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Cone pyrolysis liquefaction reactor is the key core device of biomass energy production equipment. The study of the dynamics of the cone is of great significance to the bioenergy conversion technology. Proper control of the movement time of the heat carrier in the cone can prevent it. Biomass superheated carbonisation can increase the ratio of gas and liquid formation. It is a difficult and key technology in the design of the cone pyrolysis liquefaction reactor. The kinetic differential equation of the relative motion of heat carrier particles can be obtained by kinetic analysis and calculation. It can play a key role in the in-depth study of the structural size design of the cone and the optimisation of the structural size parameters. This study conducts a dynamic simulation of the physical simplification model of biomass energy production equipment. The computer simulation software was used to simulate the running process of the simplified cone mechanism model. A reasonable physical prototype that meets the process parameters is designed. In view of the above complex dynamic phenomena, it is necessary to comprehensively analyse various methods of dynamics, and use appropriate numerical analysis and computer-aided design methods to carry out system equipment research and design.

Tool failure is one of the main causes of failure in CNC machine tool machining. The real-time online monitoring technology of CNC machine tool is studied, which is helpful to improve the efficiency of failure-free operation of CNC machine tool and reduce the probability of scrap and equipment failure due to tool failure. Here, the correlation between inverter input current and tool wear condition was studied first, based on which a new method about how to calculate line current on inverter input side was defined, according to definition of current virtual value. Then, a real-time monitoring system for online tool wear is designed and developed. The experimental results show that the system can reflect the tool wear condition and remind to change tool timely.

The new Industry 4.0 envisions a future for agile and effective integration of the physical operational technologies (OT) and the cyber information technologies (IT) as well as autonomous cooperation among them. However, the wide variety and heterogeneity of industrial systems and field devices -especially on the factory floor - increase integration complexity. To address these challenges, new technologies and concepts such as the Industrial Internet of Things (IIoT), Service-oriented Architecture (SoA), Semantic Technologies, Machine Learning and Artificial Intelligence are being introduced to the industrial environment. In this paper, we focus on how industrial automation systems and field devices can be integrated into the IIoT framework and coordinated to adapt to dynamic operating environment. Specifically, this paper proposed an interoperability solution that makes use of SoA and Semantic Technologies to achieve supervised coordination of IIoT application systems. To illustrate the potential of this approach, the Service-oriented Architecture-based Arrowhead Framework is used as the fundamental framework for the implementation of the approach.

In order to solve the problems of low production efficiency, poor quality of blanks, high labour intensity and inability to meet the market demand with the manual gypsum mould, a novel design of the gypsum mould to be applied into the automatic production of the planks was developed for ceramic handicrafts. This mould inherits the advantages of the manual mould with tenon–mortise connection and adopts six-piece combination. According to the spherical radial movement, the new mould opens and closes, mostly avoiding movement interference and significantly improving service life. The connection technique of ‘six-point positioning principle’ was proposed and adopted to embed the connection device of four side die pieces with manipulator, so as to realise the strength of gypsum die pieces and effectively connect with the manipulator. Based on the group technique, the standard eigenvector and norm for the grouping of geometric elements of target blanks were proposed to realise the grouping of many different kinds of blanks into a few number of groups with all the blanks. Within a group, the gypsum mould keeps the external geometric features and tenon–mortise connection unchanged, while the working cavity of different blanks only alters, for the flexibility of gypsum mould. Thus, the universality of the mould is gained, and the design and manufacturing efficiency is greatly improved, with the considerable reduction of the cost, and the digitisation of design, manufacturing and management processes is realised. The gypsum mould provides a new technological method and technical guarantee for changing the labour-intensive production mode of ceramic handicraft blank cast and forming, realising the intellectualisation and automation of the main production link of ceramic handicraft blank cast and efficiency automatic and intellectualised production of ceramic handicraft blank with high identity.

Wool and mohair fibres are both animal-based fibres and having circular scales on their microscopic images from the longitudinal view. Although they look very similar in their microscopic view, they show different physical/chemical properties which determine their usage area. Thus, in textile industry, they need to be separated carefully from each other. The separation of wool/mohair fibres is an important issue and can be performed with human eye by using the microscopic images, that is not time/cost effective and not objective. The novelty of the presented study is to design an objective, easy, rapid, time and cost-effective method in order to separate wool fibre from mohair fibre by using a texture analysis based identification method. For this purpose, microscopic images of both wool and mohair fibres were preprocessed as the texture images. Local binary pattern-based feature extraction process and deep learning were separately used to get determinative information from the fibres. In order to identify the samples, the classification based method was completed. Experimental results indicated that an accurate texture analysis for this kind of animal fibres is possible to identify wool and mohair fibres by using deep learning and machine learning with 99.8% and 90.25% accuracy rates, respectively.

