Set Of Process Parameters Research Articles

Effect of laser scanning angle and atmospheric oxygen on mechanical properties and microstructural morphology of selective laser-sintered aluminum-filled polyamide monolayers

The process parameters on selective laser sintering (SLS) have a strong effect on part quality. To better understand SLS of composite materials, this study focuses on the influence of laser scan angle and oxygen gas interaction on the mechanical properties, morphology, and joining mechanisms of laser-sintered aluminum-filled polyamide-12 single-layer specimens at room temperature. The results show that the strength and ductility increase with a higher laser scan angle (e.g., near 60°), and the oxygen concentration in the chamber shows a small effect on the elongation. No changes in the morphology and internal structure of the specimens were observed under a different set of process parameters. Additionally, two failure mechanisms were observed; a ductile failure that occurs when particles are well-blended where the metallic particles work effectively hindering crack propagations, and a brittle failure when local amounts of metallic particles are low.

Response Surface Modelling and Effective Application of Adaptive Neuro-Fuzzy Inference System to Analyze Surface Roughness of Al/Gr/Cp5 MMC Machined using WEDM

ABSTRACT This paper presents an investigation of wire electrical discharge machining (WEDM) of new fabricated aluminium base composite with 5% graphite by weight, i.e., Al/Gr/Cp5. The new fabricated metal matrix composite (MMC) has a complete application in space, defence, automobile, and other concerned metal industries. In this work, adaptive neuro-fuzzy inference system (ANFIS) and the response surface method (RSM) were selected for the analysis and modelling purposes. ANFIS is a soft computing technique used in nonlinear data training. Taguchi’s L27 plan was employed to conduct the experiments. The process parameters such as pulse-on time (PON), pulse-off time (POFF), a feed rate of wire (FRW) and the input current (IC) were selected for the experimentation. Based on the experimental findings, PON is identified as the most dominant parameter and the input current. WEDM performance was measured in terms of surface roughness (SR). For the optimised (minimum) surface roughness of 1.8625 microns, the best set of process parameters was PON (108 μs), POFF (52 μs), FRW (4 m/min) and the input current (12 Amp). ANFIS-predicted results were compared with the experimental results. An acceptable agreement is observed with the high value of the correlation coefficient (R2 = 0.9624). The soft computing technique, like ANFIS-RSM, shows a good competency and reliability for the presented work.

Multi-variable Optimization of the Shot Blasting of Additively Manufactured AlSi10Mg Plates for Surface Roughness Using Response Surface Methodology

Direct metal laser sintering (DMLS) technique of additive manufacturing has been proven to be the best technology for printing metallic components with higher density and better mechanical properties. However, due to higher surface roughness of the as-built metallic components, post-processing through shot blasting is the most recommended method for improving surface finish. In the present work, the optimum shot blasting conditions were explored for achieving desired surface roughness for the DMLS printed AlSi10Mg aluminum alloy plates. Three input parameters (viz., time, pressure and grit size) selected at three levels were used to formulate the design matrix. Consequently, 30 real-time shot blasting experiments were performed, and the results obtained as surface roughness were used to develop the quadratic model using response surface methodology. Further, the prediction effectiveness was verified using analysis of variance. The experimental results revealed that the effect of blasting pressure on the surface roughness was found to be positive and most significant (41.89%), followed by the negative influence of time of shot blasting (29.29%) and grit size of shots (23.71%). The predicted values of surface roughness (maximum and minimum) obtained at optimum set of process parameters were found to be in agreement (± 6%) with the experimentally measured data. The variations in surface roughness were further validated through scanning electron microscopy. The proposed mathematical model is effective, precise and promising for the prediction of surface roughness in the shot-blasted surfaces of additively manufactured aluminum alloys.

Optimization Of Wear Behaviour Of LM26-Gr Composite Using Taguchi Based Grey Relational Analysis

The progression in present manufacturing technology created the need of developing new materials for superior wear resistance. The objective of this paper is to optimize the three process parameters wear loss, wear rate, specific wear at three different levels with Taguchi technique in L9 Orthogonal array. A multi-response optimization technique Grey relational analysis is used to obtain the single process parameter setting for both the responses. LM26 metal matrix composite is fabricated by stir casting technique with Almandine garnet particles as reinforcement in different weight percentages with two different particle sizes. Analysis of Variance (ANOVA) was conducted to recognize the prevalent factor and found all the three factors as being critical. The above process was validated by Linear regression technique after conformation tests has been performed.

