Industry-driven test piece methodology for multi-axis machining system assessment under production conditions

Abstract The introduction of new products necessitates a shift in manufacturing systems from high volume/low diversity to high volume/high diversity production. A fundamental element of the manufacturing system is the machine tool which has traditionally been acquired for specific well-defined operation. However, evolving requirements demand that machine tools be capable of producing a wider range of products particularly with multi-axis machining capabilities. The main focus of existing machine tool test methods is to assess and verify the geometric and kinematic accuracy of the machine, ensuring that positioning, motion, and spindle performance meet specified tolerances. Error separation techniques are often employed within these methods to distinguish and quantify different sources of error, such as those arising from the machine, the measurement system, or the test piece. This research presents a novel systematic methodology for evaluating machining systems through machining tests using a modular scalable test piece designed from industry requirements. The methodology encompasses a process based on industry-specific requirements where critical features from actual components are translated into a test piece design that replicates real-world machining challenges while maintaining measurability and comparability. The approach was examined through experimental machining of test pieces in Spheroidal Graphite Iron under two process conditions balanced quality/cycle time versus minimum cycle time using a five-axis machining centre. Results demonstrated significant differences in geometric accuracy particularly in datum establishment and positional features with aggressive cutting parameters showing substantially larger deviations. This study provides a practical methodology for assessing machine tool adaptability and capability in high-diversity production environments contributing to improved asset management and manufacturing system design.