Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system

Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers' demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity

Therblig-embedded value stream mapping method for lean energy machining

To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12% , and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Toolpaths Programming in an Intelligent Step-NC Manufacturing Context

The current language for CNC programming is G-code which dates from the beginning of the eighties with the norm ISO 6983. With the new technologies, G-code becomes obsolete. It presents drawbacks that create a rupture in the numerical chain at the manufacturing step. A new standard, STEP-NC, aims to overtake these lacks. A STEP-NC file includes all the information for manufacturing, as geometry description of the entities, workplan, machining strategies, tools, etc. For rough pocket milling, the ISO norms propose different kind of classical strategies as bidirectional, parallel or spiral contour, etc. This paper describes a new way of toolpath programming by the repetition of a pattern all along a guide curve. It presents several advantages as building fastness and easiness. The integration of pattern strategies in STEP-NC standard is an other step for the development of these strategies but also for the enrichment of STEP-NC possibilities. A complete STEP-NC numerical chain was built, integrating these pattern strategies. The implementation of this approach of building pattern strategies was made by the development of tools for the complete manufacturing cycle, from the CAD file to the machined part. Several application cases were experimented on machine tool to validate this approach and the efficiency of the developped tools

Influence of the ratio on the mechanical properties of epoxy resin composite with diapers waste as fillers for partition panel application

Materials play significant role in the domestic economy and defense with the fast growth of science and technology field. New materials are the core of fresh technologies and the three pillars of modern science and technology are materials science, power technology and data science. The prior properties of the partition panel by using recycled diapers waste depend on the origin of waste deposits and its chemical constituents. This study presents the influence of the ratio on the mechanical properties of polymer in diapers waste reinforced with binder matrix for partition panel application. The aim of this study was to investigate the influence of different ratio of diapers waste polymer reinforced epoxy-matrix with regards to mechanical properties and morphology analysis. The polymer includes polypropylene, polystyrene, polyethylene and superabsorbent polymer (SAP) were used as reinforcing material. The tensile and bending resistance for ratio of 0.4 diapers waste polymers indicated the optimum ratio for fabricating the partition panel. Samples with 0.4 ratios of diapers waste polymers have highest stiffness of elasticity reading with 76.06 MPa. A correlation between the micro structural analysis using scanning electron microscope (SEM) and the mechanical properties of the material has been discussed

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Micro-manufacturing : research, technology outcomes and development issues

Besides continuing effort in developing MEMS-based manufacturing techniques, latest effort in Micro-manufacturing is also in Non-MEMS-based manufacturing. Research and technological development (RTD) in this field is encouraged by the increased demand on micro-components as well as promised development in the scaling down of the traditional macro-manufacturing processes for micro-length-scale manufacturing. This paper highlights some EU funded research activities in micro/nano-manufacturing, and gives examples of the latest development in micro-manufacturing methods/techniques, process chains, hybrid-processes, manufacturing equipment and supporting technologies/device, etc., which is followed by a summary of the achievements of the EU MASMICRO project. Finally, concluding remarks are given, which raise several issues concerning further development in micro-manufacturing