Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

Abstract

The State Space modelling approach has been recently proposed as an engineeringdriven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data

“…Furthermore, manufacturing operation capability cannot be estimated through general specifications from machine-tool vendors, since it also depends on many other factors such as the type of manufacturing operation, components wear, thermal effects, deflections, and cutting conditions. For instance, Abellan-Nebot et al [18] investigated the operationinduced deviations on a vertical machining center with a position accuracy of ±5 m. A 10 • C temperature increase caused thermal expansion on the spindle and produced a 50 m deviation on the machined surface, whereas in other experiments a 0.3 mm tool flank wear generated a 37 m machined surface deviation. The impacts of these factors on process capability can often be estimated from historical shop-floor quality data.…”

State Space Modeling of Variation Propagation in Multistation Machining Processes Considering Machining-Induced Variations

“…This generic model, named conventional SoV model, hereafter, considers three types of variation sources that are related to: datum surfaces that are used for locating the workpiece (datum-induced variations); fixture components that locate the workpiece on the machine-tool table (fixture-induced variations); and machining operations that generate the cutting-tool path (machining-induced variations). Recently, Abellan-Nebot et al [9] demonstrated that overlooking the machining-induced variations in conventional SoV models may lead to inaccurate variation prediction. In an experiment of a three-station machining process, severe cutting-tool wear and significant spindle thermal expansion were intentionally added to the process.…”

Process-oriented tolerancing using the extended stream of variation model

“…However, machinetools present other process variables that influence on the cutting-tool path accuracy such as cuttingtool wear, thermal state of the spindle, etc [17]. In fact, a recent research work [18] demonstrated that without considering these process variables in the SoV model, part quality prediction at the end of a MMP may result in important misleading conclusions. Therefore, a complete tolerance allocation requires the inclusion of additional process variables.…”