In mixed reality (MR) remote collaborative assembly, remote experts can guide local users to complete the assembly of physical tasks by sharing user cues (eye gazes, gestures, etc.) and spatial visual cues (such as AR annotations, virtual replicas). At present, remote experts need to carry out complex operations to transfer information to local users, but the fusion of virtual and real information makes the display of information in the MR collaborative interaction interface appear messy and redundant, and local users sometimes find it difficult to pay attention to the focus of information transferred by experts. Our research aims to simplify the operation of remote experts in MR remote collaborative assembly and to enhance the expression of visual cues that reflect experts' attention, so as to promote the expression and communication of collaborative intention that user has and improve assembly efficiency. We developed a system (EaVAS) through a method that is based on the assembly semantic association model and the expert operation visual enhancement mechanism that integrates gesture, eye gaze, and spatial visual cues. EaVAS can give experts great freedom of operation in MR remote collaborative assembly, so that experts can strengthen the visual expression of the information they want to convey to local users. EaVAS was tested for the first time in an engine physical assembly task. The experimental results show that the EaVAS has better time performance, cognitive performance, and user experience than that of the traditional MR remote collaborative assembly method (3DGAM). Our research results have certain guiding significance for the research of user cognition in MR remote collaborative assembly, which expands the application of MR technology in collaborative assembly tasks.

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

In parallel with the fast growth of the second-hand aviation market, the importance of promoting remanufacturing analytics has increased. However, end-of-life (EoL) aircraft parts remanufacturing operations are still underdeveloped. Disassembly, the most challenging and central activity in remanufacturing, directly affects the EoL product recovery's profitability and sustainability. Disassembly sequence planning (DSP) devises ordered and purposeful parting for all potentially recoverable components before physical separations. However, the complexities and uncertainties of the EoL conditions engender unpredictable DSP decision inputs. The EoL DSP needs emergent evidence of cost-effective solutions in view of Industry 4.0 (I4.0) implications and stakeholders' benefits. Among the I4.0 technologies, X-reality (XR) particularly hits the mainstream as a cognitive and visual tool consisting of virtual reality, augmented reality, and mixed reality. Recently, with the advance of I4.0 phenomenon, lean management has been theorized and tested through complementary collaboration. Since the research of integrating lean and XR into the EoL DSP is underexplored in literature, XR and lean are investigated as assistive enablers in the DSP. This study has a two-fold purpose: (1) identifying the key concepts of DSP, I4.0, XR, and lean, and extending the literature by reviewing the previous efforts of EoL aircraft remanufacturing, XR-assisted DSP, and XR-lean applications; (2) proposing "Smart Disassembly Sequence Planning (SDSP)" as a new EoL decision-support agenda after analyzing relational advantages and evolving adaptability. The barriers and limitations are highlighted from the recent associated topics, concrete academic information for developing digitalized disassembly analytics is provided, and new trends are added for future disassembly research.

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment.

Biomaterials are engineered to develop an interaction with living cells for therapeutic and diagnostic purposes. The last decade reported a tremendously rising shift in the requirement for miniaturized biomedical implants exhibiting high precision and comprising various biomaterials such as non-biodegradable titanium (Ti) alloys and biodegradable magnesium (Mg) alloys. The excellent mechanical properties and lightweight characteristics of Mg AZ91D alloy make it an emerging material for biomedical applications. In this regard, micro-electric discharge machining (EDM) is an excellent method that can be used to make micro-components with high dimensional accuracy. In the present research, attempts were made to improve the EDM capabilities by using cryogenically-treated copper (CTCTE) and brass tool electrodes (CTBTE) amid machining of biodegradable Mg AZ91D alloy, followed by their comparison with a pair of untreated copper (UCTE) and brass tool electrodes (UBTE) in terms of minimum machining-time and dimensional-irregularity. To investigate the possible modification on the surfaces achieved with minimum machining-time and dimensional-irregularity, the morphology, chemistry, micro-hardness, corrosion resistance, topography, and wettability of these surfaces were further examined. The surface produced by CTCTE exhibited the minimum surface micro-cracks and craters, acceptable recast layer thickness (2.6 µm), 17.45% improved micro-hardness, satisfactory corrosion resistance, adequate surface roughness (: 1.08 µm), and suitable hydrophobic behavior (contact angle: 119°), confirming improved biodegradation rate. Additionally, a comparative analysis among the tool electrodes revealed that cryogenically-treated tool electrodes outperformed the untreated ones. CTCTE-induced modification on the Mg AZ91D alloy surface suggests its suitability in biodegradable medical implant applications.

