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Close-Range Photogrammetry is a widespread and efficient technique in the 3D acquisition of artefacts, particularly in fields like Cultural Heritage. Despite this wide usage, also due to a convenient quality/cost ratio, it shows some limitations due to light conditions as well as the artefact surface finishing. In this paper, we would like to report the assessment of a photogrammetry approach to 3D capture metal reflective surfaces, such as bronze, which is a widely used material in ancient statues. To this aim, we propose a photogrammetry workflow based on systematic steps capable of overcome some of the main issues of reflective surfaces. To validate this approach, the developed 3D model is compared to a more accurate model of the same artefact, obtained with a 3D scanner. As a case study, we selected the Principe Ellenistico, an ancient bronze statue conserved in the Museo Nazionale Romano (Rome, Italy), of which a photogrammetric model is firstly developed and then compared to the scanned one.

The systematic integration of user needs in the product design is a key issue in industry, especially for small- and medium-sized enterprises (SMEs), which suffer a lack of engineering methods and resources. Moreover, most of the approaches described in the literature are not flexible enough to be tailored on the SMEs’ needs, involve users only in the early design phases and are not fully accessible due to the high cost in their implementation. The present paper proposes a user-centred design methodological framework specifically focused on SMEs, which supports the designer from both design and manufacturing aspects along the engineering product design process. The framework integrates engineering methods with Web-based software tools, which enable the communication and the concurrent work of the design team, and supports direct participation of users. Beside state-of-the-art methods, the framework allows the integration of specifically tailored techniques. The framework is successfully validated through an industrial case study developed in collaboration with an Italian SME. As a result, the design of an injection-moulded housing and the related interfaces of a biomedical electronic device are achieved with a reduction of uncertainty and development time, by involving users throughout the design phases and suggesting methods and tools on the basis of the designers’ know-how and SME’s specific resources.

An engineering design process consists of a sequence of creative, innovative and routine design tasks. Routine tasks address well-known procedures and add limited value to the technical improvement of a product, even if they may require a lot of work. In order to focus designers work on added value tasks, the present work aims at supporting a routine task with a Design Archetype (DA). A DA captures, stores and reuses the design knowledge with a tool embedded in a CAD software. The DA algorithms drive the designer in selecting the most effective design concept to deliver the project requirements and then embody the concept through configuring a CAD model. Finally, a case study on the definition of a DA tool for gear design demonstrates the effectiveness of the DA tool.

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.

Reconfigurable manufacturing systems (RMS) are considered the future of manufacturing, being able to overcome both dedicated (DMS) and flexible manufacturing systems (FMS). In fact, they provide significant cost and time reductions in the launch of new products, and in the integration of new manufacturing processes into existing systems. The goals of RMS design are the extension of the production variety, the adaption to rapid changes in the market demand, and the minimization of the investment costs. Despite the interest of many authors, the debate on RMS is still open due to the lack of practical applications. This work is a review of the state-of-the-art on the design of cellular RMS, compared to DMS, by means of optimization. The problem addressed belongs to the NP-Hard family of combinatorial problem. The focus is on non-exact meta-heuristic and artificial intelligence methods, since these have been proven to be effective and robust in solving complex manufacturing design problems. A wide investigation on the most recurrent techniques in DMS and RMS literature is performed at first. A critical analysis over these techniques is given in the end.

Fixture systems have a great importance in modern manufacturing and assembly because of the high number of scenarios in which they are used. Fixture design is a complex task since the system effectiveness depends both on position and type of locators. Several authors deal with the problem of determine the most suitable design for fixture systems but their investigation is commonly limited to the evaluation of the effects due to the locators position. In the present work a design method is proposed to evaluate the fixture systems considering also the locators type. Since it is possible to model the fixtures as multi-performance systems, the comparison is performed by introducing appropriate sensitivity indexes. The effectiveness of the design method is proved through the application to an automotive case study.

This paper reports about the design and modeling process of high performance servo-actuated mechanisms for automatic machines. Besides being a delicate and time consuming process, coupled simulations based on virtual prototyping finally offer the chance to integrate engineering methods proper of control system engineering and mechanical design. In particular, the main target of this work is to investigate how different virtual prototyping approaches, each having increasing level of detail, can contribute to the appropriate prediction of the expected machine performance. These results are then compared with experimental data obtained on a real servomechanism prototype. The comparison quantitatively demonstrate the improvement on torque prediction and position error reduction when detailed models of the controller and the electric motor dynamics are coupled with the mechanical system model.

