Complex Production Environments Research Articles

Smart In-Process Inspection in Human–Cyber–Physical Manufacturing Systems: A Research Proposal on Human–Automation Symbiosis and Its Prospects

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed.

Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features.

In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms' deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.

Advancing Production Management through Industry 4.0 Technologies

The development of Industry 4.0 technologies has brought significant changes to various sectors, including production management. In increasingly complex production environments, the adoption of Industry 4.0 technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics has become crucial for enhancing the efficiency and effectiveness of production pro- cesses. This study aims to explore how Industry 4.0 technologies can be applied in production management to optimize operational performance and reduce re- source wastage. The research employs a quantitative approach with data analysis obtained from case studies of manufacturing companies that have implemented Industry 4.0 technologies. Data collection was conducted through surveys and interviews with production managers, along with the analysis of operational per- formance reports. The results indicate that the implementation of Industry 4.0 technologies significantly improves production efficiency, accelerates response times to market demand changes, and reduces operational costs. These findings suggest that integrating Industry 4.0 technologies into production management can be an effective strategy for addressing the challenges of global competi- tion and changing market dynamics. This research contributes to the production management literature by offering insights into the positive impact of adopting Industry 4.0 technologies in operational contexts. The results are expected to serve as a reference for practitioners in developing more adaptive and innovative production strategies in the digital era.

Robot arm grasping based on YOLOv5 in the perspective of automated production

Abstract The rapid development of industrial intelligence has gradually expanded the application of automated production. As a typical automated production equipment, the robotic arm still faces the problems of low grasping efficiency and high control costs when facing highly integrated and miniaturized components. Given this, to improve the grasping level of the robotic arm in complex production environments, this study first constructs a kinematic mathematical model of the robotic arm. Secondly, based on the algorithm that You Only Look Once, improvements are made to its convolution operation and feature extraction modules, ultimately proposing a new type of robotic arm grasping control model. The results showed that the loss test value of the new model was the lowest at 2.75, the average detection error of the captured object was the lowest at 0.003, and the average detection time was the shortest at 1.28 seconds. The highest success rate for grasping six types of industrial parts was 94%, and the lowest average energy consumption was 35.67 joules. Therefore, research models can significantly improve the grasping performance of robotic arms under various complex conditions, thereby achieving efficient manipulation of robotic arms in industrial automation.

Digital Engineering in Photonics: Optimizing Laser Processing

This article explores the transformative impact of digital engineering on photonic technologies, emphasizing advancements in laser processing through digital models, artificial intelligence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material deformation and defects. The integration of AI further refines these models, improving productivity and quality in applications such as micromachining and cladding. Additionally, the combination of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manufacturing, offering significant improvements in process optimization, defect reduction, and system efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solutions for complex production environments.

Integrated nitrogen fertilizer management for improving wheat yield and the efficiency of water and nitrogen fertilizer use

Improving resource use efficiency through optimized nitrogen (N) management is the key to increasing crop productivity, but it remains unclear whether integrated N management practices could better improve wheat yield and the use of N and water. Here we investigated the effects of individual and integrated N management practices on wheat production in different soil-climatic conditions, and constructed the relationship between the soil-climate factors and N application rate on wheat yield. Results showed that individual N management practice (optimized N application rate only) reduced wheat yield by 2.4 %, increased N use efficiency (NUE) by 45 %, and decreased water use efficiency (WUE) by 2.2 %; however, integrated N management practice (integrating optimized N rate with irrigation, tillage, manure, or straw return) increased yield and NUE on average by 5.4 % and 55 % respectively, without reducing WUE. In different soil-climate conditions, individual N management practice decreased wheat yield from 1.1 % to 5.0 %; however, integrated N management practice increased yield by 8.5 %, 6.4 %, and 5.8 % in soil organic carbon (SOC) ≥ 10 g kg−1, mean annual precipitation ≤ 400 mm, and 10 °C < mean annual temperature ≤ 15 °C, respectively. Mixed-effects model showed that the integrated N management practice had a higher yield by better exploiting the yield-enhancing benefits of SOC. Facing the complex production environment of the future, applying integrated N management practice will be a key alternative for improving grain production and resource use efficiency.

Digital Model of Automatic Plate Turning for Plate Mills Based on Machine Vision and Reinforcement Learning Algorithm

Plate turning is an essential step in the plate rolling process. The traditional control mode relies on the manual observation of billets and mainly manual operation. Manual plate turning becomes an external disturbance of the automatic control system of plate mills, which reduces the reproducibility and accuracy of the rolling process. The automatic plate turning function is urgently needed to improve the control level of the rolling line. In this paper, based on the improved image processing algorithm, the position and angle information of the billet conversion process are detected in real time, and the real-time processing of detection data in a complex production environment is realized. Based on the change in the billet rotation angle in the actual plate turning process, a mathematical model is constructed to simulate the plate turning process. On this basis, the digital model and optimization algorithm for automatic plate turning based on reinforcement learning are established, and the automatic optimization of plate turning speed and accuracy is completed. The field application of data-driven plate turning systems replaces manual plate turning control. The plate turning angle detection error of the system is ≤2°. The average plate turning time of each billet is greatly shortened compared with manual plate turning mode, and the fastest time can be shortened by more than 1 s, which greatly improves the production efficiency and is of great significance for improving the automatic control level and digital upgrade of plate mills.

Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea

Pest target identification in agricultural production environments is challenging due to the dense distribution, small size, and high density of pests. Additionally, changeable environmental lighting and complex backgrounds further complicate the detection process. This study focuses on enhancing the recognition performance of tea pests by introducing a lightweight pest image recognition model based on the improved YOLOv8 architecture. First, slicing-aided fine-tuning and slicing-aided hyper inference (SAHI) are proposed to partition input images for enhanced model performance on low-resolution images and small-target detection. Then, based on an ELAN, a generalized efficient layer aggregation network (GELAN) is designed to replace the C2f module in the backbone network, enhance its feature extraction ability, and construct a lightweight model. Additionally, the MS structure is integrated into the neck network of YOLOv8 for feature fusion, enhancing the extraction of fine-grained and coarse-grained semantic information. Furthermore, the BiFormer attention mechanism, based on the Transformer architecture, is introduced to amplify target characteristics of tea pests. Finally, the inner-MPDIoU, based on auxiliary borders, is utilized as a replacement for the original loss function to enhance its learning capacity for complex pest samples. Our experimental results demonstrate that the enhanced YOLOv8 model achieves a precision of 96.32% and a recall of 97.95%, surpassing those of the original YOLOv8 model. Moreover, it attains an mAP@50 score of 98.17%. Compared to Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8, its average accuracy is 17.04, 11.23, 5.78, 3.75, and 2.71 percentage points higher, respectively. The overall performance of YOLOv8 outperforms that of current mainstream detection models, with a detection speed of 95 FPS. This model effectively balances lightweight design with high accuracy and speed in detecting small targets such as tea pests. It can serve as a valuable reference for the identification and classification of various insect pests in tea gardens within complex production environments, effectively addressing practical application needs and offering guidance for the future monitoring and scientific control of tea insect pests.

Experimental research on the reflection and response characteristics of pressure pulse waves for different gas pipeline blockage materials

AbstractNatural gas pipelines are exposed to complex production and transportation environments, leading to blockages of varying compositions and forms due to various factors. This paper proposes a pressure pulsing wave‐based pipe blockage detection method, and a series of pipeline blockage detection experiments of different materials are carried out, including hydrate blockage, ice blockage, paraffin blockage, and water blockage. Through the blockage detection calculation theory and the reflection and transmission characteristics during pressure wave emission, the blockage position, length, and section blockage rate under different blockage materials are obtained, and the influence of different blockage materials on the reflection and transmission signal characteristics of the pressure pulse wave is analyzed. The results show that the solid blocking material does not noticeably impact the reflectivity and reflective properties of pressure waves, and different cross‐sectional shapes have a noticeable impact on the reflectivity of the pressure wave. The reflected wave waveform of the liquid blockage material is visible but irregular. The average error of blockage location is 0.78%.

Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx

Decision-making in complex production environments is challenging as the information and knowledge requirements must be constantly observed since the ecosystems they operate in are continuously changing. Artificial intelligence (AI) can tackle complexity in decision-making by making machines more intelligent. But reacting to changing or new problems and related decision processes to facilitate the understanding of the involved humans is an equally important problem. Therefore, decision support systems are required to assist complex decisions and enable flexibility to support the decision-makers. Within this scope, we will introduce the Democratic AI-based Decision Support System (DAI-DSS), which is designed and implemented within the EU-funded FAIRWork project, considering both human and machine actors during decision-making. The FAIRWork project proposes a model-based approach to both express high-level decision scenarios and formally describe the decision processes, which are then used as input for configuring the decision support system to meet concrete decision problems.

Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

AbstractThe transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform.

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model

As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.

Surface defect classification of hot-rolled steel strip based on mixed attention mechanism

PurposeThis paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.Design/methodology/approachThe authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.FindingsWith these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.Originality/valueThis study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.

Automated digital twin generation of manufacturing systems with complex material flows: graph model completion

Industry 4.0 determined the emergence of technologies that enable data-driven production planning and control approaches. A digital model can be used to make decisions based on the current state of a manufacturing system, and its efficacy strictly depends on the capability to correctly represent the physical counterpart at any time. Automated model generation techniques such as process mining can significantly accelerate the development of digital twins for manufacturing systems. However, complex production environments are characterized by the convergence of different material and information flows. The corresponding data logs present multiple part identifiers, resulting in the wrong finding of the system structure with traditional process mining techniques. This paper describes the problem of discovering manufacturing systems with complex material flows, such as assembly lines. An algorithm is proposed for the proper digital model generation, aided by the new concept of object-centric process mining. The proposed approach has been applied successfully to two test cases and a real manufacturing system. The results show the applicability of the proposed technique to realistic settings.

Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach

Previous research focuses on approaches of deep reinforcement learning (DRL) to optimize diverse types of the single-objective dynamic flexible job shop scheduling problem (DFJSP), e.g., energy consumption, earliness and tardiness penalty and machine utilization rate, which gain many improvements in terms of objective metrics in comparison with metaheuristic algorithms such as GA (genetic algorithm) and dispatching rules such as MRT (most remaining time first). However, single-objective optimization in the job shop floor cannot satisfy the requirements of modern smart manufacturing systems, and the multiple-objective DFJSP has become mainstream and the core of intelligent workshops. A complex production environment in a real-world factory causes scheduling entities to have sophisticated characteristics, e.g., a job’s non-uniform processing time, uncertainty of the operation number and restraint of the due time, avoidance of the single machine’s prolonged slack time as well as overweight load, which make a method of the combination of dispatching rules in DRL brought up to adapt to the manufacturing environment at different rescheduling points and accumulate maximum rewards for a global optimum. In our work, we apply the structure of a dual layer DDQN (DLDDQN) to solve the DFJSP in real time with new job arrivals, and two objectives are optimized simultaneously, i.e., the minimization of the delay time sum and makespan. The framework includes two layers (agents): the higher one is named as a goal selector, which utilizes DDQN as a function approximator for selecting one reward form from six proposed ones that embody the two optimization objectives, while the lower one, called an actuator, utilizes DDQN to decide on an optimal rule that has a maximum Q value. The generated benchmark instances trained in our framework converged perfectly, and the comparative experiments validated the superiority and generality of the proposed DLDDQN.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

A Cross-Source Point Cloud Registration Algorithm Based on Trigonometric Mutation Chaotic Harris Hawk Optimisation for Rockfill Dam Construction

A high-precision three-dimensional (3D) model is the premise and vehicle of digitalising hydraulic engineering. Unmanned aerial vehicle (UAV) tilt photography and 3D laser scanning are widely used for 3D model reconstruction. Affected by the complex production environment, in a traditional 3D reconstruction based on a single surveying and mapping technology, it is difficult to simultaneously balance the rapid acquisition of high-precision 3D information and the accurate acquisition of multi-angle feature texture characteristics. To ensure the comprehensive utilisation of multi-source data, a cross-source point cloud registration method integrating the trigonometric mutation chaotic Harris hawk optimisation (TMCHHO) coarse registration algorithm and the iterative closest point (ICP) fine registration algorithm is proposed. The TMCHHO algorithm generates a piecewise linear chaotic map sequence in the population initialisation stage to improve population diversity. Furthermore, it employs trigonometric mutation to perturb the population in the development stage and thus avoid the problem of falling into local optima. Finally, the proposed method was applied to the Lianghekou project. The accuracy and integrity of the fusion model compared with those of the realistic modelling solutions of a single mapping system improved.

Investigation of the customized furniture industry’s production management systems

As customers become increasingly involved in the manufacturing process of their products, the manufacturing pattern of many industries is leaning towards customization and personalization, with customized furniture enterprises being one of them. Facing such a complex production environment, an excellent set of production management systems is a powerful weapon to ensure the orderly execution of production. In this paper, the architecture of the production management system, the specific functions undertaken by the different systems, the integration between the production systems and the data communication of the production systems, combined with the actual production, are analyzed with a comprehensive and multi-dimensional way, intending to provide positive reference values for furniture companies or other customized production companies.

Digital twin driven production progress prediction for discrete manufacturing workshop

• This article focuses on a novel synchronous update method for digital twin workshop model with integrating learning approach. • The representation model is established to describe the performance degradation rules of discrete manufacturing workshop. • An Adaboost-DNN-LSTM based synchronous update method is proposed to guarantee the consistency between DTM and physical workshop. • A competitive election mechanism is designed to select base learner among DNN and LSTM between historical and dynamic sample. In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. Digital twin technology offers an enabling tool for PP analysis. Although the production process can be observed, analyzed, and controlled in real-time by digital twin model (DTM), there exist some uncertain events, degradation of manufacturing elements, and abnormal disturbance in physical workshop (PW), which would cause the deviation between DTM and PW performance and affect the prediction accuracy of PP. Synchronous evolution of DTM for precision holding to ensure the consistency between DTM and the performance of PW, and guarantee the accuracy of DTM is still a challenging issue, especially when dealing with new dynamic samples for complex production environment of discrete manufacturing workshop (DMW). This article focuses on how to effectively construct DTM synchronous update methods based on dynamic sample data for DMW. This study proposes a representation model of performance degradation and an Adaboost-DNN-LSTM based synchronous update model with competitive election mechanism to enhance the accuracy of PP prediction with time in industrial environment. The experiment is conducted in the realistic production dataset, which demonstrates that the proposed synchronous evolution model has good performance for realizing the synchronization of the performance of physical workshop in industrial environment, and can greatly improve the prediction ability for PP.