The effect of printing speed on the tensile strength of acrylonitrile butadiene styrene (ABS) samples fabricated using the fused deposition modelling (FDM) process is addressed in this research. The mechanical performance of FDM-ABS products was evaluated using four different printing speeds (10, 30, 50, and 70 mm/s). A numerical model was developed to simulate the experimental campaign by coupling two computational codes, Abaqus and Digimat. In addition, this article attempts to investigate the impacts of printing parameters on ASTM D638 ABS specimens. A 3D thermomechanical model was implemented to simulate the printing process and evaluate the printed part quality by analysing residual stress, temperature gradient and warpage. Several parts printed in Digimat were analysed and compared numerically. The parametric study allowed us to quantify the effect of 3D printing parameters such as printing speed, printing direction, and the chosen discretisation (layer by layer or filament) on residual stresses, deflection, warpage, and resulting mechanical behaviour.

The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study.

In recent years, micro-hole drilling with a diameter of less than 1 mm has been widely applied in electronic information, semiconductor, metal processing, and other fields. Compared with conventional drilling, the engineer problems that micro drills are more prone to suffer failure in advance have restricted further development of mechanical micro drilling. In this paper, the main substrate materials of micro drills were introduced. And two important technical means to improve properties of tool material, namely, grain refinement and tool coating, were also introduced, which are current main research directions of micro drills from the perspective of materials. The failure mechanisms of micro drills were briefly analyzed, mainly tool wear and drill breakage. In the structure of micro drills, cutting edges and chip flutes are directly related to tool wear and drill breakage, respectively. So the structural optimization and design of micro drills, especially for key structures such as cutting edges and chip flutes, have to face great challenges. Based on the above, two pairs of requirements for micro drills were proposed, that is, the balance between chip evacuation and drill stiffness and the balance between cutting resistance and tool wear. So some innovative schemes and related researches of micro drills regarding cutting edges and chip flutes were reviewed. Finally, a summary of micro drill design and existing problems and challenges is proposed.

The numerical simulation of metal cutting processes requires material data for constitutive equations, which cannot be obtained with standard material testing procedures. Instead, inverse identifications of material parameters within numerical simulation models of the cutting experiment itself are necessary. This report presents the findings from a large-scale study of dry orthogonal cutting experiments on Ti6Al4V (Grade 5) and Ck45 (AISI 1045). It includes material characterization through microstructural analysis and tensile tests. The study details the measurement of cutting insert geometries and cutting edge radii, evaluates process forces, deduces friction coefficients and coefficients for Kienzle's force model, and analyzes chip forms and thicknesses as well as built-up edge formation depending on the process parameters. The collected data, stored in pCloud, can support other researchers in the field, e.g. for recomputations within numerical models or inverse parameter identifications. The dataset includes force measurements, cutting edge scans, and chip images including longitudinal cross-sections of chips.

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving.

