Abstract The optimization of energy consumption is an emerging topic in the manufacturing sector because it is the first and most likely step for the transition toward a greener manufacturing strategy. The present study focuses on monitoring and optimizing the energy consumption of milling machines, which are essential tools in modern manufacturing and are used by many manufacturing companies but which also consume a large amount of energy and generate significant environmental impacts. This study presents a step-by-step methodology for energy profiling of milling machines using vector-quantization–based unsupervised machine learning. The process includes long-term power monitoring, preprocessing with peak shaving, clustering into machine states using K-means, and subsystem-level analysis. Data were collected from a PAMA Speedram 2000 milling machine, and the approach demonstrated its ability to differentiate between operational states and identify energy optimization opportunities. Results show that adjusting auxiliary system duty cycles based on machine states can reduce total energy use by more than 50 % in some scenarios. Our findings indicate that specific operational modes exhibit distinct energy-consumption characteristics, which can be leveraged to enhance the efficiency of milling operations. A scenario that implements some solutions to develop a greener milling process is presented based on the partial use of the most energy-demanding auxiliary systems.

Abstract The advent of Industry 4.0 technologies has significantly impacted industries, including biobased product manufacturing. Recognizing their potential to enhance product circularity and sustainability, this study investigates the integration of Industry 4.0 technologies into the biobased product value chain. It also explores how these technologies aid sustainability assessments across the biobased manufacturing industry. Through a systematic literature review from January 1991 to June 2023, 37 relevant articles were identified from over 2,000 results. These articles address three key questions concerning product sustainability assessment approaches, the use of Industry 4.0 technologies for evaluating the sustainability of both conventional and biobased products, and the integration of these technologies with sustainability assessment methodologies, engineering tools, and software. The review highlights challenges within existing sustainability assessment methods and emphasizes the need for a holistic and integrated sustainability strategy across the product life cycle. It also demonstrates how Industry 4.0 technologies can overcome limitations in traditional methods, enabling better evaluation across the biobased value chain. Lastly, the article suggests future research opportunities, including exploring Industry 4.0-driven symbiotic relationships between industries to create circular economy models that reduce waste and enhance resource efficiency.

Abstract 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 Modeling Smart and Sustainable Manufacturing and Infrastructure Enabling 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 chains Computer science, physical science, synthesis/processing science, sustainability science, and engineering R&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.

Abstract The high heat resistance of Inconel 718 poses challenges in the machining process. Smart machining factor adoption can improve its machinability. This study is focused on investigating the use of Al2O3-ZrO2 ceramic inserts in turning process of Inconel 718 to examine the impact varying machining settings on surface roughness, tool wear, and material removal rate. The turning process input variables used were feed rate (f), cutting speed (v), and depth of cut (d). The influence of each variable on surface roughness, tool wear, and material removal rate was analyzed using two-dimensional surface plots and the main effect plot. Scanning electron microscopy and energy-dispersive spectrographic examination were conducted to explore the various wear pattern mechanisms on the tool face. Analysis of variance was used to determine the percentage impact of all turning process variables on response variables. The quadratic mathematical model for surface roughness and material removal rate showed strong concordance with both experimental and predicted results. Abrasive marks were perceived on the tool face when experiments conducted at a high cutting speed (565.4 m/min), a minimal depth of cut (0.1 mm), and a low feed rate. The depth of cut was increased from 0.1 to 0.5 mm, and while keeping a constant cutting speed, the abrasive area increased. The presence of nickel, chromium, and iron proposes the adhesion of Inconel 718, leading to the formation of various oxide layers at high temperatures. As a consequence, built-up layers and a built-up edge are formed on the tool face. The study gives insight on the significance of Al2O3-ZrO2 ceramic inserts in improving the machinability of Inconel 718 and provides valuable insights into the wear mechanisms during the machining process.

Abstract Design for circular economy (DfCE) aims to systematically incorporate circular economy (CE) considerations during the design phase. In this article, we introduce an integrated quantitative framework that concurrently assesses product functionality, CE, and sustainability performance to enable a more holistic DfCE. This framework enables coupling multiple life-cycle phase simulation models for estimating the effects of parameterized changes in a product’s design or life-cycle behavior on its CE and sustainability performance. We showcase the ability of the proposed framework to support CE- and sustainability-centric design optimization and design space exploration using a case study on a commercial flange coupling. Results show that geometric optimization, to a certain extent, can compensate for material substitution. Furthermore, we show the existence of trade-offs between the above three indicators and that optimizing the flange coupling design to reduce global warming potential results in an increase in energy intensity for the same material composition. The case study shows the potential of the presented modeling framework to provide meaningful insights for DfCE. We demonstrate that the developed framework supports DfCE by highlighting interdependencies between product life-cycle data and their influence on CE and sustainability performance, which can be difficult to assess through other means. This research facilitates the integration of circularity considerations into simulation-based design by leveraging existing engineering simulation models and provides concrete design guidance on how products can be redesigned for CE.