Extended TODIM method based on normal wiggly hesitant fuzzy sets for deducing optimal reinforcement condition of agro-waste fibers for green product development

Green composites from agro-waste have gained exponential interests within the scientific domain owing to the increasing environmental concerns associated with the burning of crop residue. Such composites are being designed to produce green products (particleboards in the present case) that addresses the challenges confronted by the agricultural sector, meeting the technical, economic and environmental sustainability goals. However, there are challenges associated with the identification of optimum processing parameters to obtain the suitable green composites from agro-waste in accordance with the industrial requirements. The present work therefore aims to investigate the performance of bio-epoxy based composite particleboards derived using various weight percentages of punica-granatum agro waste treated with aqueous solution sodium bicarbonate for different time duration. To achieve the aforementioned objective, a novel decision making framework based on prospect theory has been proposed that makes decision under hesitant fuzzy environment by modeling the experts’ preferences using Normal Wiggly Hesitant Fuzzy Sets (NWHFS). Proposed decision making framework investigates to identify the optimal set of process parameters (wt% and time duration of chemical treatment) to achieve the best particleboard that meets optimally the technical, environmental and economic requirements. It has been revealed through the application of the proposed decision making framework that the green composite particleboards fabricated with 30% agro-waste and 5 days chemical treatment have the best achievable technical, environmental and economic indicators. The validity, correctness and the robustness of the rankings have been revealed through the sensitivity analysis approach and solving past case studies. The case under consideration has also been solved by other extant fuzzy TODIM approaches. Henceforth, the proposed decision making framework tends to provide the designers a tool to deduce the set of optimum parameters for the green composites from agro-waste that can be employed to explore the possibilities of designing green products and therefore aid in meeting the sustainable goals.

Employing a Low-Cost Desktop 3D Printer: Challenges, and How to Overcome Them by Tuning Key Process Parameters

3D printing has been referred as being the third industrial revolution and was introduced as an innovative fabrication method in order to overcome shortcomings of existing prototyping fabrication techniques. This method involved the forthright fabrication of three-dimensional solid objects derived from a digital CAD file. The advancements in additive manufacturing technology in combination with the expiration of several patents led to the dramatic reduction of 3D printing equipment purchasing and usage costs thus making the technology available to wide masses. However, due to the modus operandi of the additive process, that functions in a layer to layer building principle, technical expertise is needed to properly operate such devices. Therefore, several parameters have to be carefully tuned and a certain level of expertise is required in order to accomplish the desired printing results. In addition, the newly introduced FFF lower-end desktop 3D printers suffer more from these disadvantages and therefore limit users to intermediate or unsatisfactory printing results. This article looks into 3D printing challenges while stressing the contribution of key process parameters in printing results. Different process parameter settings are tested, depicting the differences in printing quality, while optimal parameter settings are being proposed.

Data-driven Prediction of Temperature Evolution in Metallic Additive Manufacturing Process

In this study, a data-driven deep learning model for fast and accurate prediction of temperature evolution and melting pool size of metallic additive manufacturing processes are developed. The study focuses on bulk experiments of the M4 high-speed steel material powder manufactured by Direct Energy Deposition. Under non-optimized process parameters, many deposited layers (above 30) generate large changes of microstructure through the sample depth caused by the high sensitivity of the cladding material on the thermal history. A 2D finite element analysis (FEA) of the bulk sample, validated in a previous study by experimental measurements, is able to achieve numerical data defining the temperature field evolution under different process settings. A Feed-forward neural networks (FFNN) approach is trained to reproduce the temperature fields generated from FEA. Hence, the trained FFNN is used to predict the history of the temperature fields for new process parameter sets not included in the initial dataset. Besides the input energy, nodal coordinates, and time, five additional features relating layer number, laser location, and distance from the laser to sampling point are considered to enhance prediction accuracy. The results indicate that the temperature evolution is predicted well by the FFNN with an accuracy of 99% within 12 seconds.