Automated Guided Vehicles (AGVs) are increasingly being employed in logistics to carry out tiring and repetitive tasks inside large industrial buildings. In these settings, AGVs perform more efficiently than human workers in that they exhibit no signs of fatigue, their movements can be tracked automatically and their path can be timed to feed/collect objects in a quasi-predictable time frame and to even perform some object manipulations/refinement during transportation. In this article, we present a simulation analysis assessing the feasibility of commodity WiFi for operations control in an autonomous production site. In this context, AGVs and infrastructure endpoints are involved in periodic data exchange, the later gathering and acting upon AGV health data in order to ensure the systems operations continuity. To this end, we have setup a realistic simulation scenario mimicking the sites’ operational environment discussing some trade-offs that emerge.

This paper describes the developed system for testing and evaluating the thermal comfort of smart textiles clothing. The system is based on a hardware unit which is able to send data to the developed application for an Android smartphone over the low energy Bluetooth. The whole system was verified by comparative measurements and thermal camera scanning.

This paper presents research focused on the development of an e-textile piano made by several textile technologies. The textile piano was designed to combine different ways of contacting, such as embroidery, soldering, gluing, thermo-transfer. A blue cotton fabric was used as a substrate for textile piano. Three different samples of the textile piano were made and tested. Arduino LilyPad USB Plus and Arduino LilyPad SimpleSnap were used as a control unit for designed pianos. The advantages, disadvantages of different technologies and examples are shown and discussed in the paper.

It is necessary to test the reliability of the operation of the machine itself for meeting the high-speed, ultra-precision, flexible, and other modern manufacturing and processing needs. In practical applications, the obtained reliability detection information presents a variety of uncertainties such as randomness, imprecision, incompleteness and so on, due to the complexity of the machining environment, the limitations of the sensor itself and the imperfections of the information acquisition technology. The uncertainty reasoning method is proposed to test reliability and accuracy precision. Based on the multi-sensor information characteristic, the rule of the credibility sets is established to achieve the operational reliability analysis. It can realise the uncertainty reasoning for the modal and reliability rules. Examples of analysis show that during the high-end CNC machine tools operational reliability analysis, it has a practical effect to study the uncertainty reasoning method of the modal and reliability propositional rule and the analysis basis can be provided for the precision degradation and fault diagnosis.

CNC machine tools play important role in industries. The dynamic characteristics of the machine tools are the main factor which affected the status of a machine tools. A three-dimensional model of a five-axis CNC machine tool was built by using PRO/E. First, the model was imported into ANSYS software in order to analyse the dynamic characteristics of the machine tool; the first six natural frequencies and vibration modes of the machine tool were obtained through modal analysis and harmonic response analysis. The accuracy of the finite element analysis model was verified through the experimental results. Finally, the optimisation suggestions of the machine tool structure were proposed.

With the speeding up of the industrialisation process, lightweight products are more and more favoured by people, so the engineering plastic is born at the right moment. Engineering plastics are often used for structural materials in engineering. They have high mechanical strength, such as high temperature resistance, and are wear-resisting. With the development of engineering plastics, their advantages have been gradually accepted by people. In recent years, statistics shows that about 35% of the engineering plastics are used to produce auto spare parts. Using plastic products instead of tempered products is a trend of energy saving and environmental protection. However, there are some security risks such as the stability of the plastic riveting process. The quality of the product can be quickly detected by the artificial but some tiny product cracks are hard to detect. In order to make use of the plastic rivet products better, a product quality inspection system combining computer vision with infrared image processing technology is designed in the background of the hot riveting process of some automobile products. Firstly, this paper uses the infrared detector for image acquisition, then the digital images are obtained through the analogue digital conversion circuit.

