Manufacturing Environment Research Articles

Energy-Efficient Industrial Internet of Things in Green 6G Networks

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.

Electrobiofabrication of antibody sensor interfaces within a 3D printed device yield rapid and robust electrochemical measurements of titer and glycan structure.

We report the integration of 3D printing, electrobiofabrication, and protein engineering to create a device that enables near real-time analysis of monoclonal antibody (mAb) titer and quality. 3D printing was used to create the macroscale architecture that can control fluidic contact of a sample with multiple electrodes for replicate measurements. An analysis "chip" was configured as a "snap-in" module for connecting to a 3D printed housing containing fluidic and electronic communication systems. Electrobiofabrication was used to functionalize each electrode by the assembly of a hydrogel interface containing biomolecular recognition and capture proteins. Specifically, an electrochemical thiol oxidation is used to assemble a thiolated polyethylene glycol hydrogel, that in turn is covalently coupled to either a cysteine-tagged protein G that binds the antibody's Fc region or a lectin that binds the glycans of target mAb analytes. We first show the design, assembly, and testing of the hardware device. Then, we show the transition of a step-by-step sensing methodology (e.g., mix, incubate, wash, mix, incubate, wash, measure) into the current method where functionalization, antibody capture, and assessment are performed in situ and in parallel channels. Both titer and glycan analyses were found to be linear with antibody concentration (to 0.2 mg/L). We further found the interfaces could be reused with remarkably similar results. Because the interface assembly and use are simple, rapid, and robust, we suggest this assessment methodology will be widely applicable, including for other biomolecular process development and manufacturing environments.

Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments.

Bill-of-materials visualization for aerospace & defense: A digital transformation retrospective

We look back at seven years of work to reduce test cost in complex aerospace manufacturing organizations using a bill-of-materials visualization, describing the evolution from a data integration team to a data analytics team, and several of the innovations we developed to be successful. We illustrate a maturity path for digital transformation using open-source tools that visualize over a hundred products, comprised of thousands of parts and bills-of-material to optimize factory flow. We aggregate past work for reducing test cost in aerospace manufacturing, reinforcing and extending work of Deming, and place it in the context of evolving data science capabilities, specifically so others can evaluate how they might accelerate their own digital transformations. We follow the development of a single product from its conception through its deployment and use, a production flow visualization of bills-of-materials plus operations, and discuss how it facilitates analytics and decision-making, depicting complementary visual analytics capabilities built to support it. Reducing cost of testing in complex manufacturing environments is our primary focus by understanding how testing contributes to product cost, and to gain insight into the testing process to reduce testing – and ultimately product – cost. Complex manufacturing organizations considering digital transformations can evaluate the principles, approach, and problems we encountered throughout the team's evolution, specifically, how to extend Deming's work complemented by standard bill-of-materials plus operations visualizations to make critical manufacturing optimizations.

The impacts of emotional intelligence and IOT on operational efficiency in manufacturing: A cross-cultural analysis of Nigeria and the US

This review paper explores the dual impacts of Emotional Intelligence (EI) and the Internet of Things (IoT) on Operational Efficiency (OE) in the manufacturing sectors of Nigeria and the United States, providing a comprehensive cross-cultural analysis. In an era where manufacturing industries globally are undergoing rapid technological advancements and organizational transformations, understanding the synergistic role of human and technological factors is crucial. EI, which encompasses the ability to recognize, understand, and manage emotions, significantly influences leadership effectiveness, team dynamics, and overall workplace harmony. Concurrently, the integration of IoT technologies facilitates real-time data monitoring, predictive maintenance, and enhanced automation, leading to substantial improvements in OE. The paper first delineates the conceptual frameworks of EI and IoT, highlighting their individual contributions to manufacturing efficiency. Subsequently, it discusses the comparative analysis of the manufacturing environments in Nigeria and the US, examining how cultural contexts shape the application and impact of EI and IoT. In the United States, advanced technological infrastructure and a culture of innovation foster the widespread adoption of IoT and the integration of EI in organizational practices. Conversely, Nigeria's manufacturing sector, while facing challenges such as infrastructural deficits and skill gaps, shows potential for growth through targeted investments in technology and human capital development. Through this cross-cultural lens, the paper identifies key differences in the perceptions and applications of EI, the extent of IoT integration, and the resulting OEs. The findings suggest that while the US benefits from a mature ecosystem for both EI and IoT, Nigeria's unique socio-economic landscape necessitates tailored strategies to harness these tools effectively. The analysis concludes with policy and practical implications, offering insights for enhancing manufacturing efficiency through balanced and culturally sensitive approaches to technological and EI advancements. This comprehensive review underscores the importance of synergizing human and technological capabilities to drive sustainable growth and competitiveness in the global manufacturing sector. Keywords: Emotional Intelligence, Internet of Things, Operational Efficiency, Manufacturing, Cross-Cultural Analysis, Nigeria and United States.

