Purpose/Objective: New definitions for some dosimetric parameters for use in quality assurance of flattening filter free (FFF) beams generated by medical linear accelerators have been suggested. The present study aims to validate these suggestions and to propose possible reference levels. Materials and Methods:The main characteristics of FFF photon beams were described in terms of: field size, penumbra, unflatness, slope and peak-position parameters. Data were collected for 6 and 10 MV-FFF beams from three different Varian TrueBeam linacs, and a Varian Clinac iX upgraded to FFF capability for its 6 MV. Measurements were performed with a 2D-array (Starcheck system from PTW-Freiburg), with a linear array (LA48 system from PTW-Freiburg) and with the portal dosimetry method GLAaS utilizing the build-in portal imager of TrueBeam.Results: All the parameters suggested to characterize the FFF beams were measured and evaluated Little variation was observed among the different linacs. Referring to two reference field sizes of 10x10 and 20x20cm 2 , at SDD=100cm and d=d max , from the portal imaging data converted into dose map with the GLAaS method, the following results were obtained, averaged on X and Y profiles. Field size: 9.95±0.02 cm and 19.98±0.03 cm (including allenergies. Penumbra: 2.7±0.3 mm and 2.9±0.3 mm for 6MV-FFF; 3.1±0.2 mmand 3.3±0.3 for 10MV-FFF. Unflatness: 1.11±0.01 and 1.25±0.01 for 6MV-FFF; 1.21±0.01 and1.50±0.01 for 10MV-FFF. Slope: 0.320±0.020 %/mm and 0.43±0.015 %/mm for 6MV-FFF; 0.657±0.023%/mm and 0.795±0.017 %/mm for 10MV-FFF. Peak Position: -0.2±0.2 mm and -0.4±0.2 mm for 6MV-FFF; -0.3±0.2 mm and 0.7±0.3 mmfor 10MV-FFF. Results would depend upon measurement depth. With thresholds set to at least 95% confidence level from the measured data, and to account for possible variations between detectors and methods and experimental settings, a tolerance set of: 1 mm for field size and penumbra, 0.04 for unflatness, 0.1 %/mm for slope and 1 mm for peak position could be proposed from our data. Conclusions:The parameters proposed to characterize the FFF profiles (in particular the unflatness, the slope and the peak position) appear to be a viable solution for routine checks, also presenting strong similarity to the conventional parameters used for flattened beams. The results from three different TrueBeams and a Clinac-iX suggested the robustness of the methods and the possibility to use general tolerances for the parameters. The data suggested also the reproducibility of beam characteristics among different systems (of the same vendor) and could therefore be possibly generalized.Purpose/Objective: A new method for IMRT verification with EBT3 has been developed, avoiding the need of a previous calibration. Performing a single scan gives the possibility to obtain results in less than one hour and avoids environmental and interscan variability. We have developed a method to evaluate measurements of two-dimensional dose distributions following the protocol described by Lewis et al, without the need of a prev...

Sustainability is one of the biggest challenges of this century either for the environment or economical growth. The required cultural shift needs challenging action that will involve deeply software and hardware aspect of manufacturing processes. In this paper, the software part of the matter is addressed by proposing a product centric ontology, in which concepts of product, processes and resources are associated to functions and sustainable manufacturing knowledge. The aim is to design a knowledge-based system that, simulating a sustainable manufacturing expert, is able to automatically identify change opportunities and to propose alternatives on the basis of the existing production scenario.

Despite the recent growing interest in the "factory smartness", still there are only few small and medium enterprises (SMEs) that adopt effective Industry 4.0 (I4.0) solutions. The main reasons can be related to the lack of formalized processes, lack of ICT knowledgeas well as low-cost commercial systems.To cope with these issues, this work focuses on the development and the application of an approach to provide SMEs with a multipurpose , modular, knowledge-based system: the main aim is to provide a modular and extensible system that can be incrementally implemented without requiring huge initial investments. This system is based on a core design-knowledge meta-model. From this core meta-model, multi-purposes modules can be built: in this paper, we present modules for the traceability support, the AR-powered assembly support, themachine-tomachine control and the data analysis support.

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Abstract.To cope with the customer-oriented business model in a global competitive market, enterprises tend to be networked for achieving mass customisation: i.e. offering customisable products with the same efficiency as mass production. This scenario highlights two faces of variability: variability of needs (on customer side) and variability of organisations (on production side). Both types of variability induce a huge number of specified products, namely configurations. This configuration variability must be efficiently managed. This position paper discusses trends and issues for rationalising the number of configurations: i.e. engineering the right number of configurations that match both the customer needs and the production strategy. After this positioning, we propose a systemic perspective for addressing the discussed issues from a sustainability point of view. Finally we give a perspective for a product line definition method that leads to models that meet the discussed variability rationalisation.

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Today, sustainability becomes one of the biggest challenges. It represents a key issue in every production activity. To face this issue, a possible solution is to enhance knowledge usage in manufacturing and sustainability domains. In this paper, we extend the ONTO-PDM ontology for formalizing sustainable manufacturing knowledge. An industrial case is presented for instantiating the extension. Moreover we design a knowledge-based system, which exploits sustainable manufacturing knowledge for supporting design and process planning with sustainability proposals, generating machine code starting from product specifications.

Highly customized products with shorter life cycles characterize the market today: the smart manufacturing paradigm can answer these needs. In this latter production system context, the interaction between production resources (PRs) can be swiftly adapted to meet both the variety of customers' needs and the optimization goals. In the scientific literature, several architectural configurations have been devised so far to this aim, namely: hierarchical, heterarchical or hybrid. Whether the hierarchical and heterarchical architectures provide respectively low reactivity and a reduced vision of the optimization opportunities at production system level, the hybrid architectures can mitigate the limit of both the previous architectures. However, no hybrid architecture can ensure all PRs are aware of how orienting their behavior to achieve the optimization goal of the manufacturing system with a minimal computational effort. In this paper, a new "hybrid architecture" is proposed to meet this goal. At each order entry, this architecture allows the PRs to be dynamically grouped. Each group has a supervisor, i.e. the optimizer, that has the responsibility: 1) to monitor the tasks on all the resources, 2) to compute the optimal manufacturing parameters and 3) to provide the optimization results to the resources of the group. A software prototype was developed to test the new architecture design in a simulated flow-shop and in a simplified job shop production.