Energy Efficiency in Machining of Aircraft Components

High production costs and material removal rates characterize the manufacturing of aircraft components made of titanium. Due to competitive pressure, the manufacturing processes are highly optimized from an economical perspective, whereas environmental aspects are usually not considered. One example is the recycling of titanium chips. Because of process-induced contaminations they do not meet the quality required for recycling in high-grade titanium alloys. Thus the components need to be manufactured from primary material, which leads to a poor energy balance. This paper describes a methodology to increase the recycling rate and energy efficiency of the manufacturing process by investigating the influencing parameters on chip quality of the machining process with the aim to increase the chip quality to a recyclable degree under monetary aspects. The analysis shows that the recycling rate can be significantly increased through dry cutting, which also brings economic benefits.German Federal Ministry for Economic Affairs and Energy (BMWi)/03ET1174

Combining in-house Pooling and Sequencing for Product Regeneration by Means of Event-driven Simulation

The condition of complex products in the transport industry, such as train couplings or aircraft turbines, is not exactly determinable before their disassembly and diagnosis in a maintenance plant. Thus, planning and control of their regeneration is impeded since work plans and spare part demand result at short notice. This paper presents a novel method, which combines a planning approach, the in-house pooling of components, and a controlling approach, the sequencing of components, by means of event-driven simulation. Thereby, mean cycle time, mean tardiness and on-time delivery can be optimized under the consideration of the volatile conditions. © 2017 The Authors. Published by Elsevier B.V.German Ministry of Economics and Energ

Competence-based Personnel Scheduling through Production Data

Personnel scheduling closes the missing link between personnel and production planning in manufacturing companies. Personnel scheduling has a significant impact on the development of employee competencies and the achievement of production goals. Nevertheless, in industrial practice the performance of the employee is not considered in production planning. According to the 5th Global Productivity Study of the Proudfoot Consulting 37% of working time is wasted, which is mainly due to a lack of planning and control. A major reason is the difficult measurability of employee competence and performance. In this paper, a method that describes the influence of employees on the processing and set-up time and its implementation at a manufacturer of thread parts is shown. For this, production data is statistically evaluated to predict the employee's influence. This information will be used by an algorithm for personnel scheduling. Thus, the highest possible competence development can be achieved in accordance with the utilization of the production. © 2017 The Authors. Published by Elsevier

Augmenting Milling Process Data for Shape Error Prediction

New integrated sensors and connected machine tools generate a tremendous amount of in-depth process data. The continuous transformation of the obtained data into deployable machining knowledge allows for faster ramp-ups, more reliable process outcome and higher profitability. A system for recording data from various sources - including a simultaneous material removal simulation - is implemented to aggregate and store process data. In addition to the simulation results, process data from the machine control, cutting forces and shape error samples are collected. A series of slot milling processes are carried out with varying cutting speed, feed per tooth and width of cut in a full factional design. In order to continuously evaluate process data, automatized methods are required. This is achieved using the simulation results to determine all relevant cutting conditions. Dependencies between cutting parameters, sensor signals and cutting result are identified and quantified. However, a one-dimensional model does not predict the shape error accurately. As an alternative model, a multidimensional model based on a Support Vector Machine is trained, using process forces and simulation data. The obtained prediction accuracy is significantly higher compared to the one-dimensional model and can be used to design highly reliable cutting processes.DFG/CRC/65

Simulation-based surface roughness modelling in end milling

The surface topography often is an important quality criterion for the manufacturing of milled workpieces as it often defines their functional behaviour. In machining both, the kinematics of the process and the stochastic influences deriving from the machine tool, workpiece and the surrounding environment affect the workpiece's surface roughness. This paper presents a simulation-based method for flank milling, which considers kinematic and stochastic influences including run-out errors and tooth length variations. The simulation results are used in combination to predict the surface roughness depending on the chosen process parameters. Hence, also making in possible to choose appropriate process parameters to achieve a defined surface roughness

Simulation-based feed rate adaptation considering tool wear condition

The process forces generated in machining are related to a deflection of the milling tool, which results in shape deviations. In addition to process parameters like feed rate, width and depth of cut or cutting speed, the wear condition of the tool has a significant influence on the shape deviation during flank milling. In process planning it is important to take the tool condition and the ideal time for tool change into account when selecting the process parameters. An assistance system is being researched at the Institute of Production Engineering and Machine T ools (IFW) in cooperation with Kennametal Shared Services GmbH to support this task. T he assistance system adjusts automatically the feed rate considering a predefined maximum shape deviation. Additionally, it identifies an optimal moment for tool change. T he advantages of the system are particularly evident in planning of individual milling processes. T he assistance system is based on a combination of a material removal simulation and empirical models of the shape error. For this purpose, spindle currents as well as measured shape errors are stored in a database. T hese data are extended by the actual local cutting conditions calculated by a process-parallel material removal simulation. Afterwards, the data is transferred into process knowledge via a Support Vector Machine (SVM). Within a technological NC simulation before the start of manufacturing, the generated knowledge is applied to predict the shape error of the workpiece and to automatically adjust the feed rate. By adapting the feed rate, it is possible to control the tool life. T he required tool change is defined by specifying a limit for the permitted width of flank wear land. T he presented assistance system enables the prediction of the shape error parallel to the manufacturing process and the automatic determination of the feed rate as well as the ideal time for tool change

Ontology-based production planning under the consideration of system robustness

Volatile markets and high customer requirements regarding schedule reliability increase the relevance of robust production planning. To achieve robustness in planning, system-inherent buffers are used. Buffers include resource capacities that are kept free to respond to changes. Targets other than achieving the production plan are not considered, so a trade-off between reliability and the further development of the manufacturing process is not possible. This paper presents a new approach for production planning based on robustness analysis that enables a multi-criteria optimization. An information system enables the company-specific design of the robustness analysis

Technological CAD/CAM chain for automated polishing of geometrically complex workpieces

Despite recent advances in automation technology, geometrically complex workpieces are still often finished manually. An example for this is the post-processing of dental prostheses, which display exceptionally high surface requirements. Aiming for an improved quality assurance, this article presents a methodology for an adaptive tool path planning for polishing of geometrically complex workpieces. For this purpose, the initial roughness of the workpiece is determined using a machine-integrated measuring system. Next, suitable process parameters are selected based on machine knowledge and the adapted NC code of the polishing process is generated and simulated. The results of the simulation and the actual polishing process are compared afterwards and transformed into process knowledge. Thus, the adaption of the process parameters and the quality of the simulation are continuously improved. The article highlights the implementation of the methodology with special emphasis on the selection of the process parameters

Investigations on a standardized process chain and support structure related rework procedures of SLM manufactured components

For the successful production of high quality parts by selective laser melting, various process steps are required. Besides the SLM process itself, different pre- and rework steps are needed to produce a final component. Therefore, the first part of this paper presents a concept of a standardized process chain for carrying out the necessary planning and production procedures. For this purpose, the CAD-model is enriched with information regarding support structures, the desired surface quality and the position of tooling points. Since major steps in the reworking procedure are the removal of residual powder, the removal of support structures and the finishing operations for functional component surfaces, selected experimental results concerning these steps are presented in the second part of the paper. Based on the result, recommendations for the design of support structures are given

Machine Learning Approach for Optimization of Automated Fiber Placement Processes

Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved.BMWi/ZIM KF2328125PO