Smart industrial information integration: a lightweight privacy protection model in an intelligent manufacturing architecture

In the industrial ecosystem, both the manufacturing and edge clouds collaborate via virtualisation technology, playing a crucial role in the efficient aggregation and processing of vast data from endpoints, offering computational and analytical support for enhanced decision-making in all industrial operations. Consequently, ensuring the reliability of virtualised manufacturing environments is paramount. Remote attestation offers a viable solution, however, current schemes are plagued by inefficiencies and privacy leakage issues. To address these concerns, we propose an efficient remote attestation approach. The approach actively conceals sensitive industrial information in measurement logs by using a hash algorithm, thereby preventing privacy breaches. Furthermore, it streamlines log entries with a log compression algorithm, enhancing remote verification efficiency in the industrial information architecture. Extensive experiments were conducted in an authentic cloud environment by applying this approach across both mesh and hierarchical remote verification structures, reducing time overhead by approximately 81.32%. Additionally, it marginally impacts server performance with only a 1.49% reduction. The results affirm the proposed approach's exceptional efficacy in decreasing verification durations and mitigating remote verification attacks, demonstrating its promising utility and feasibility in the intelligent industrial platform.

Abnormal pattern recognition for online inspection in manufacturing process based on multi-scale time series classification

The collection of large volumes of temporal data during the production process is streamlined in a cyber manufacturing environment. The ineluctable abnormal patterns in these time series often serve as indicators of potential manufacturing faults. Consequently, the presence of effective analytical methods becomes essential for monitoring and recognizing these abnormal manufacturing patterns. However, the extensive process data may contain various minor abnormal patterns, typically reflecting changes in production status influenced by multiple anomalous causes. This study introduces an approach for recognizing abnormal manufacturing patterns through multi-scale time series classification (TSC). Long-term process signals undergo slicing using dynamically sized observation windows and subsequent classification at multiple scales employing our proposed TSC model, the distance mode profile-multi-branch dilated convolution network (DMP-MDNet). DMP-MDNet comprises two key modules aimed at bypassing complicated feature engineering and enhancing generalization capability. The first module, DMP, uses similarity measurement to encode scale- and magnitude-invariant temporal properties. Subsequently, the MDNet, equipped with multi-receptive field sizes, effectively leverages multi-granularity data for accurate classification. The effectiveness of our method is demonstrated through the analysis of a real-world body-in-white production dataset and various widely used public TSC datasets, showing promising applicability in actual manufacturing processes.

Fuzzy expert system for ergonomic assembly line worker assignment and balancing problem under uncertainty

ABSTRACT In the era of Industry 5.0, there is a significant gap in addressing ergonomic risks and imprecise task times in manufacturing systems. This study aims to fill this gap by extending the ergonomic assembly line balancing problem with worker assignment. It employs a novel two-phase framework combining a constructive heuristic for feasibility with a unique ergonomic assessment method developed through a fuzzy expert system. Validated using 96 synthesized numerical instances, the proposed method addresses the scarcity of fuzzy and ergonomic-oriented data in benchmarks. Then, computational results are thoroughly analyzed to evaluate the method’s performance and identify potential areas for further research. The proposed optimization method provided high-quality solutions with a majority demonstrating low ergonomic risk and high worker safety, contributing to an overall improvement in ergonomic conditions. The integration of fuzzy expert system and advanced optimization techniques yields a robust framework for achieving a safer and more efficient manufacturing environment.

Integrating MATLAB Image Processing with PLC-Based Automation Systems for Enhanced Process Control in Robotic Manufacturing Environments

Integrating MATLAB Image Processing with PLC-Based Automation Systems for Enhanced Process Control in Robotic Manufacturing Environments

Visual defect obfuscation based self-supervised anomaly detection

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalies. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: (1) time-consuming inference due to multiple masking, (2) output inconsistency by random masking, and (3) inaccurate reconstruction of normal patterns for large masked areas. Motivated by this, this study proposes a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolves the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the performance than emptying those regions by binary masking, thereby overcomes issue 3. The proposed approach achieves a high performance without any change of the model structure. Promising results are shown through laboratory tests with public industrial datasets. To suggest EAR be possibly adopted in various industries as a practically deployable solution, future steps include evaluating its applicability in relevant manufacturing environments.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

Deep learning model for optimizing control and planning in stochastic manufacturing environments

Within the context of Industry 4.0, manufacturing plants implement smart technologies, which adopt machine learning and deep learning, to identify manufacturing problems and provide sensible solutions to the experts. In literature, such applications have demonstrated high-performance in terms of feature extraction and big data analysis compared to typical machine learning methods. However, in the context of control and planning, the research efforts are still focused on integrating ad-hoc solutions, e.g., heuristic algorithms and deep reinforcement learning, thus making it difficult to generate general scheduling rules for manufacturing processes that could be easily applied to a variety of stochastic manufacturing systems. To this end, this paper considers a deep learning application for addressing the scheduling optimization problem in multi-stage stochastic manufacturing environments. The aim is the formulation of a predictive model that could derive suitable authorizations for activities under uncertainty. Findings demonstrate that the proposed predictive model generates policies that improve the performance of the integrated operations within manufacturing systems, constituting to efficient and sustainable manufacturing with effective material management and near-to-zero waste.

