@inproceedings{597,
  abstract     = {{This paper is aimed to discuss current research using data mining techniques and industry statistics in production environments. The general research approach is based on the idea of using data mining processes and techniques of industry statistics to find rare and hidden patterns behind failures of complex components. A case study will be applied to illustrate how the technique is carried out and where the limits of this approach occur. The case study deals with a component supplier of printing machines, which received an increasing number of client complaints, all related to one distinct problem. The observed failures seem to occur only among clients with very high quality standards. The affected component undergoes a very complex production process with several steps in different departments. Every single production unit records data information from multiple process variables and at different points in time. In the beginning there was no understanding of the failure causes in production at all. Therefore a huge amount of production data had to be analyzed to find the pattern that discloses the failure.
The data mining process starts with a first step in which the given data sets are prepared and then cleaned. Followed up by building a prediction model. The aim is to detect the root causes for failures and to predict potential failures in affected components. This paper shows how to use data mining to get the answer on pressing production failures.
}},
  author       = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea}},
  booktitle    = {{Production engineering and management : proceedings, 5th international conference, October 1 and 2, 2015, Trieste, Italy}},
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-941645-11-0}},
  keywords     = {{Data mining, production failure, multi-variant analysis, multivariate process control, predictive modelling, case study}},
  location     = {{Trieste, Italy}},
  number       = {{1}},
  pages        = {{163--174}},
  publisher    = {{Hochschule Ostwestfalen-Lippe}},
  title        = {{{Data Mining: A Potential Detector to Find Failure in Complex Components}}},
  year         = {{2015}},
}

@inproceedings{598,
  abstract     = {{The aerospace sector is characterized by long product life cycles and a need for lightweight design. Additive manufacturing is a technology that produces parts layer by layer and thus enables the manufacturing of any complex parts at nearly no extra costs. A topology optimization enhances the part’s
performance for their special purpose. The results are often complex bionic structures that cannot be produced with conventional manufacturing technologies. The paper analyzes how the high potential of this technologycan be applied to aerospace parts. A topology optimization will be conducted for an aircraft part explaining the crucial points and a life cycle analysis examines the achieved sustainable improvements for the aircraft’s life cycle.
}},
  author       = {{Huxol, Andrea and Villmer, Franz-Josef}},
  booktitle    = {{Production Engineering and Management}},
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-941645-11-0}},
  keywords     = {{Additive manufacturing, topology optimization, aerospace, life cycle costs}},
  location     = {{Trieste, Italy}},
  number       = {{1}},
  pages        = {{207--218}},
  title        = {{{Hybrid Manufacturing Machines: Combining Additive and Subtractive Manufacturing Technologies}}},
  year         = {{2015}},
}

@inproceedings{599,
  abstract     = {{Order picking has long been identified as the most labor costly and intensively activity in warehouse management. The orders from the customers need to be fulfilled tightly and timely. In order to keep the required high service level, the warehouse has to increase the picking productivity under the constraints of limited capacity. This paper concerns a man-togoods order picking system, in which the order pickers have to drive with a pallet jack to the storage locations. Considering that the orders are mostly small orders which consist of less lines, it is efficient to combine severalsingle customer orders into one picking order. Under this circumstance, this paper intends to answer the question of how customer orders should be grouped into picking orders with the aim of minimizing the total travel length through the warehouse. Consequently the productivity of the order picking system can be improved. An optimization problem for order batching is introduced. The optimization method of order batching is then proposed. Based on the simulation of different scenarios of incoming orders, it can be concluded that the developed method is effective in improving the productivity of the concerned order picking system.
}},
  author       = {{Li, Li and Schulze, L.}},
  booktitle    = {{Production Engineering and Management}},
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-941645-11-0}},
  keywords     = {{Order picking, man-to-goods, order batching, picking productivity, genetic algorithm}},
  location     = {{Trieste, Italy}},
  number       = {{1}},
  pages        = {{319--326}},
  title        = {{{Improving the Productivity of a Man-to-Goods Order Picking System through Optimization of Order Batching}}},
  year         = {{2015}},
}

@proceedings{589,
  editor       = {{Villmer, Franz-Josef and Padoano, Elio}},
  isbn         = {{978-3-941645-10-3}},
  location     = {{Lemgo}},
  pages        = {{301}},
  title        = {{{Production Engineering and Management}}},
  volume       = {{10}},
  year         = {{2014}},
}

@proceedings{725,
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-941645-09-7}},
  location     = {{Trieste, Italy}},
  pages        = {{282}},
  title        = {{{Production Engineering and Management}}},
  volume       = {{9}},
  year         = {{2013}},
}