This study investigated the print homogeneity of Ti-6Al-4 V alloy parts, when printed over a large build area of 250 250 170 mm, using a production scale laser powder bed additive manufacturing system. The effect of part location across this large build area was investigated based on printed part porosity, microstructure, hardness, and tensile properties. In addition, a Hot Isostatic Pressing (HIP) treatment was carried out on the as-built parts, to evaluate its impact on the material properties. A small increase in part porosity from 0.01 to 0.09%, was observed with increasing distance from the argon gas flow inlet, which was located on one side of the build plate, during printing. This effect, which was found to be independent of height from the build plate, is likely to be associated with enhanced levels of condensate or spatter residue, being deposited at distances, further from the gas flow. Despite small differences in porosity, no significant differences were obtained for microstructural features such as prior grain, lath thickness, and phase fraction, over the entire build area. Due to this, mechanical performances such as hardness and tensile strengths were also found to be homogenous across the build area. Additionally, it was also observed based on the lattice constants that partial in-situ decomposition of phases occurred during printing. Post HIP treatment result showed a decrease of 7 and 6%, in the yield strength (YS) and ultimate tensile strength (UTS), respectively, which was associated with a coarsening of lath widths. The potential of the laser powder bed system for large area printing was successfully demonstrated based on the homogenous microstructure and mechanical properties of the Ti-6Al-4 V alloy parts.

Hydrophobicity plays a pivotal role in mitigating surface fouling, corrosion, and icing in critical marine and aerospace environments. By employing ultrafast laser texturing, the characteristic properties of a material's surface can be modified. This work investigates the potential of an advanced ultrafast laser texturing manufacturing process to enhance the hydrophobicity of aluminium alloy 7075. The surface properties were characterized using goniometry, 3D profilometry, SEM, and XPS analysis. The findings from this study show that the laser process parameters play a crucial role in the manufacturing of the required surface structures. Numerical optimization with response surface optimization was conducted to maximize the contact angle on these surfaces. The maximum water contact angle achieved was 142º, with an average height roughness (Sa) of 0.87 ± 0.075 µm, maximum height roughness (Sz) of 19.4 ± 2.12 µm, and texture aspect ratio of 0.042. This sample was manufactured with the process parameters of 3W laser power, 0.08 mm hatch distance, and a 3 mm/s scan speed. This study highlights the importance of laser process parameters in the manufacturing of the required surface structures and presents a parametric modeling approach that can be used to optimize the laser process parameters to obtain a specific surface morphology and hydrophobicity.

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) "stop-and-go" approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

Precise tooling is vital for defect-free production of micro injection moulded (μ-IM) or hot-embossed products. The demoulding stage of such moulding and forming processes poses a serious challenge to the integrity of thin miniature features because of friction, adhesion, and thermal stresses. Typically, micro moulds involve geometrically textured patterns or features such as linear ridges, pillars, channels, and holes, the characteristic dimensions of which range from 10 to 300 μm. Realistically complex mould designs, containing precision micro features (enhanced fillet radius and positive draft angle) and high surface quality, are presented in this work. Electropolishing based on forward pulse currents (PC) has been used to shape and polish Ni micro moulds that contain sets of micron-scaled linear ridges and star patterns in order to ease the separation of moulded polymeric parts from the metallic mould during ejection and demoulding. The use of forward pulsed currents improved the mould design by increasing the fillet radii and draft angle while keeping the surface roughness low and maintaining a good surface shine. An optimization study of forward PC using a green solution of nickel sulfamate varied EP times (0-70 min) and duty cycles (40, 50, 60, and 70%) at a process conditions of 2.8 V, 50 °C, and 250 rpm. The best topographical and morphological changes were observed for a typical microfluidic channel ( × , 100 × 110 μm) with an EP time of 70 min and 50% duty cycle: fillet radius increased by 3.8 μm, draft angle by 3.3°, and the channel width reduced by 11.4% while surface roughness changed by 8.6% and surface shine improved by 48.9%. Experimental validation was performed using hot embossing wherein the electropolished Ni mould replicated the micro channels and star patterns in PMMA chips with notably fewer burrs, material pile up, and no feature distortion. Moreover, there was a reduction in the side wall roughness of micro channels in PDMS casting with electropolished Ni mould by 16%. Hence, this work presents a significant scientific contribution to improving the efficiency of micro mould tools and reduces the defects caused by friction and adhesion in replicated polymeric parts.