Evaluation of mechanical property predictions of refill Friction Stir Spot Welding joints via machine learning regression analyses on DoE data

The high-potential of lightweight components consisting of similar or dissimilar materials can be exploited by Solid-State Joining techniques. Whereas defects such as pores and hot cracking are often an issue in fusion-based joining processes, via solid-state joining processes they can be avoided to enable high-quality welds. To define an optimal process window for obtaining anticipated joint properties, numerous time and cost consuming experiments are usually required. Building a predictive model based on regression analysis enables the identification and quantification of process-property relationships. On the one hand, mechanical property and performance predictions based on specific process parameters are needed, on the other hand, inverse determination of required process parameters for reaching desired properties or performances are demanded. If these relations are obtained, optimized process parameter sets can be identified while vast numbers of required experiments can be reduced, as underlying physical mechanisms are utilized. In this study, different regression analysis algorithms, such as linear regression, decision trees and random forests, are applied to the refill Friction Stir Spot Welding process for establishing correlations between process parameters and joint properties. Experimental data sets used for training and testing are based on a Box-Behnken Design of Experiments (DoE) and additional test experiments, respectively. The machine-learning based regression analyses are benchmarked against linear regression and DoE statistics. The results illustrate a decryption of relationships along the process-property chain and its deployment to predict mechanical properties governed by process parameters.

Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method

The product quality, productivity, and cost are mainly considered to make the manufacturing plan in plastic injection molding (PIM). The process parameters in PIM play a crucial role in determining the product quality, productivity, and cost. There are actually contradictions between above three properties. Therefore, it is difficult to quickly and accurately obtain the process parameters setting that meet the product quality requirement under the premise of acceptable productivity and cost. In this paper, a differential sensitivity fusion method (DSFM) is proposed to perform the multi-objective optimization of process parameters in PIM for the product quality and productivity improvement and the cost-saving, which integrates sampling strategy, numerical simulation, metamodeling method, and multi-objective optimization algorithm. The sampling strategy is utilized to generate sampling points from the design space at different parameter levels. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, the metamodeling method is applied to construct the response predictors to calculate the objective responses for any sampling point in the global design space. The multi-objective optimization algorithm is executed to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. The automobile front bumper is taken as the case study to verify the proposed method. The numerical results demonstrate that the proposed metamodeling method has better prediction accuracy and performance compared to some classical methods (e.g., response surface model, Kriging) and the multiple objectives cannot reach the optimal simultaneously. Moreover, the trade-off analysis identifies the better solution for decision-making, which helps to quickly and effectively select the optimal process parameters setting.

Development of an intelligent decision model for non-traditional machining processes

In order to fulfil the ever increasing requirements of various hard and difficult-to-machine materials in automobile, turbine, nuclear, aviation, tool and die making industries, the conventional material removal processes are now being continuously substituted by an array of non-traditional machining (NTM) processes. The efficient and improved capabilities of these NTM processes have made them indispensible for the present day manufacturing industries. While deploying a particular NTM process for a specific machining application, the concerned process engineer must be aware of its capability which is influenced by a large number of controllable parameters. In this paper, an intelligent decision model is designed and developed in VBASIC to guide the concerned process engineer to have an idea about the values of various NTM process responses for a given parametric combination. It would also advise about the tentative settings of different NTM process parameters for achieving a set of target response values. The operational procedure of this developed system is demonstrated with the help of three real time examples.

Influence of Processing Parameters on Surface Properties of SKD61 Steel Processed by Powder Mixed Electrical Discharge Machining

Many studies have carried out on the characteristics of surfaces processed by PMEDM with various powders. However, limited works have used the tungsten carbide powder in the PMEDM process and investigated its effects on the surface properties. In this research, the influence of main process parameters, including the peak current (Ip), the pulse on time (Ton), and the powder concentration (Cp) on surface properties—i.e., surface roughness (Ra), microhardness of surfaces (HV), and surface morphology of SKD61 steel machined by PMEDM with tungsten carbide powder, was explored in two modes: the fine-finish mode and the semi-finish mode. The results show that the peak current, the pulse on time, and the powder concentration have a noticeable influence on surface properties. The surface roughness, the microhardness of surfaces, and the surface morphology at the small peak current (Ip =1 A) and the short pulse on time (Ton =16 µs) were improved better than those at the large peak current (Ip=4 A) and the long pulse on time (Ton =200 µs) with all powder concentrations. The best improvement of the surface roughness at Ip=1 A; Ton=16 μs; Cp=40 g/l is 0.471 ± 0.011µm with a reduction of 57.984% as compared to the normal EDM. The set of processing parameters {Ip=1 A; Ton=16 μs; Cp=60 g/l} has the most positive effect on the improvement of microhardness and surface morphology: The microhardness was enhanced up to 825 ± 19 HV with an increase of 129.167% as compared to the normal EDM. The surface morphology had the smooth surface, the few micro-cracks, the few voids, the few droplets, and the few globules of debris as compared to that of other process parameters and the normal EDM.

