The evaluation of a novel haptic machining VR-based process planning system using an original process planning usability method
Fletcher, Craig Allen
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This thesis provides an original piece of work and contribution to knowledge by creating a new process planning system; Haptic Aided Process Planning (HAPP). This system is based on the combination of haptics and virtual reality (VR). HAPP creates a simulative machining environment where Process plans are automatically generated from the real time logging of a user’s interaction. Further, through the application of a novel usability test methodology, a deeper study of how this approach compares to conventional process planning was undertaken. An abductive research approach was selected and an iterative and incremental development methodology chosen. Three development cycles were undertaken with evaluation studies carried out at the end of each. Each study, the pre-pilot, pilot and industrial, identified progressive refinements to both the usability of HAPP and the usability evaluation method itself. HAPP provided process planners with an environment similar to which they are already familiar. Visual images were used to represent tools and material whilst a haptic interface enabled their movement and positioning by an operator in a manner comparable to their native setting. In this way an intuitive interface was developed that allowed users to plan the machining of parts consisting of features that can be machined on a pillar drill, 21/2D axis milling machine or centre lathe. The planning activities included single or multiple set ups, fixturing and sequencing of cutting operations. The logged information was parsed and output to a process plan including route sheets, operation sheets, tool lists and costing information, in a human readable format. The system evaluation revealed that HAPP, from an expert planners perspective is perceived to be 70% more satisfying to use, 66% more efficient in completing process plans, primarily due to the reduced cognitive load, is more effective producing a higher quality output of information and is 20% more learnable than a traditional process planning approach.