A new variant of the inherent strain (IS) method is proposed to predict component distortion in powder bed fusion additive manufacturing (AM) that addresses some of the shortcomings of the previous work by accounting for both the compressive plastic strain formed adjacent to the melt pool and the thermal strain associated with the changing macroscale thermal field in the component during fabrication. A 3D thermomechanical finite element (FE) model using the new approach is presented and applied to predict the distortion of a component fabricated in an electron beam powder bed fusion (EB-PBF) machine. To improve computational efficiency, each computational layer is comprised of six powder layers. A time-averaged volumetric heat input based on beam voltage and current data obtained from the EB-PBF system was calculated and applied to each computational layer, consistent with the process timing. The inherent strains were applied per computational layer as an initial anisotropic contribution to the thermal strain at the time of activation of each computational layer, resulting in the sequential establishment of static equilibrium during component fabrication, which accounts for the variation in the local macroscale thermal field. The thermal field and distortion predicted by the thermomechanical model were verified using experimentally derived data. The model predicts in-plane compressive strains in the order of 10. Differences in the inherent strain were found at different locations in the component, consistent with differences in the macroscale thermal field. The proposed method is general and may also be applied to the laser powder bed fusion (L-PBF) process.

One of the challenges in the transfer of heat during the mechanical machining process is the coolant substance used in the internal cooling method which is generally liquid water or a water-based coolant. This limits the heat transfer capacity insofar as the thermal conductivity of liquid water is concerned. The other difficulty is the requirement for an external mechanical system to pump the coolant around the internal channel, providing efficient transfer of the accumulated thermal energy. This study proposes a novel method to address this issue by using liquid gallium which provides the means to transfer the excess heat generated during the cutting process by integrating the design into an aluminium oxide insert. Combining this with a magnetohydrodynamic drive, the coolant system operates without the need for mechanical input. Liquid gallium is nontoxic and has a much higher thermal conductivity over liquid water. Investigations of the novel cooling system is performance compared against liquid water through numerical modelling, followed by an experimental machining test to ascertain the difference in heat transfer effectiveness, tool wear rates and workpiece surface finish when compared to dry machining and external cooling conditions on stainless steel 316L. Without cooling, experimental machining tests employing a cutting speed of V = 250 m min resulted in a corner wear VB rate of 75 μm, and with the magnetohydrodynamic-based coolant on, produced a VB rate of 48 μm, indicating a difference of 36% in relative tool wear under the same cutting conditions. Increasing the cutting speed V to 900 m min, produced a corner wear VB rate of 357 μm without the active coolant and a VB rate of 246 μm with the magnetohydrodynamic-based coolant on, representing a decrease of 31% in relative tool wear. Further tests comparing external liquid water cooling against the liquid gallium coolant showed at V = 250 m min, a difference of 29% in relative tool wear rate reduction was obtained with the internal liquid gallium coolant. Increasing the cutting speed to V = 900 m min, the data indicated a difference of 16% relative tool wear reduction with the internal liquid gallium. The results support the feasibility of using liquid gallium as an internal coolant in cutting inserts to effectively remove thermal energy.

Robots bring eventful impacts to the workplace, benefiting from the advantages of implementing any technology should be based on the premise of safety. This work proposes a systematic method to establish a postprocessor module for any specified 6R articulated robot. Instead of obsessively emphasizing achieving the desired locations and orientations by hand guiding grab and dragging a robotic arm for operation in teaching mode, the significant end-effector poses are calculated to form a new path for the joints efficiently in this study. Since robotic motion control is usually a complex system whose users must be well trained and acquainted with using them. There is a need for a GUI solution that can provide intuitive robotic motion control on the current location by the user independently, easy setup, arrangement, adjustment, and monitoring robot motion tasks. The proposed system simplifies the interaction between the technician and the industrial robotic arm in the case of robotic motion control and tracking at a distant location. The presented method is fully adapted to alternate between joint angles and end-effector poses on a graphic user interface system. After developing the capabilities of the solver, a functional postprocessor is programmed inside the proposed GUI system. Examples with specified posture and predefined movements are demonstrated for corroborating the algorithm. The results show considerable efficiency and reliability in task planning and are fully supported for automatic path generation.