A comparison of bending-fatigue properties of surface-induction-hardened SAE 1045 bar steels with and without vanadium and the influence of comparable low-temperature induction-tempering and furnace-tempering treatments

Abstract Specimens of SAE 1045 steels with and without 0.10 wt pct of vanadium were surface-induction hardened with the same set of processing parameters. Fully martensitic microstructures formed from austenitic microstructures with a common grain size of 10 μm. Specimens were induction tempered at 270 °C for 5 s or at 220 °C for 4 min; a third set of specimens was furnace tempered at 215 °C for 10 h. Near-surface hardness of tempered specimens was between 586 and 620 Vickers hardness (about 55 Rockwell-C hardness), with statistically higher values for the 10V45 steel. Axial residual stresses in the range of -690 to -796 MPa were measured at specimen surfaces, and similar average values were reported for both steels. Analyses revealed that the 5-s/270-oC condition was the most highly tempered, and the least tempered was the 4-min/220-oC condition. Load-controlled, fully reversed, cantilever bending-fatigue tests were conducted with notched specimens. Fatigue failures were initiated at multiple sites at notched surfaces in association with near-surface oxide inclusions. Endurance limits ranged from 470 to 605 MPa, with similar average values for both steels. Ratios of adjusted endurance limit to calculated ultimate tensile strength were approximately 0.35. A trend of increased endurance limit with a decrease in the degree of tempering was established for 1045 steel, but not for 10V45 steel. The least-tempered 4-min/220-oC condition of 1045 steel exhibited an unexpectedly high endurance limit of 605 MPa, a result ascribed largely to the higher surface residual stress for this condition. Similarly superior performance in the 10V45 steel for this tempering condition was not observed. The latter outcome was attributed to embrittlement at prior austenite grain boundaries owing to a higher level of phosphorous.

Comparison of the EHLA and LPBF Process in Context of New Alloy Design Methods for LPBF

Additive Manufacturing (AM) processes are becoming more and more important for production of parts with increasing geometrical complexity and functionality. However, to draw on the full potential of AM technologies, alloys that exploit process inherent particularities such as extremely high cooling rates (ca. 106 K/s) have to be developed. One of most important AM-processes is Laser Powder Bed Fusion (LPBF), a batch-wise process. This complicates experimental alloy development and increases the use of powder resources since only one chemical composition can be tested within one test job and the process chamber has to be cleaned carefully in between. The process Extreme High-Speed Laser Material Deposition (EHLA) has been found to have similar cooling rates as LPBF, however it uses an in situ supply of powders which allows an easy switching between materials and has potential for rapid alloy development methods. Since the mechanical properties of materials primarily depend on chemical composition and microstructure, which in turn depends heavily on the cooling rates in the production process, the EHLA-process could be used as a means for an accelerated alloy development for LPBF. However, to explore this possibility, a thorough comparison of the two processes has to be performed.In this work, EHLA and LPBF processes are compared and evaluated regarding the following characteristics: process parameters, laser intensities and volume energy densities, resulting microstructure (primary dendrite arm spacing, DAS), melt pool size and shape. The reference samples were manufactured using one set of LPBF process parameters and EHLA samples were manufactured using three different sets of process parameters.The volume energy densities Ev [J/mm³] of the processes were found to differ by a factor 2.4 with higher Ev observed in LPBF. However, considering that approximately 2 to 3 layers are remelted with each pass of the laser beam, the introduced Ev per pass approximates the Ev introduced in the EHLA process. The melt pool size as seen in a cross section in the EHLA-manufactured samples is approximately 25 times larger than in the LPBF-manufactured samples and its depth to width ratio (d/w ratio) can be attributed to a heat conduction welding process while the d/w ratio observed in the LPBF-manufactured sample suggests a transition process between heat conduction welding and deep welding. The observed DAS is in the same order of magnitude for both processes ranging from 0.55 to 1.15 µm in EHLA-manufactured samples and 0.73 µm in the LPBF-manufactured reference sample. Since the resulting microstructures of samples manufactured with both processes show this common feature and EHLA process parameters can be adjusted to control the cooling rates, the transferability between EHLA- and LPBF-processes is supported in this first investigation. Research for a more efficient alloy development for LPBF using EHLA will be continued by e.g. examining chemical compositions and performing mechanical testing.

