| 1. |
- Akhlaq, Muhammad, et al.
(author)
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Designing an integrated driver assistance system using image sensors
- 2012
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In: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145. ; January, s. 1-24
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Journal article (peer-reviewed)abstract
- Road accidents cause a great loss to human lives and assets. Most of the accidents occur due to human errors, such as bad awareness, distraction, drowsiness, low training, and fatigue. Advanced driver assistance system (ADAS) can reduce the human errors by keeping an eye on the driving environment and warning a driver to the upcoming danger. However, these systems come only with modern luxury cars because of their high cost and complexity due to several sensors employed. Therefore, camera-based ADAS are becoming an option due to their lower cost, higher availability, numerous applications and ability to combine with other systems. Targeting at designing a camera-based ADAS, we have conducted an ethnographic study of drivers to know what information about the driving environment would be useful in preventing accidents. It turned out that information on speed, distance, relative position, direction, and size and type of the nearby objects would be useful and enough for implementing most of the ADAS functions. Several camera-based techniques are available for capturing the required information. We propose a novel design of an integrated camera-based ADAS that puts technologies-such as five ordinary CMOS image sensors, a digital image processor, and a thin display-into a smart system to offer a dozen advanced driver assistance functions. A basic prototype is also implemented using MATLAB. Our design and the prototype testify that all the required technologies are now available for implementing a full-fledged camera-based ADAS.
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| 2. |
- Bi, Z. M., et al.
(author)
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Improved Control and Simulation Models of a Tricycle Collaborative Robot
- 2008
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In: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145. ; 19:6, s. 715-722
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Journal article (peer-reviewed)abstract
- The objective of collaborative manufacturing is to create the synergism from the collaboration of manufacturing resources. Most of the studied collaborations are made among intelligent machines; however, the collaboration can be realized even between machines and human being, and a collaborative robot (Cobot) belongs to the latter. A cobot is a robot designed to assist human beings as a guide or assistor in a constrained motion. Various prototypes have been developed and the potentials of these robots have been demonstrated. The research presented in this paper focuses on the control and simulation models of a tricycle cobot with three steered wheels, with the following two contributions: (i) A concise model for the closed-loop control is developed. Existing closed-loop control has been implemented in an intuitive way, and some control parameters have to be determined by a trial-and-error method. (ii) A simulation model is proposed to validate the control algorithms. No simulation model is available and the control models of other existing systems have to be validated experimentally. The developed control and simulation models have been implemented. Graphic simulation is also developed. Case studies are provided and the simulation results are analyzed.
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| 3. |
- Deng, T., et al.
(author)
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Federated learning-based collaborative manufacturing for complex parts
- 2022
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In: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145.
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Journal article (peer-reviewed)abstract
- The manufacturing of complex parts, such as aircraft structural parts and aero-engine casing parts, has always been one of the focuses in the manufacturing field. The machining process involves a variety of hard problems (e.g. tool wear prediction, smart process planning), which require assumptions, simplifications and approximations during the mechanism-based modelling. For these problems, supervised machine learning methods have achieved good results by fitting input–output relations from plenty of labelled data. However, the data acquisition is difficult, time consuming, and of high cost, thus the amount of data in a single enterprise is often limited. To address this issue, this research aims to realise the equivalent manufacturing data sharing based on federated learning (FL), which is a new machine learning framework to use the scattered data while protecting the data privacy. An enterprise-oriented framework is first proposed to find FL participants with similar data resources. Then, the machining parameter planning task for aircraft structural parts is taken as an example to propose an FL model, which mines the knowledge and rules in the historical processing files from multiple enterprises. In addition, to solve the data difference among enterprises, domain adaptation method in transfer learning is used to obtain domain-invariant features. In the case study, a prototype platform is developed, and to validate the performance of the proposed model, a data set is built based on the historical processing files from three aircraft manufacturing enterprises. The proposed model achieves the best performance compared with the model trained only with the data from a single enterprise, and the model without domain adaptation.
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| 4. |
- Dias-Ferreira, Joao, et al.
(author)
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BIOSOARM : a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors
- 2016
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In: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145. ; , s. 1-24
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Journal article (peer-reviewed)abstract
- Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.
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| 5. |
- Gerber, Tobias, et al.
(author)
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Towards a seamless integration between process modeling descriptions at Business and Production levels - work in progress
- 2014
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In: Journal of Intelligent Manufacturing. - : Springer Science and Business Media LLC. - 0956-5515 .- 1572-8145. ; 25:5, s. 1089-1099
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Journal article (peer-reviewed)abstract
- To fulfill increasing requirements in the manufacturing sector, companies are faced to several challenges. Three major challenges have been identified regarding time-to-market, vertical feedback loops and level of automation. Grafchart, a graphical language aimed for supervisory control applications, can be used from the process-planning phase, through the implementation phase and all the way to the phase for execution of the process control logics, on the lower levels of the automation triangle. This work in progress is examining if the same concepts could be used on the higher levels of the automation triangle as well. By splitting the execution engine and the visualization engine of Grafchart various different visualization tools could potentially be used, however connected by the shared Grafchart semantics. Traditional Business languages (e.g. BPMN) could therefore continue to be used for the process-planning phase whereas traditional production languages (e.g. Grafchart or other SFC-like languages) could be used for the execution. Since they are connected through the semantics, advantages regarding the three identified challenges could be achieved; time-to-market could be reduced, the time delays in the vertical feedback loops could be reduced by allowing Key Performance Indicator visualization, and the level of automation could be increased.
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| 6. |
- Hassan, Muhammad, et al.