The concept of cyber-physical systems (CPSs) is a core technology indispensable to Industry 4.0. The development of an intelligent automatic operational management platform is presented in this study. The platform is used to collect on-site data, provide predictive maintenance, and monitor and control the mobile robots. For predictive maintenance, it is targeted at products from manufacturing big data to cloud computing, and predictive maintenance for all factories around the world. The efficiency of the management system is improved by the performance evaluation strategy. In the experiments, the authors implement and discuss the predictive maintenance platform with generated synthetic data and real-world data collected from a gearbox plant. The results show that the important variables possess large drops according to mean decrease Gini. A warning generation for battery simulation and manufacturing execution is also presented to demonstrate the proposed system design. The results show that the discharge curve drops sharply at the last stage, and the warning value can be set accordingly.

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.

Cyber-physical production systems are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. In order to make these systems interoperable with each other for addressing Industry 4.0 applications a number of service-oriented architecture frameworks are developed. Such frameworks are composed by a number of services, which are inherently dynamic by nature and thus imply the need for self-adaptation. In this paper we propose generic autonomic management as a service and show how it can be integrated in the Arrowhead framework. We propose generic and reusable interfaces for each phase of the autonomic control loop in order to increase the usability of the service for other frameworks and application systems, while reducing the software engineering effort. To show the utility of our approach in the Arrowhead framework we use a climate control application as a representative example.

‘S’ specimen through its unique profile features makes it possible to fully mobilise the five-axis linkage function in the five-axis finishing stage. The continuous rotation direction of the rotating axis not only reflects the static error of the machine tool but also the servo system follow error, tool positioning error, profile error etc. The authors studied its influence on the accuracy of each axis of five-axis CNC machine tool by analysing the geometrical characteristics of a S specimen and using UG's CAM environment to process ‘S’ specimen. Finally, the corresponding program forming system for S specimen machining experiments is designed by LabVIEW, and the authors used macro instruction to do NC program trigger system to collect vibration signals segmentally, so that the processing and acquisition system can be synchronised. This provides a more complete basis for the test piece cutting method to evaluate performance of machine tools.

Machine learning techniques have been widely applied to production processes with the aim of improving product quality, supporting decision-making, or implementing process diagnostics. These techniques proved particularly useful in manufacturing industry where huge variety of heterogeneous data, related to different production processes, can be gathered and recorded but where traditional models fail due to the complexity of the production process. In this study, we describe a novel Machine Learning methodology to associate some product attributes (either defects or desirable qualities) to process parameters. Namely we combine Support Vector Machine (SVM) and the Support Vector Representation Machine (SVRM) to perform instance ranking. The combination of SVM and SVRM guarantees a high flexibility in modeling the decision surfaces (thanks to the kernels) while limiting overfitting (thanks to the principle of margin maximization). Thus, this method is well suited for modeling unknown, possibly complex relationships, that may not be captured by simple handcrafted models. We apply our method to production data of an investment casting industry placed in South Italy. We obtain an instance ranking that may be used to infer proper values of process parameter set-points.

The separating and screening of cylindrical parts are always difficult in the assembly industry, and the previous devices have the weakness of low working efficiency, low accuracy, and low automaticity. Based on the theoretical analysis, modelling and simulation, a theory of simultaneous separation and equidistant screening is proposed, and the corresponding equipment is established with the core modules of the multi-grooved cylindrical cam mechanism, pneumatic system, sensing, and controlling system. Solidworks software is used for three-dimensional modelling and kinematic simulation, which verify the correctness of the equipment design.

Made available in DSpace on 2019-10-04T12:35:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) New alternatives for monitoring the ceramic grinding process have been studied. Monitoring vibration signals is one of the most successful methods because some characteristics that describe the behaviour and influence of the process on ground parts are only noticeable by studying such signals. This study aims to monitor the finishing of advanced ceramics during the surface grinding process via digital processing of the vibration signals. Experimental tests were performed using a surface tangential grinding machine with a diamond grinding wheel and alumina (Al2O3) test specimens. The vibration signal was measured by an accelerometer and recorded by an oscilloscope at a 2 MHz sampling rate. The tests were conducted at different depths of cut for two workpiece speeds (v(w)) under mild and severe machining conditions. Confocal microscopy and surface roughness R-a measurements were performed after grinding each workpiece to classify the samples. Digital signal processing was performed to achieve feature extraction. A frequency range of 800 Hz-2 kHz was most strongly related to the post-grinding ceramic condition. A correlation was found between vibration and integrity of the ceramic workpiece because the vibration signal was proportional to the surface roughness for each cutting depth used. To support the conclusion presented, a statistical analysis through variance by analysis of variance was performed. Sao Paulo State Univ, Dept Elect Engn, Av Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil Sao Paulo State Univ, Dept Mech Engn, Av Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil Sao Paulo State Univ, Dept Elect Engn, Av Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil Sao Paulo State Univ, Dept Mech Engn, Av Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil CNPq: 306677/2013-0