Abstract Induction hardening is a heat treatment that has been increasingly employed in the industry in recent years. It is a complex, highly coupled, and multiphysical process involving electromagnetism, thermal, mechanical, and metallurgical physics. One of the main quality requirements of the process is the hardened case depth generated in the workpiece. The usual method to measure the hardened case and ensure the quality of the parts is to use destructive techniques, which generate material and energy waste and production inefficiencies. Additionally, selecting process parameters such as current, frequency, or scanning speed typically requires several trial-and-error iterations. The goal of this work is to provide a hybrid digital twin (DT) that acts as a nondestructive test technique, predicting the resulting hardened case in real-time and enabling the correction of process parameters during the induction hardening process, ultimately achieving a zero-waste manufacturing scheme. For this purpose, a DT based on an artificial neural network (ANN) model is developed, predicting the hardened case depth in real-time using four monitored input variables: induction frequency, current, and two temperature measurements on the surface of the hardened part. The required data for DT development and training are obtained using a finite element model. Several ANN architectures are evaluated, and the configuration with the best regression results is chosen for implementation in an industrial induction hardening machine. The hardened case predictions obtained from the developed DT demonstrate high accuracy within the analyzed frequency and current range.

ABSTRACT In the pursuit of innovative biosensing technologies for critical applications such as early breast cancer detection, the development of efficient and portable devices is crucial. This work describes a unique stereolithography (SLA)-based three-dimensional–printed microfluidic device intended particularly for optofluidic biosensing with just microliter quantities of blood, similar to diabetes monitoring devices. Unlike typical cumbersome lab equipment such as the Biacore machine, which needs large blood sample volumes and laboratory processing, microfluidic technology allows for patient-operated, at-home testing, decreasing the requirement for hospital visits. The main contribution of this study is to optimize the SLA printing parameters, namely the exposure duration, in order to improve the microfluidic chip’s transparency and channel quality. This improvement allows for the exact immobilization of biorecognition components within the channels, resulting in sensitive and efficient biomarker detection. By extending the exposure duration, we considerably increase the structural integrity and optical clarity of the microfluidic channels, which are critical for successful biosignal transduction in labeled sensing applications. This development not only leads to a cheaper cost and faster manufacturing compared with conventional technologies but also offers increased performance in real bio-sensing applications. Thus, our work represents a big step forward in the development of accessible, efficient, and compact devices for early-stage illness diagnosis, outperforming existing lab-based diagnostics.

ABSTRACT The job shop scheduling problem is a classical optimization challenge aimed at determining the optimal processing order by assigning a set of resources to a corresponding set of operations. This article investigates various approaches to address the online job shop scheduling problem, employing a simulation-based study. Dispatching rules are applied to allocate resources to operations, with discrete event simulation used for problem assessment. The study also incorporates human productivity factors, specifically investigating the shortest processing time (SPT) dispatching rule in a separate scenario. Five distinct scenarios are simulated, including four dispatching rules (first in, first out; last in, first out; longest processing time; and SPT) and an additional scenario integrating the SPT rule and human productivity factors. The simulation results are used to compare makespan across these scenarios, revealing that the scenario involving the SPT dispatching rule and human productivity factors represents the shortest makespan. Through a TOPSIS technique-based ranking, considering makespan and cost as criteria, the study identifies the SPT rule and human productivity factors as the most efficient scenario. The findings imply that employing human productivity factors with effective dispatching rules, such as SPT, can significantly improve job shop scheduling operational efficiency and lead to more optimal results in both makespan and overall operational costs.

ABSTRACT Smart manufacturing has opened tremendous opportunities to access, collect, and analyze a plethora of process and product data. Simultaneously, the manufacturing domain presents unique challenges regarding data for current modeling approaches—be it machine learning or physics-based models. In this paper, we highlight the opportunity presented by combining data-driven and physics-based models in a hybrid approach to address these data challenges. The paper provides a depiction of the unique data challenges in the manufacturing domain, illustrates the different facets of data analytics in manufacturing (including physics-based, data-driven, and hybrid modeling), and provides a qualitative mapping of fit for the different modeling classes on the data challenge dimensions.

ABSTRACT In modern industries, there is a growing demand for high-quality parts that are flexible, efficiently produced, and that minimize the wastage of raw materials to meet consumer needs. Computers are essential in various manufacturing sectors, but the integration of computer-aided design (CAD) and computer-aided manufacturing (CAM) remains a challenge for many industries. Feature recognition (FR) serves as a critical link in integrating CAD and CAM. This paper presents a concise review of CAD, CAM, and FR, as well as feature classifications, feature representation, feature recognition methods, and criticisms of different FR techniques in the context of prismatic, rotational, and sheet metal part features.