Process planning is a task comprising a broad range of activities to design and develop an appropriate manufacturing process for producing a part. Interpretation of the part design, selection of manufacturing processes, definition of operations, operation sequences, machining datums, geometrical dimensions and tolerances are some common activities associated with the task. Process planning is also “the link between product design and manufacturing” with the supplementary commission to support design of competitive products. Process planning is of a complex and dynamic nature, often managed by a skilled person with few, or no, explicit methods to solve the task. The work is heuristic and the result is depending on personal experiences and decisions. Since decades, there have been plenty of attempts to develop systems for computer-aided process planning (CAPP). CAPP is still awaiting its breakthrough and one reason is the gap between the functionality of the CAPP systems and the industrial process planning practice. This thesis has an all-embracing aim of finding methods that cover essential activities for process planning, including abilities to predict the outcome of a proposed manufacturing process. This is realised by gathering supporting methods suitable to manage both qualitative and quantitative characterisation and analyses of a manufacturing process. The production research community has requested systematisation and deeper understanding of industrial process planning. This thesis contributes with a flow chart describing the process planning process (PPP), in consequence of the methodological studies. The flow chart includes process planning activities and information flows between these activities. The research has been performed in an industrial environment for high volume manufacturing of gear parts. Though gear manufacturing has many distinctive features, the methods and results presented in this thesis are generally applicable to precision manufacturing of many kinds of mechanical parts. / <p>QC 20140522</p>

Studies of avian navigation are making increasing use of miniature Global Positioning Satellite devices, to regularly record the position of birds in flight with high spatial and temporal resolution. I suggest a novel approach to analysing the data sets pro- duced in these experiments, focussing on studies of the domesticated homing pigeon (Columba Livia) in the local, familiar area. Using Gaussian processes and Bayesian inference as a mathematical foundation I develop and apply a statistical model to make quantitative predictions of homing pigeon flight paths. Using this model I show that pigeons, when released repeatedly from the same site, learn and follow a habitual route back to their home loft. The model reveals the rate of route learning and provides a quantitative estimate of the habitual route complete with associated spatio-temporal covariance. Furthermore I show that this habitual route is best described by a sequence of isolated waypoints rather than as a continuous path, and that these waypoints are preferentially found in certain terrain types, being especially rare within urban and forested environments. As a corollary I demonstrate an extension of the flight path model to simulate ex- periments where pigeons are released in pairs, and show that this can account for observed large scale patterns in such experiments based only on the individual birds’ previous behaviour in solo flights, making a successful quantitative prediction of the critical value associated with a non-linear behavioural transition.