Industry 4.0 is a promising vision for advancing the manufacturing sector through the recent innovations in information and Communication Technologies that enable collecting, storing, and processing detailed and accurate data about industry processes. This data enables manufacturers for data-driven decision making to significantly improve their operations and profitability. Most of the large manufacturing enterprises can benefit from this as they can collect more data that can be utilised to enhance their decision-making processes. Small and medium enterprises (SMEs) have limited data and resources, thus reducing the possible gains. However, if SMEs and small manufacturing facilities collaborate and share data, which is then jointly analysed, feasibility and quality of their data analytics and decision-making processes could be significantly enhanced. This study discusses collaborative data analytics (CDAs) in Industry 4.0, summarising findings into a novel CDA framework that can be used by manufacturing enterprises of any size and scale to enable and enhance the mutual benefits of CDAs and decision-making processes. The CDA framework can enhance the key factors and performance metrics of manufacturing facilities such as reliability, availability, and efficiency. The study also provides a preliminary benefit analysis of utilising the proposed CDA framework for manufacturing SMEs.

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined.

Made available in DSpace on 2018-11-26T17:39:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-08-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter counts' was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation. Sao Paulo State Univ, Dept Elect Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil Sao Paulo State Univ, Dept Mech Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil Sao Paulo State Univ, Dept Elect Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil Sao Paulo State Univ, Dept Mech Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil FAPESP: 2016/02831-5 CNPq: 306677/2013-0

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.

There is an ongoing shift in industries from mass production to low-batch-production with highly individualized goods. This increases the programming effort required for the producing machines and robots, which is currently carried out by robot experts. For keeping the production economical, new programming approaches are required, allowing shop-floor workers to instruct robots. One approach is to develop robotic skills, which are pre-programmed software modules that only need to be parametrized by the shop-floor user. In this paper, a new software architecture for robot skills is presented, which aims at robustness and human-robot interaction. In addition, four basic demands on the skill parametrization are described that fasten up the process and increase intuitiveness for the user. We give several examples and implement a screwing skill and a pick & place skill, which are demonstrated in two case studies.

Mass-customization of wearable products are offered as a higher added value to the broad public and had to compete with ready-to-wear offer. However, people with specific requirements are not covered with the current mass-customized products. This is the case of the elderly, disabled, diabetic and obese population groups when wearing textiles, clothing, footwear and textile-based orthotic goods. However, at present, available knowledge and flexibility of production equipment and machinery of SMEs operating in these traditional industries (even those that already offer made-to-measure products to the mass public) is unable to respond to the individual needs among such heterogeneous groups. The FASHION-ABLE project has solved this problem with: a user framework defining and quantifying the relevant user attributes, New Collaborative Product Customization Services, New stretch-leather's manufacturing processes and equipment, New 3D-spacer fabric's flexible manufacturing process and machinery, New textile's finishing operations and equipment, An extended manufacturing order management structure and tools, Life- Cycle Analysis instruments, and Interoperable Product Data Management tools.

International audience; This paper presents an industrial application called "RVPI" [1] for training operators in a tyre manufacturing plant. This training is performed through an Informed Virtual Environment integrating different aspects of human activity and expertise. To create the system, knowledge extraction and a co-design of the system have been realized in collaboration with the German tyre manufacturer Continental. The system is deployed in real plants in five different countries and is now becoming a regular training tool. Results show that production costs are reduced with RVPI due to a shorter training period and a better understanding of the process.

International audience; Mechatronics is the integration of different sciences and techniques of mechanical engineering, automatic control, electronics, and informatics. The rapid evolution of the market competitors requires the reduction of development time of a product while increasing the quality and performance. It is, therefore, necessary to increase the efficiency of the design process. To meet this need, simulation and, especially, virtual prototyping have become a key technology. It is difficult to find simulation tools are able to analyze multidependent systems of different areas. However, an environment that allows a simulation integrating multidisciplinary mechatronic systems is necessary. This paper describes a method of design and simulation of mechatronic systems. First, we identify the behavior model and its associated 3D geometric model. The behavior model is seen as a dynamic hybrid system of two coupled hybrid automata (operative part and control part). Then, we present OpenMASK and OpenModelica simulators, the IEEE1516 standard HLA and work related to this distributed architecture for simulation. In a top-down approach, we present our method and experiments to integrate HLA functionalities in these simulators and to distribute the modeling elements of mechatronic systems. Also, we propose extensions to integrate real-time for interactive simulations. Finally, we apply our approach on a representative example of a mechatronic system.