(author)
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Experience from implementing digital twins for maintenance in industrial processes
- 2024
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In: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145. ; 35, s. 875-884
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Journal article (peer-reviewed)abstract
- The capability of estimating future maintenance needs in advance and in a timely manner is a prerequisite for reliable manufacturing with high availability in a production unit. Additionally, conducting planned maintenance efforts regularly and prematurely increases the service lifetimes and utilization rates of parts, which leads to more sustainable production. The benefits of predictive maintenance are obvious, but introducing it into a facility poses various challenges. In this study, digital twins of well-functioning machines are used for predictive maintenance. The discrepancies between each physical unit and its digital twin are used to detect the maintenance needs. A thorough evaluation of the method over a period of 18 months by comparing digital twin detection results with maintenance and control system logs shows promising results. The method is successful in detecting discrepancies, and the paper describes the techniques that are used. However, not all discrepancies are related to the maintenance needs, and the evaluation identifies and discusses the most common sources of error. These are often the results of human interaction, such as parameter changes, maintenance activities and component replacement.
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| 7. |
- Heitz, Thomas, et al.
(author)
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Investigation on eXtreme Gradient Boosting for cutting force prediction in milling
- 2023
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In: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145.
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Journal article (peer-reviewed)abstract
- Accurate prediction of cutting forces is critical in milling operations, with implications for cost reduction and improved manufacturing efficiency. While traditional mechanistic models provide high accuracy, their reliance on extensive milling data for force coefficient fitting poses challenges. The eXtreme Gradient Boosting algorithm offers a potential solution with reduced data requirements, yet the optimal utilization of eXtreme Gradient Boosting remains unexplored. This study investigates its effectiveness in predicting cutting forces during down-milling of Al2024. A novel framework is proposed optimizing its precision, efficiency, and user-friendliness. The model training incorporates the mechanistic force model in both time and frequency domains as new features. Through rigorous experimentation, various aspects of the eXtreme Gradient Boosting configuration are explored, including identifying the optimal number of periods for the training dataset, determining the best normalization and scaling technique, and assessing the hyperparameters’ impact on model performance in terms of accuracy and computational time. The results show the remarkable effectiveness of the eXtreme Gradient Boosting model with an average normalized root mean square error of 14.7%, surpassing the 21.9% obtained by the mechanistic force model. Additionally, the machine learning model could capture the runout effect. These findings enable optimized milling operations regarding cost, accuracy and computation time.
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| 8. |
- Iunusova, Eleonora, 1993-, et al.
(author)
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Early fault diagnosis in rolling element bearings : comparative analysis of a knowledge-based and a data-driven approach
- 2023
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In: Journal of Intelligent Manufacturing. - : Springer Nature. - 0956-5515 .- 1572-8145. ; , s. 1-21
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Journal article (peer-reviewed)abstract
- The early identification of a defect that is developing in a bearing is crucial for avoiding failures in rotating machinery. Frequency domain analysis of the vibration signals has been shown to contribute to a better understanding of the nature of a developing defect. Early signs of degradation might be more noticeable in certain frequency bands. The advantages in identifying and monitoring these bandwidths are several: prevention of serious machinery damages, reduction of the loss of investments, and improvement of the accuracy in failure predicting models. This paper presents and compares two approaches for the diagnosis of bearing faults. The first approach was knowledge-based. It relied on principles of mechanics to interpret the measured vibration signals and utilized prior knowledge of the bearing characteristics and testing parameters. The second approach was data-driven whereby data were acquired exclusively from the vibration signal. Both approaches were successfully applied for fault diagnosis by identifying the frequencies of the vibration spectra characteristic for the bearing under study. From this, bandwidths of interest for early fault detection could be determined. The diagnostic abilities of both approaches were studied to analyze and compare their individual strengths regarding the aspects of implementation time, domain knowledge, data processing associated knowledge, data requirements, diagnostic performance, and practical applicability. The advantages, apparent limitations as well as avenues for further improvement of both approaches are discussed.
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| 9. |
- Ji, Wei, et al.
(author)
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A big data analytics based machining optimisation approach
- 2019
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In: Journal of Intelligent Manufacturing. - : SPRINGER. - 0956-5515 .- 1572-8145. ; 30:3, s. 1483-1495
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Journal article (peer-reviewed)abstract
- Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.
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| 10. |
- Karlsson, B, et al.
(author)
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A dynamic safety system based on sensor fusion
- 2000
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In: Journal of Intelligent Manufacturing. - 0956-5515 .- 1572-8145. ; 11:5, s. 475-483
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Journal article (peer-reviewed)abstract
- Machines in industry, including industrial robots, have in many cases dramatically reduced the man-made work and improved the work environment. New machines introduce, however, new risk factors. Traditionally machines are safeguarded by means that more or less rigidly separates the machines from the personnel. This works well in many traditional areas, i.e., where industrial robots are involved. There is however a risk that the safety system limits the valuable flexibility of the robot, which can be considered as a quality that tends to become even more valuable in the progress of programming possibilities and sensor technology. This article shows an example how a safety system can be designed to achieve increased flexibility in co-operation between human and production safety strategy. The proposed safety system is totally based on sensor information that monitors the working area, calculate the safety level and improve the system dynamically, e.g., reduce the robot capability in conjunction to the system safety level. The safety system gain information from the sensors and calculates a risk level which controls the robot speed, i.e., the speed is reduced to achieve a sufficiently low risk level. The sensor data is combined with fuzzy-based sensor fusion and fuzzy rules. The safety system is based on sensor information, hence it automatically adjusts to changes in the guarded area as long as the functionality of the sensors is maintained. Finally, we present a system implementation in an industrial robot application.
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