The tolerances of welded chassis are usually defined and adjusted in very expensive trials and errors on the shop floor. Computer Aided Tolerancing (CAT) tools are capable to optimize the tolerances of given product and process. However, the optimization is limited since the manufacturing process is already mostly defined by the early choices of product design. Therefore, we propose an integrated design method that considers the assembly operations before the detail design of the chassis and the concept design of the fixture system. The method consists in four phases, namely functional analysis in the CAD environment, assembly sequence modelling in the CAT tool, Design Of Simulation Experiment on the stack of the tolerance ranges and finally optimization of the tolerances. A case study on a car chassis demonstrates the effectiveness of the method. The method enables to selectively assign tight tolerances only on the main contributors in the stack, while generally requiring cheaper assembly operations. Moreover, a virtual fixture system is the input for the assembly equipment design as on optimized set of specifications, thus potentially reducing the number of trials and errors on the shop floor.

We present an efficient and user-friendly parametric CAD-based design method for the graphical description of positive displacement machines, exploiting commercial parametric CAD software and a tailored interface. An executable module simulates the motion of the machine components, analyzes the machine geometry, and automatically extrapolates the geometrical data from the 3D CAD model, generating data files that can be directly used for following fluid dynamic analyses. The graphical approach supports investigation of the machine performance and exploring optimized design variants. The method has been applied to three industrial test cases: An external gear pump, an axial piston pump, and a gerotor pump. A complete case study focused on the external gear pump is proposed, as well as the results from the other two types are summarized. We validate the CAD-based method by comparing the obtained data with the data coming from the application of state-of-the-art analytical methods.

Industrial robotics provides high flexibility and reconfigurability supported by a user-friendly programming, but still lacks in accuracy. An effective workcell calibration reduces errors in robot manufacturing and enables robot machining applications. A novel workcell calibration method is embedded in an integrated design framework for an in-depth exploitation of CAD-based simulations and offline programming. The method is composed of two steps: first calibration of the workpiece-independent equipment in the workcell layout and final automated online calibration of workpiece-dependent equipment. The method is finally applied to a changeable robotic workcell for finishing aluminium cast housings for aerospace gear transmissions characterised by complex shapes and by close dimensional and geometrical specifications. Experimental results prove the method effectiveness in enhancing accuracy in robot machining.

In recent years, Human Robot Cooperation (HRC) has found an increasing adoption in manufacturing, especially to help humans in the execution of manual assembly tasks. An effective employment of HRC encompasses human relief from exhausting operations. Therefore, the design of cooperative solutions should be developed accordingly to ergonomic aspects. The present work proposes an approach to support the integration of ergonomic evaluation of manual operations in the design of HRC solution, based on modelling and simulation of the human body along the manufacturing tasks. The proposed modified model integrates the ergonomic metrics and returns a fatigue level along the working shift scheduling. A real manual assembly of biomedical products has been selected to validate the proposed approach. As a result, the suggested fatigue model provides an objective ergonomic evaluation of manual operations which verifies the impact of the HRC solution on the production goals.

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches.

Human Robot Collaboration (HRC) have proved to be effective if compared to traditional hybrid automation in assembly tasks, especially when human-like sensitivity and high quality are required. However, a rigorous engineering design is mandatory in order to successfully apply HRC to Industry. Academy and Industry are asked to jointly work for exploiting the technical opportunities given by robots and humans. Scientific literature often describes the application of HRC in manufacturing but rarely presents systematic engineering design approaches. The present paper investigates and describes the systematic design of a HRC workcell for assembling bio-medical products. Moreover, productivity and profitability of the developed solution are evaluated and discussed.

Recent trends in industrial manufacturing impose the adoption of changeable systems, based on reconfigurable and flexible equipment. In this scenario, industrial robotics platforms are central to design highly reconfigurable systems. A Robotic Reconfigurable Machining Platform (RRMP), as defined, is a modular architecture for robotic workcells, designed in order to exploit the flexibility features of robots and extend their field of application to high precision machining. RRMP calibration is a key task, which involves calibration of tools, workpieces and peripherals. However, state-of-the-art calibration methods and tools lead to hardly predictable system downtime, which impacts the reconfiguration phase. A novel method to perform the workpiece calibration is proposed for the reduction of the reconfiguration efforts in RRMPs. The method is addressed through a full integration with a virtual environment for robot simulation and programming. The method is finally applied to an industrial case study and compared to the most widely diffused online approach.

Industrial robots offer a good basis for machining from a conceptual point of view. Still they are rarely utilized for machining applications in industry compared to CNC machines due to their low stiffness and the bad achievable work piece quality. Available compensation approaches, like online compensation approaches to increase position accuracy using costly additional hardware and measurement equipment as well as offline compensation approaches using a set of empirical measurement data and models to predict deviation, try to compensate errors whether to already avoid them if possible. In this paper milling and robot strategies are proposed to increase work piece quality without additional hardware or models. Experimental validations of the results have been performed for different kinds of shapes and materials.

