Dry electrical discharge machining (EDM) is a modification of the oil EDM process in which the liquid dielectric is replaced by a gaseous dielectric. This study investigates the effects of different types of gas (air, nitrogen, and mixture of argon/air) on the machining characteristics of dry EDM of M35 workpiece material. A Taguchi L27 orthogonal array design was applied to investigate the effects of six control factors, including current, pulse on-time, duty factor, gas pressure, electrode rotational speed and specifically type of gas on machining responses, including material removal rate (MRR), surface roughness, and radial overcut. Also, the surface integrity was investigated in different dielectric mediums. Results show that the argon/air mixture can improve the MRR with respect to air and nitrogen. The best dimensional accuracy can be obtained by using nitrogen as the dielectric medium. Also, the machined surface with nitrogen has the fewest small drops and the microcracks in Aagon/air mixture is more than those air one. So, the argon/air mixture is the best dielectric with respect to nitrogen and air mediums for dry EDM of high-speed steel M35.

In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, (4) Data Analysis System, and (5) Knowledge Extraction System. The functional requirements of the integrated system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry Electrical Discharge Machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In summary, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.

The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, quality assessment, and anomaly detection. A key advancement is the transition from static to adaptive design of experiments (DoE), using real-time analytics for iterative refinement. This paper introduces an innovative adaptive DoE embedded in an expert system for grinding, combining data-driven and knowledge-based methodologies. The KSF Grinding Expert™ system recommends optimized grinding control variables, guided by a multi-objective optimization framework using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Gray Relational Analysis (GRA). The rule-based adaptive DoE iteratively refines recommendations through feedback and historical insights, reducing the number of trials by excluding suboptimal parameters. A case study validates the approach, demonstrating significant enhancements in process efficiency and precision. This knowledge-based adaptive strategy reduces experimental trials, adapts DoE according to different grinding processes, and can prevent critical defects such as surface cracks. In the case study, optimized results which are offered by the expert system and validated with over 90% accuracy are incorporated into the system’s knowledge base, enabling continuous improvement and reduced experimentation in future iterations.

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO₂ footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO₂ footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO₂ footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.