Factory planning system considering energy-efficient process under cloud manufacturing

Cloud computing sets to make a change in business between enterprises over the Internet based on services providing dynamically reconfigurable and virtualized networks. It is difficult for an aircraft manufacturer to consider the performance and status of each factory (i.e., capacity, inventory, order, technology, etc.) because of the huge amount of suppliers. In legacy environments, the supplier consumed a long time to identify the machine and process required and the energy to be consumed in order to satisfy a new customer demand. If the process occurred with a new order should be optimized, additional time and cost were needed. Cloud computing can enable the relationship between the manufacturer and suppliers of aircraft, which is representative of mass customized products, to be flexible and efficient. In order to enhance the response time during contracting with suppliers, this paper focuses on an architecture, providing a service for a decision with the concerns about the time and cost as well as energy with regard to process planning. For realizing this service the manufacturer provides the process plan described using neutral language for numerical controllers called 'STEP-NC' which is independent from a specific machine tool. On the supplier side, the machinability check, tool path generation and energy use estimation should be identified with machine tool specifications described by STEP-NC. In the Cloud computing environment, the analysis is performed to check the possibility that a supplier can complete a new order from the manufacturer on the due date with the analysis of machine tools and on-going production schedule. This function is developed in a system which can be installed on a virtual platform and provided by a cloud service. (C) 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Machine Tool Use Phase

Ioan Oliver Avram

This thesis is motivated from the fundamental decision problem in today's manufacturing industry which could be summarized as one simple question: how is it possible to achieve economic growth by taking advantage of the latest technologies while protecting the environment? The machine tool has undergone a remarkable change in recent years and the classical concept of an independent, manually operated machine tool, specially dedicated to a particular machining process no longer belongs to the production environment. The increasing usage of computer control and measuring equipment as well as workpiece and tool handling devices has transformed the machine tool into a sophisticated hybrid production system having a lower demand for human supervision of the process. All these efforts are gathered together in order to achieve a high productivity system able to machine different parts respecting quality requirements at the lowest cost. In addition, increasing attention to the environmental and health impacts by governmental regulations and by growing awareness in society is forcing manufacturers to find environmentally friendly designs and machining strategies. The objective of this research is to propose a multicriteria decision aid methodology (MCDA) for evaluation of the use phase of a machine tool system by jointly considering economical, technical and environmental criteria which allows the decision maker to be in a better position to make sustainable decisions. The evaluation is carried out at two levels by addressing the process level with its local cutting area effects and the main activities of the machine tool system by giving full consideration to reduction/elimination of the cutting fluid and reduction of the overall energy consumption. After carefully reviewing previous research, a consistent hierarchical evaluation criteria structure and an entity relationship diagram characterizing the organizational scheme of a database, which stores the data to be used in the evaluation process, is proposed for each level. As a second step, this thesis proposes a model for estimation of the mechanical energy requirements of the cutting subsystem of a machine tool based on the cutter location data, the cutting parameters and the technical specifications of the spindle and feed axes. The MCDA methodology and the energy consumption model were implemented in a software tool using the VBA (Visual Basic for Applications) language and was validated through various tests consisting in the machining of different milling and drilling features of a prismatic part by employing different cutting parameters and cooling/lubrication alternatives. In addition to the cutting tests, an extensive monitoring of the power share amongst the main subsystems of a modern machining center was carried out and a power data collection framework was proposed. The acquisition, interpretation and storage of the experimental data were supported by a measurement platform developed in the LabVIEW environment.