Servo-actuated mechanisms are increasingly substituting fully mechanical drives in order to increase flexibility and reconfigurability of modern automatic machines. The overall servomechanism performance, especially in the case of high-dynamic motions, is the direct consequence of several interacting factors, namely electric motor and linkage dynamics, controller efficacy, and requested motion law. In particular, Point-To-Point (PTP) trajectories are usually designed in order to comply with technological constraints, imposed by the required interaction with the handled product, and to maximize some optimality criterion such as, for instance, energy efficiency or limited actuation torques. In this context, the present paper proposes a novel method for generating either energy-optimal or torque-optimal PTP motions described by piecewise fifth-order polynomials. The optimization cost functions are based on a virtual prototype of the system, which comprises behavioral models of power converter, controller and electric motor coupled with the mechanical system. Results are then compared with experimental data obtained on a physical prototype. The comparison quantitatively shows that better-behaved PTP trajectories can be designed by including the dynamic contribution of each sub-system component.

Actual industrial robotic systems offer performance to effectively cope with the requirements in manufacturing dealing with flexibility and quality. However, their known limits in accuracy do not allow to extend their field of application to high-accuracy machining, actually covered by state-of-the-art CNC machine tools. The European Project COMET has recently proposed an approach to develop a robotic reconfigurable workcell with enhanced accuracy for machining, through the full integration of different theoretical models, technological solutions and manufacturing strategies. The present paper presents and demonstrates the effectiveness of a demo reconfigurable machining workcell for one of its possible configurations, based on CAM off-line programming. In particular, an experimental campaign has been designed and realized in order to discuss the dimensional and geometrical quality obtained for an aluminium automotive part in comparison with quality and costs offered by a standard 5-axis CNC machine tool.

Changeability accomplishes the engineering design of competitive sustainable manufacturing systems, considered as industrial products characterized by inherent life cycle. Main drivers for changeability are manufacturing system reconfigurability and hybridization. A Hybrid Reconfigurable System (H-RS) is characterized by the coexistence and cooperation of industrial robots and skilled human workers to perform complex tasks within a common reconfigurable production environment. H-RSs rise use-productivity along their total system life cycle, fostering the evaluation and implementation of feasible and innovative technologies, and increasing the utilization ratio and the multiple use—or re-use—of resources. The paper proposes an engineering method which aims at enhancing changeability in H-RSs through the application of a multi level reconfigurability approach within a digital environment. The method includes the advanced design and modeling of digital devices which embed mechanics, electronics, control logic and software code. Advanced models are exploited to analyze the system performance in the system domain of changes and to realize an effective human training. An industrial case study describes the application of the method to the design of a hybrid reconfigurable workcell for manufacturing and assembly of top class car chassis.

Engineering changeability-oriented and cost-driven approaches are needed by enterprises to design and optimize manufacturing and assembly systems for the demanding production requirements of the present industrial scenario. The integrated design of Reconfigurable Systems addresses tailored flexibility through modularity, integrability of resources, product and process customization, and system convertibility and diagnosability. The cooperation of robot and humans in hybrid environments offers a good trade-off between changeability, high quality and low costs, by exploiting the human dexterity and cognitive proactivity, together with robotic accuracy and performances. Virtual prototyping methods and digital manufacturing solutions are now mature and effective enough to play a strategic role within the hybrid reconfigurable system (H-RS) design and optimization process. The present research work proposes an engineering method to design and optimize H-RSs, by using virtual prototyping and digital manufacturing as a strategic support for the analysis and synthesis of the technical solutions, especially those related to human-robot cooperation. An industrial case study on a hybrid reconfigurable assembly system of a top class car aluminum chassis is finally presented.

The interest in novel engineering methods and tools for optimizing the energy consumption in robotic systems is currently increasing. In particular, from an industry point of view, it is desirable to develop energy saving strategies applicable also to established manufacturing systems, being liable of small possibilities for adjustments. Within this scenario, an engineering method is reported for reducing the total energy consumption of pick-and-place manipulators for given end-effector trajectory. Firstly, an electromechanical model of parallel/serial manipulators is derived. Then, an energy-optimal trajectory is calculated, by means of time scaling, starting from a pre-scheduled trajectory performed at maximum speed (i.e. compatible with actuators limitations). A simulation case study finally shows the effectiveness of the proposed procedure.

The development of safe, energy efficient mechatronic systems is currently changing standard paradigms in the design and control of industrial manipulators. In particular, most optimization strategies require the improvement or the substitution of different system components. On the other hand, from an industry point of view, it would be desirable to develop energy saving methods applicable also to established manufacturing systems being liable of small possibilities for adjustments. Within this scenario, an engineering method is reported for optimizing the energy consumption of serial manipulators for a given operation. An object-oriented modeling technique, based on bond graph, is used to derive the robot electromechanical dynamics. The system power flow is then highlighted and parameterized as a function of the total execution times. Finally, a case study is reported showing the possibility to reduce the operation energy consumption when allowed by scheduling or manufacturing constraints.

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modelled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system is presented. It shows that there exists a possibility for a significant reduction of the energy consumption, in comparison to state-of-the-art scheduling approaches.