applicable to the diverse machining configurations. It can create fine-grained models predictable up to the level of a numerical control program. It uses standard-based data interfaces such as STEP-NC and MTConnect to implement interoperable and comprehensive data representations. This paper also presents a case study to demonstrate the feasibility and effectiveness of the proposed approach.

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Ensuring the long-term usability of engineering informatics (EI) artifacts is a challenge, particularly for products with longer lifecycles than the computing hardware and software used for their design and manufacture. Addressing this challenge requires characterizing the nature of EI, defining metrics for EI sustainability, and developing methods for long-term EI curation. In this paper we highlight various issues related to long-term archival of EI and describe the work towards methods and metrics for sustaining EI. We propose an approach to enhance the Open Archival Information System (OAIS) functional model to incorporate EI sustainability criteria, Digital Object Prototypes (DOPs), and end user access requirements. We discuss the end user’s requirements from the point of view of reference, reuse and rationale – the “3Rs” – to better understand the level of granularity and abstractions required in the definition of engineering digital objects. Finally we present a proposed case study and experiment.