The aim of this paper is to study the mechanical behavior of corrugated board boxes, focusing attention on the strength that the boxes are able to offer in compression under stacking conditions. A preliminary design of the corrugated cardboard structures starting from the definition of each individual layer, namely the outer liners and the innermost flute, was carried out. For this purpose, three distinct types of corrugated board structures that include flutes with different characteristics, namely the high wave (C), the medium wave (B), and even the micro-wave (E), were comparatively evaluated. More specifically, the comparison is able to show the potential of the micro-wave which would eventually allow a significant saving of cellulose in the fabrication process of the boxes, thus reducing the manufacturing costs and causing a lower environmental footprint. First, experimental tests were carried out to determine the mechanical properties of the different layers of the corrugated board structures. Tensile tests were performed on samples extracted from the paper reels used as base material for the manufacturing of the liners and flutes. Instead, the edge crush test (ECT) and box compression test (BCT) were directly performed on the corrugated cardboard structures. Secondly, a parametric finite element (FE) model to allow, on a comparative basis, the study of the mechanical response of the three different types of corrugated cardboard structures was developed. Lastly, a comparison between the available experimental results and the outputs of the FE model was carried out, with the same model being also adapted to evaluate additional structures where the E micro-wave was usefully combined with the B or C wave in a double-wave configuration.

Bio-based polymers are a class of polymers made by living organisms, a few of them known and commercialized yet. Due to poor mechanical strength and economic constraints, they have not yet seen the extensive application. Instead, they have been an appropriate candidate for biological applications. Growing consumer knowledge of the environmental effect of polymers generated from petrochemical sources and a worldwide transition away from plastics with a lifespan of hundreds of years has resulted in greater interest in such hitherto unattainable sectors. Bio-based polymers come in various forms, including direct or "drop-in" replacements for their petrochemical counterparts with nearly identical properties or completely novel polymers that were previously unavailable, such as polylactide. Few of these bio-based polymers offer significantly improved technical specifications than their alternatives. Polylactic acid (PLA) has been well known in the last decade as a biodegradable thermoplastic source for use in 3DP by the "fused deposition modeling" method. The PLA market is anticipated to accomplish 5.2 billion US dollars in 2020 for its industrial usage. Conversely, 3DP is one of the emerging technologies with immense economic potential in numerous sectors where PLA is one of the critical options as the polymer source due to its environmentally friendly nature, glossiness, multicolor appearance, and ease of printing. The chemical structure, manufacturing techniques, standard features, and current market situation of PLA were examined in this study. This review looks at the process of 3DP that uses PLA filaments in extrusion-based 3DP technologies in particular. Several recent articles describing 3D-printed PLA items have been highlighted.

Finishing machining of Ti6Al4V, known for its high strength and heat conduction resistance, demands optimisation to achieve high-quality end products. This study explores modifying the chip contact length on the rake face and altering the flank face with a cavity to minimise process forces and temperatures while maintaining cutting edge integrity. The research validates the manufacturability of ultra-short pulsed laser-ablated tool geometry modifications, indicating potential for industrial scale-up. Extensive experimental evaluations under dry conditions assess the impact of tool modifications at various feed rates for planing and turning. Significant reductions in process forces and temperatures were observed with rake face modifications, particularly at a cavity distance of approximately 34 µm. Ideal performance was noted for feed rates between 0.035 and 0.045 mm for planing and 0.040 to 0.045 mm/rev for turning. Smoothed Particle Hydrodynamics (SPH) simulations employing a Johnson-Cook material model were used to analyse chip formation and to predict the process forces. These simulations revealed a clear change in the chip formation and lower process forces and temperatures. The SPH results closely matched experimental outcomes, with a discrepancy of less than 7 % in cutting forces for both tool types, although feed forces were underestimated by about 50 %. The effect of the tool modification is reflected accurately at the respective feeds.