Machined surfaces contain rich information about machining conditions and system behavior and are typically assessed using off-line, small-area metrology. This study developed and validated an image-based methodology for process-oriented surface texture analysis of end-milled Spheroidal Graphite Iron (SGI), enabling scalable, non-contact monitoring suitable for in-line deployment. End milling trials were conducted under optimized and aggressive cutting conditions and in two orthogonal feed directions (X,Y). Surface topography from White Light Interferometry (WLI) was complemented by Charge-Coupled Device (CCD) microscope imaging. Image processing comprised automatic orientation correction, intensity profile extraction, and frequency-domain analysis via Fast Fourier Transform and power spectral density estimation. Texture metrics (RMS amplitude, skewness, kurtosis, dominant wavelength) were derived from intensity profiles, and two spectral indices were introduced: a Change Index (CI), capturing high-frequency content linked to process disturbances, and a Surface Anisotropy Metric (SAM), quantifying texture directionality. Aggressive cutting increased RMS by 28.5% and shifted skewness by 274% with strong statistical significance. Directional analysis showed 22% higher texture amplitude in Y than X, indicating axis-dependent machine behavior. CI correlated with the machining parameters and stability, while SAM reflected the machine and setup characteristics. Trends were consistent with WLI, supporting the method as a rapid, complementary tool for surface quality and machine condition monitoring.

The evaluation of machine tool characteristics and their impact on surface quality is challenging, often requiring disruptive traditional methods. This study introduces a novel, non-invasive approach using optical camera images for rapid and accurate assessment. Data robustness was ensured by acquiring initial images outside the machining chamber with consistent external illumination, focusing on detailed intensity profile analysis. Machined surfaces were processed using intensity profile extraction and Fast Fourier Transform (FFT). The dominant spatial wavelength (0.1833 mm) consistently showed excellent agreement (within 1.85%) with the theoretical feed per revolution (0.1800 mm). This robustly validates the method's ability to precisely capture primary kinematic tool marks. Temporal information, inferred from spatial frequencies, underwent subsequent FFT to identify periodic phenomena and harmonics. The comprehensive spatial and temporal FFT analyses offer detailed, quantitative surface characterizations. The clear distinctions in temporal harmonic patterns provide robust, frequency-domain signatures informing machining system performance and process integrity.

The transport sector is growing and so is the awareness of the environmental impact from fossil fuels. This calls for changes in how road transport is powered, driven by both rules and regulations and from customer and societal expectations. There are several technical solutions to reduce and finally replace the use of fossil fuels currently discussed both in academia and industry and those solutions are at different maturity levels. The aim of this research is to investigate how the introduction of new technologies effects the evolvement of the components in the powertrain. This knowledge will be valuable for the truck industry OEMs to support the transition of the production system to match future needs. This is done in two parts. First, a semi-structured interview with experts from the automotive industry was conducted, then a literature study. The research shows that several powertrain technologies will exist, optimized for different markets and applications. On a component level, effort will be made to reduce the losses in the powertrain and the strive for efficiency will lead to higher requirements on geometrical quality, tighter tolerances, and surface requirements.

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.