EPFL2010

Modeling and Simulation of Energy Efficient Milling Process Plans for Prismatic Parts

Nenad Nesic

The milling process has been continuously optimized from many aspects: forces, velocities, stability, quality. Numerous papers have been published that report results in the domain of toolpath optimization with criteria such as raising the quality of the machined part, obtaining a stable machining process and maximizing the material removal rate. However, only recently researchers have started investigating the ecological impact of machining processes. With the constantly increasing prices of electrical energy and all environmental problems caused by the production and waste of the energy, it has become indispensable to look for ways to optimize machining processes from the energy consumption aspect, too. It is in this domain, then, that the work described in this dissertation contributes – to add a new criterion for the sustainable operation of machine tools: reducing the energy consumption during the use phase of the machine tool. The total energy spent in a machining process is the sum of power spent by all machine tool subsystems multiplied by the time that they are working. Therefore, the operating strategies for increasing energy efficiency for the high-speed machining processes must be oriented towards the reduction of the total machining time (productive and unproductive) and reduction of instantaneous consumed power in the machining process or, preferably, both. There are many causes for milling process time and energy inefficiency. The major sources of potential time reductions include: (i) the issues related to the geometry and topology of the toolpath („air-time”, overlapping segments, gouge and uncut regions, orientation of the toolpath), (ii) the issues related to the kinematics of the feed drives (the feed velocity/acceleration profile). The possible domains for power consumption reduction are related to the forces that exist in the machining process (cutting, inertial, gravitational and friction forces). The power invested to overcome the cutting force is necessary for the machining process but its consumption should be optimized through constant cutting engagement and feedrate scheduling strategies. The other forces are responsible for the pure mechanical power loss and they should be minimized. The optimization variables for inertial and gravitational forces include the feed motion profile and moving mass configuration, while friction losses are load and/or velocity dependent. In addition, there are some power losses in the electric components of machine tool drives (electrical power losses), whose dependency on velocity and mechanical load can also be modeled. Despite many research efforts to analyze and model the phenomena causing inefficiency of the machining process, they are usually not taken into account during milling process planning. In fact, rare are the attempts to use this knowledge to optimize the milling process with more than one of the above mentioned criteria. The reason for that lies in the fact that increasing the instantaneous values of tool engagement and feedrate in order to minimize total machining time also increases the consumed power and, indirectly, deteriorates the tool and/or destabilizes the cutting process. The existence of conflicting criteria in the objective functions imposes the use of evolutionary algorithms for multiobjective optimization. The process of experimental validation of such an optimization model requires costly and complex external hardware setup and it is also time consuming. Hence, there is an emerging need for the development of an accurate model, which would be used for the simulation of the milling process and testing the competing optimization strategies. The ultimate goal of contemporary manufacturing science and engineering is the development of a holistic virtual machine tool system. The motivation of this PhD research is to contribute to this objective by developing the “virtual milling process for prismatic parts (produced by 2.5D milling)”. This category of objects is selected, because the majority of CNC milling tasks can be performed using 2.5D milling. A very large number of mechanical parts are prismatic (their geometry consists exclusively of features that represent 2D contours extruded in a perpendicular direction). Parts that are even more complex are usually produced from a blank by a 2.5D roughing and 3D–5D finishing. Therefore, development of an accurate model of a 2.5D milling process represents a significant contribution to manufacturing engineering. Starting with above given problem description, we have defined the following research objectives: 1. Model power and energy consumption of a 2.5D milling machine as a function of the toolpath geometry (curvature and continuity), cutting engagement, real feedrate profiles and spindle rotational speed, taking into account moving mass configuration, all power losses (mechanical and electrical) in feed-axis and spindle drives and electromechanical constraints of servo drives; 2. Design an integrated software environment for simulation of the developed models; 3. Validate the model experimentally; 4. Demonstrate the potential of its use for multi-objective process optimization. Cutting engagement calculation is performed by using two competing methodologies, in order to make a comparison of their performances. The first one, the pixel based method, is based on discrete image processing, while the other, the exact method, is based on Boolean operations and vector geometry. The tests performed in more than 20 feature/toolpath combinations demonstrate that the exact method is more