Numerical investigation of the pouring mechanism in the filling of epoxy resin in pressure casting processes

Epoxy resins are one of the most widely used thermosetting materials, especially for transformer insulation in the electrical industry. One of the primary technologies used for manufacturing such products is based on pressure casting. Computational fluid dynamics (CFD) calculations of the mould filling stage in a pressure casting process for epoxy resin casting are carried out and presented in this paper. Two designs of a gating system are studied and their effects on the flow parameters in the mould are analyzed. The two designs being top pour and bottom pour inlet. A new solver is developed in an open source CFD framework, OpenFOAM, which incorporates the Navier–Stokes equations, energy equation including the viscous dissipation and a new isoAdvector approach for capturing the liquid-gas interface. The objective is to simulate the flow and heat transfer of epoxy resin, while being filled in the mould that is initially empty and heated to a specified temperature. Formulation was validated with experimental measurements of the classical dam breaking problem. For the resin filling problem, further verification was carried out using grid-dependent studies and temporal fill factor comparisons to experiments. In addition, instantaneous contours of volume fraction and temperature, and velocity vectors are studied to understand the flow patterns and heat transfer in the mould during the filling process. Among both the gating designs, the bottom pour was the more preferred one as it resulted in less turbulence, sloshing and it provides an easy passage for the voids to escape leading to higher fill factor. This analysis also helps in recognising the location of voids and thereby help in potentially identifying optimal process parameter settings for a more suitable mould design.

Processing of IN718-SS316L bimetallic-structure using laser powder bed fusion technique

ABSTRACT A crack-free Inconel 718 – Stainless steel 316 L bimetallic structure has been fabricated using the laser powder bed fusion (LPBF) process. Firstly, an optimum process parameter set resulting in defect-free bonding of both materials has been predicted by carrying out a parametric study. Thereafter, microstructural and mechanical characterization such as optical microscopy, X-ray diffraction, scanning electron microscopy with energy-dispersive X-ray spectroscopy, hardness and tensile testing have been carried out to investigate the characteristics of the bimetallic interface. The porosity quantification shows a density greater than 99.5% with no observable cracks or porosity at the transition zone. The secondary electron micrographs reveal a small inter-diffusion region at the interface. The interface and the nearby region consist of both cellular and columnar grains. The tensile strength of the bimetallic-structure approaches the strength of stainless steel 316 L. Even with the material discontinuity, the fracture happens at the weaker side (stainless steel 316 L) indicating the strong strength of the dissimilar interface. The fractography of the fractured surface displays a typical ductile cup and cone type of fracture. The results strongly suggest the suitability of the LPBF process for fabricating defect-free high-strength IN718-SS316L bimetallic structures.

Research on Magnetic-Vibration Combination Treatment Based on Multifield Coupled Finite Element Analysis

Abstract In this paper, the three-dimensional simulation modeling of the magnetic-vibration combination residual stress reduction device was established based on the multifield coupled finite element analysis, and the residual stress reduction method of magnetic-vibration combination treatment was researched. The placement mode of the electromagnetic device and the factors affecting the effect of the magnetic-vibration combination reduction were comprehensively studied. The results showed that when the electromagnetic device was placed vertically, it was more beneficial to reduce the residual stress. The electromagnetic frequency was the maximal factor to affect the magnetic-vibration combination, and the factors that followed were voltage, material performance, exciting force, and exciting frequency. The greater the electrical conductivity of the material was, the lower the magnetic induction intensity was. The thicker the steel plate was, the easier it was to magnetize, but it was not conducive to vibration, and there was an optimal thickness under a certain set of parameters. Applying tension to the steel plate was beneficial to increase magnetization to reduce stress, and the aforementioned results of the simulation were consistent with the experimental results. The simulation study in this paper provided theoretical support for the process parameter setting of magnetic-vibration combination treatment to reduce residual stress.