Aligning a collaborative resistance model with a change management process: a case study in automation in industry

This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen interviews of eight engineers, a model is created to understand how the company of study changed a manufacturing activity while noting obstacles identified in the process. This model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in the literature to understand critical elements found within change management. The case study aligns with both a design process and a traditional change management process defined in the literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the change management process.

Aspects of microbiological monitoring of the production environment in pharmaceutical and biotechnological manufacturing

Aspects of microbiological monitoring of the production environment in pharmaceutical and biotechnological manufacturing

Review of surface metrology artifacts for additive manufacturing

Test artifacts, resembling real machine parts, allow quantitative evaluation of system performance and insight into individual errors, aiding in improvement and standardization in additive manufacturing. The article provides a comprehensive overview of existing test artifacts, categorized based on geometric features and material used. Various measurement techniques such as stylus profilometry and computed tomography are employed to assess these artifacts. It was shown that Selective Laser Melting (SLM) technology and titanium alloys are prevalent in artifact creation. Specific artifact categories include slits, angular aspects, length parameters, variable surfaces, and others, each accompanied by examples from research literature, highlighting diverse artifact designs and their intended applications. The paper critically discusses the main problems with existing geometries. It paper underscores the importance of user-friendly and unambiguous artifacts for dimensional control, particularly in surface metrology. It anticipates the continued growth of metrological verification in future manufacturing environments, emphasizing the need for precise and reliable measurement results to support decision-making in production conditions

Considerations for Tungsten Carbide as Tooling in RFSSW.

Tool wear is a key issue for the manufacturing performance of refill friction stir spot welding in high-volume manufacturing environments. As such, the aim of this study is to examine conditions in which tungsten carbide with a cobalt binder can succeed as a tool material in the spot welding of 2029 aluminum for a sustained lifetime. Critical factors are shown herein to include cleanliness and thermal management. The life of a WC-Co toolset is demonstrated to be approximately 2998 welds, which is of the same scale as conventional steel tooling. With a WC-Co shoulder and probe, the H13 clamp showed the only significant wear.

Construction of digital twin workshop integrated with edge computing and deep learning

Abstract This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments.

Advanced Analysis and Problem-Solving in the Industry: A Strategic Approach to Assisting PT. XYZ in Overcoming Operational Challenges

This research aims to solve operational challenges at PT. XYZ using advanced methodologies and strategic approaches, with a specific focus on enhancing Overall Equipment Effectiveness (OEE) and Total Productive Maintenance (TPM). OEE serves as a critical performance metric encompassing availability, performance, and quality to provide a comprehensive view of equipment efficiency. The integration of TPM, emphasizing proactive and preventive maintenance, further supports these goals by engaging all employees in maintaining and improving equipment performance. The research prioritizes first-tier machine improvements such as optimizing the gripper area, pneumatic systems, strap holding clamps area, nose wire, transfer conveyor, and cutting area. Additionally, second-tier enhancements in production systems involve refining scheduling processes to prevent operator shortages, providing ongoing training to maintain operator proficiency, and ensuring readiness across all production needs. These measures aim to systematically address the root causes of inefficiencies, ultimately enhancing overall operational effectiveness in alignment with organizational objectives and standards, thereby fostering a more streamlined and productive manufacturing environment.

The Big Role of Data Analytics in the Industrial Internet of Things

An IoT architecture is presented in the article which is a predictive way to maintain a group of buses using J1939 sensor data[1]. The authors adopted a minimally viable prototype (MVP) at the Socie´te´ de Transport de l’Outaouais (STO) garage in Gatineau, Canada, which was able to capture around 1 GB of uncompressed J1939 data per bus every day.[1] Data is a combination of the sensor reading and consists of data for the wheel speed, vehicle distance, driver pedal positions, engine information, oil information, coolant information, and transmission fluid information. The authors are trying to come up with a variant of Consensus Self-Organized Models (COSMO) named ICOSMO, which can self-fit the sensor selection for anomaly detection. The fourth industrial revolution has changed the face of ancient manufacturing by bringing in new technologies such as sensors, AI, IoT, and big data analytics. This equipment is used to convert factories into intelligent and interconnected systems that generate a huge amount of data. The data collected, when properly processed, can be utilized to improve decision making and reliability and safety. In addition, it makes predictive maintenance inevitable, thus minimizing the downtime and optimization of the performance[2]. Industrial big data is identified by the ”5V” elements: volume, velocity, variety, veracity, and value. Due to these characteristics, traditional data processing techniques become inadequate, so these circumstances require novel approaches to the treatment of the numerous and diverse data types that are produced in industry 4.0. The Proposed ICOSMO Infrastructure for IoT Predictive Maintenance consists of the following key activities: 1) finding out which sensor classes are the reasons for the faulty components, 2) adaptation of the sensor class (SC) and the sensor prediction capability (SPC) using fault detection abilities and 3) COSMO sensor configuration has to be rearranged after a certain period of time. ICOSMO has been verified with data from a minimally viable prototype (MVP) deployed on buses at the Socie´te´ de Transport de l’Outaouais in Canada. Furthermore, the interviewees said that the framework also includes techniques for the composition and the characterization of industrial big data, which is essential to make a practical predictive maintenance system that can work in smart manufacturing environments