Modelling lay-up automation and production rate interaction on the cost of large stiffened panel components

This paper presents an integrated design and costing method for large stiffened panels for the purpose of investigating the influence and interaction of lay-up technology and production rate on manufacturing cost. A series of wing cover panels (≈586kg, 19·9m2) have been sized with realistic requirements considering manual and automated lay-up routes. The integrated method has enabled the quantification of component unit cost sensitivity to changes in annual production rate and employed equipment maximum deposition rate. Moreover the results demonstrate the interconnected relationship between lay-up process and panel design, and unit cost. The optimum unit cost solution when using automated lay-up is a combination of the minimum deposition rate and minimum number of lay-up machines to meet the required production rate. However, the location of the optimum unit cost, at the boundaries between the number of lay-up machines required, can make unit cost very sensitive to small changes in component design, production rate, and equipment maximum deposition rate. - See more at: http://aerosociety.com/News/Publications/Aero-Journal/Online/1941/Modelling-layup-automation-and-production-rate-interaction-on-the-cost-of-large-stiffened-panel-components#sthash.0fLuu9iG.dpuf

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

A Structured Approach to Simulate Manufacturing System In Virtual Environment

Nowadays, the realization of the Virtual Factory (VF) is the strategic goal of many manufacturing enterprises for the coming years. The industrial scenario is characterized by the dynamics of innovations increment and the product life cycle became shorter. Furthermore products and the corresponding manufacturing processes get more and more complex. Therefore, companies need new methods for the planning of manufacturing systems.
To date, the efforts have focused on the creation of an integrated environment to design and manage the manufacturing process of a new product. The future goal is to integrate Virtual Reality (VR) tools into the Product Lifecycle Management of the manufacturing industries.
In order to realize this goal the authors have conducted a study to perform VF simulation steps for a supplier of Industrial Automation Systems and have provided a structured approach focusing on interaction between simulation software and VR hardware tools in order to simulate both robotic and
manual work cells.
The first results of the study in progress have been carried out in the VR Laboratory of the Competence Regional Centre for the qualification of the Transportation Systems that has been founded by Campania Region.

Influence of Manufacturing Tolerance on Aircraft Direct Operating Cost (DOC)

The influence of manufacturing tolerance on direct operating cost (DOC) is extrapolated from an engine nacelle to be representative of an entire aircraft body. Initial manufacturing tolerance data was obtained from the shop floor at Bombardier Aerospace Shorts, Belfast while the corresponding costs were calculated according to various recurring elements such as basic labour and overtime labour, rework, concessions, and redeployment; along with the non-recurrent costs due to tooling and machinery, etc. The relation of tolerance to cost was modelled statistically so that the cost impact of tolerance change could be ascertained. It was shown that a relatively small relaxation in the assembly and fabrication tolerances of the wetted surfaces resulted in reduced costs of production that lowered aircraft DOC, as the incurred drag penalty was predicted and taken into account during the optimisation process.

Parametric optimisation of manufacturing tolerances at the aircraft surface

Up until now, aircraft surface smoothness requirements have been aerodynamically driven with tighter manufacturing tolerance to minimize drag, that is, the tighter the tolerance, the higher is the assembly cost in the process of manufacture. In the current status of commercial transport aircraft operation, it can be seen that the unit cost contributes to the aircraft direct operating cost considerably more than the contribution made by the cost of block fuel consumed for the mission profile. The need for a customer-driven design strategy to reduce direct operating cost by reducing aircraft cost through manufacturing tolerance relaxation at the wetted surface without unduly penalizing parasite drag is investigated. To investigate this, a preliminary study has been conducted at 11 key manufacturing features on the surface assembly of an isolated nacelle. In spite of differences in parts design and manufacture, the investigated areas associated with the assembly of nacelles are typical of generic patterns in the assembly of other components of aircraft. The study is to be followed up by similar studies extended to lifting surfaces and fuselage