precise whereas the pixel-based method is computationally much faster. The machine tool operator commands a constant feedrate value. During the process, based on his/her subjective judgment, he/she can manually tune it to keep the process in the desired boundaries. One of the objectives of this research is to predict the real feedrate values so that they can be integrated in the part program before the process start. The feasible profiles of feed velocities for each toolpath segment are analyzed by answering the following questions: can the commanded feedrate be achieved and with what sequence of acceleration and deceleration phases, how much the tool has to slow down in corners and how does the feedrate profile degenerate in segments where the commanded feedrate cannot be reached? In order to determine all forces and torques, a machine tool model is developed. The following subsystems are modeled as rigid bodies (without vibrations): feed motor shaft, transmission, lead screw/nut pair, lead screw bearings, guides (sliding and rolling design), table with the workpiece and electrospindle (rotor, stator and bearings). All kinetic variables (linear and angular speeds; cutting, inertial and friction forces and torques) are then calculated in the joints of this multi-body system. The modeling of all speeds, forces and torques that occur in the machine tool system during the machining process, allows for calculation of various indicators of process time and energy efficiency: material removal rate, total machining time, useful power (invested in cutting), power loss due to the friction in the joints, power losses in electrical motors, electrical energy consumption of feed and spindle drives. In order to assess the accuracy and the reliability of the developed model two sets of experiments have been performed. The model validation is performed on several subsystem levels and the simulation results show a very good compliance with the experimental results. The first experimental setup consists of the force dynamometer, to measure cutting force components in linear axis directions, two laser optical position sensors, to measure the real feedrate and the power sensor, to measure the total power consumption of the machine tool electrical motors. The machining process was performed on the 6-axis machining center C.B. Ferrari A152 and the data acquisition platform was developed in LabVIEW 2010. The second experimental setup consists of the torque dynamometer, to measure the cutting torque directly on the spindle, the built-in current sensor that measures the motor current proportional to the electrical power consumed by the spindle and, again, the power sensor, to measure the total power consumption of the machine tool electrical motors. This time, MIKRON HPM 600U, a 5-axis milling machine equipped with the controller iTNC530, was used for the experimentation. The developed model is ready for practical implementation. Several examples have been prepared to demonstrate its capabilities in the domains of milling process simulation, sensitivity analysis and prospective optimization strategies. These examples include the prediction of various kinetostatic variables for given toolpaths, choice of the optimum machining strategy (minimization of machining time, moving mass and energy consumption) and process optimization by feedrate scheduling (reduction of cutting force fluctuation). As a conclusion, the major contribution of this PhD research is the development of a comprehensive rigid-body dynamics model of a machine tool system, aimed for the generation, simulation and optimization of 21⁄2D milling process plans. The model features several novelties compared to existing approaches: (i) it takes into account the machine tool rigid body dynamics and real tool kinematics in toolpath planning, (ii) it predicts all mechanical and electrical power losses in the machine tool system (iii) it allows the modeling of machine tool system electromechanical constraints. The model was experimentally validated on two real machine tools in different workshops and its capabilities for the simulation of different toolpath patterns and optimization of process parameters were successfully demonstrated using several examples.

EPFL2012

Developing energy estimation model based on Sustainability KPI of machine tools

Ian Anthony Stroud, Jumyung Um

Since eco-efficiency of manufacturing resource has been emphasized, various sensors to measure energy consumption have been developed and machine tool builders also provide data of energy consumption of their own products. Due to the variety and complexity of machine tools, however, an enormous amount of data is generated and can lead to uncertainties in further interpretation. The data relating to energy consumption can be classified into process parameters and machine specifications. In order to estimate the energy use that a new machine tool utilizes, the relationship with various performance indicators of the machine tool and a process plan should be examined. The challenge is how to link the machine specifications and process plan in order to obtain actual energy consumption. This paper proposes an approach for deriving an energy estimation model from general key performance indicators of the sustainability of machine tools. For the detailed application, the proposed methodology is applied to the laser welding process of an automotive assembly line and the milling process of an aircraft part manufacturer. The paper describes the methodology for finding the parameters necessary for calculating energy use and to develop the energy estimation model by utilizing experimental data. (C) 2015 Published by Elsevier B.V.