International audience; Reverse engineering (RE) is a domain of current interest where physical models are measured or digitized in order to obtain a virtual model. Currently, these virtual models are rebuilt using approaches that consider only a geometric point of view. These models are generally frozen (i.e., poorly parameterized and not easy to modify). These kinds of models are good for many applications but people need more for redesign operations. This paper proposes a knowledge-based approach for reverse engineering that enables a RE user to rebuild nonfrozen models that are close to an original CAD model.

The manufacture of open cell metal foams by dissolution and sintering process (DSP) is the matter of the present work. Aluminum foams were produced by mixing together carbamide particles with different mesh sizes (i.e., space-holder) and very fine aluminum powders. Attention was first paid at understanding the leading phenomena of the different stages the manufacturing process gets through: Compaction of the main constituents, space-holder dissolution, and aluminum powders sintering. Then, experimental tests were performed to analyze the influence of several process parameters, namely, carbamide grain size, carbamide wt %, compaction pressure, and compaction speed on the overall mechanical performance of the aluminum foams. Meaningfulness of each operational parameter was assessed by analysis of variance. Metal foams were found to be particularly sensitive to changes in compaction pressure, exhibiting their best performances for values not higher than 400 MPa. Neural network solutions were used to model the DSP. Radial basis function (RBF) neural network trained with back propagation algorithm was found to be the fittest model. Genetic algorithm (GA) was developed to improve the capability of the RBF network in modeling the available experimental data, leading to very low overall errors. Accordingly, RBF network with GA forms the basis for the development of an accurate and versatile prediction model of the DSP, hence becoming a useful support tool for the purposes of process automation and control. Copyright © 2009 by ASME.

In this work, an advanced anisotropic elastic-plasticity model is combined with a damage model and a strain localization criterion in the aim to describe accurately the mechanical behavior of sheet metals. Large strain, fully three-dimensional, implicit time integration algorithms are developed for this model and implemented in the finite element code Abaqus. The resulting code is used to predict the strain localization limits as well as the springback after forming of sheet steels. The impact of strain-path dependent hardening models on the limit strains and on the amount of springback is addressed.; International audience; In this work, an advanced anisotropic elastic-plasticity model is combined with a damage model and a strain localization criterion in the aim to describe accurately the mechanical behavior of sheet metals. Large strain, fully three-dimensional, implicit time integration algorithms are developed for this model and implemented in the finite element code Abaqus. The resulting code is used to predict the strain localization limits as well as the springback after forming of sheet steels. The impact of strain-path dependent hardening models on the limit strains and on the amount of springback is addressed.

The aim of this work is to design and to improve the geometry of a porthole‐die for the production of aluminum components by means of 3D FEM simulations. In fact, the use of finite element models will allow to investigate the effects of the die geometry (webs, extrusion cavity) on the material flow and on the stresses acting on the die so to reduce the die wear and to improve the tool life. The software used to perform the simulations was a commercial FEM code, Deform 3D. The technological data introduced in the FE model have been furnished by METRA S.p.A. Company, partner in this research. The results obtained have been considered valid and helpful by the Company for building a new optimized extrusion porthole‐die.

In order to face the more and more frequent market changes, reliable and flexible factory automation systems have to be developed or reconfigured with reduced times and costs. Therefore, innovative development methodologies for the structured design and testing of industrial supervision and control systems are mandatory. In the present work a development methodology is presented, which uses the formal reference model proposed by the IEC 61499 international standard to design the modules of a factory automation system. Moreover, a closed-loop simulation based testing method is presented to verify the different hierarchic levels of the designed automation system in a modular way, following a bottom-up approach. Such a technique is implemented in the Matlab environment, using its toolboxes Simulink and Stateflow. Through the adoption of formal models that exploit the concepts of modularity, encapsulation and abstraction, the description of complex systems is simplified and model reuse is enhanced. Furthermore, by means of a simulation based testing method correctness is verified before final implementation and reliability is improved. In the paper an application example is also presented, where the design and testing of the automation system of a manufacturing cell for a furniture factory by means of the proposed framework is illustrated.