Finite element simulation and integration of CEM43 °C and Arrhenius Models for ultrasonic-assisted skull bone grinding: A thermal dose model

The aim of the study was to develop a novel automated setup for bone grinding to limit the temperature to below 43 °C. The feasibility of using ultrasonic actuation during bone osteotomy was explored with different machining variables, such as rotational speed, feed rate and ultrasonic frequency, in terms of the criterion variable (i.e., temperature). A thermal dose model based on the CEM43 °C and the Arrhenius model was developed for the prediction of tissue damage during bone grinding. CEM43 °C is a normalizing method to convert the time-temperature relationship into an equivalent number of minutes at 43 °C. For every degree rise in temperature above 43 °C, the cell viability significantly increased. The temperature generated during bone grinding was measured with an infrared thermography technique. The increase in temperature above threshold levels of 43 °C and 47 °C may harm the bone tissues and cause thermogenesis and osteonecrosis, respectively. A finite-element simulation was conducted to visualise the spatial and temporal distribution of temperature on the bone surface after bone grinding. Furthermore, simulation results were used to measure the depth of thermogenesis and osteonecrosis at the grinding site. Evaluation of the optimised set of bone grinding process parameters was supported with analysis of variance at the 95% confidence level.

Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach

A numerical simulation approach is proposed to predict the optimal parameter setting during high pressure die casting. The contribution from the optimal parameters, the temperature, showed more influence on the casting quality than the other parameters. This study’s outcome was beneficial for finding the solution for casting defects that occurs due to incorrect setting of process parameters in die casting. Thus, a combination of numerical optimisation techniques and casting simulation serves as a tool to improve the casting product quality in die casting industries. This paper aims to analyse and optimise critical parameters like injection pressure, molten metal temperature, holding time, and plunger velocity, contributing to the defects. In this research paper, an effort has been made to give optimal pressure, temperature, holding time, and plunger velocity parameters using ProCAST simulation software that uses finite element analysis technology. Numerical analysis for optimising the parameters by varying the temperature of molten metal, injection pressure, holding time, and plunger velocity, concerning solidification time at hot spots, is an essential parameter for studying the defect analysis in the simulated model.

Modelling and experimental observation of the deposition geometry and microstructure evolution of aluminum alloy fabricated by wire-arc additive manufacturing

In this paper, a model composed of a macroscopic finite element (FE) model coupled with a microscopic phase field (PF) model was constructed to investigate the Al alloy microstructure evolution during wire arc additive manufacturing (WAAM). First, the relationships between single layer process parameters and the resulting deposition geometries (width and height) were investigated. A three-dimensional FE model was built to calculate the thermal distribution and temperature gradient under different process parameters. These results were then fed into a PF model to obtain the microstructure corresponding to each set of process parameters. A bridge was constructed by this method, starting from the WAAM process parameters and yielding the microstructure throughout solidification. The simulated results showed that the primary dendrite arm spacing (PDAS) decreased slightly with current increases, but the substrate moving speed had a more obvious impact on PDAS, which rose significantly with increased substrate speed. Finally, metallographic examination and energy dispersive spectroscopy tests were conducted on specimens fabricated by WAAM with different process parameters, to verify the simulated PF results. The simulated morphology and size of dendrites were in good agreement with experimental results.

Correlative cross-sectional characterization of nitrided, carburized and shot-peened steels: synchrotron micro-X-ray diffraction analysis of stress, microstructure and phase gradients

Mechanical properties of case modified steels depend decisively on the near-surface gradients of residual stresses, microstructures, phases and chemical composition, which are generated by the empirically well-established case-hardening techniques. Currently, however, to obtain the correlation between near-surface structure–property gradients, applied hardening process parameters and steels’ overall performance is a very challenging task. In this work, high-energy synchrotron cross-sectional X-ray diffraction (CSmicroXRD) using a pencil beam cross-section of 20 × 500 μm2 and complementary analytical techniques are used to characterize the surface-to-bulk gradient of (i) a plasma nitrided steel W300, (ii) a carburized case hardening steel (grade 18CrNiMo7-6) and (iii) a shot-peened high strength steel, type 300M. CSmicroXRD analysis reveals complex gradients of martensite and austenite phases, residual stresses in both phases, crystallographic texture and the evolution of diffraction peak broadening with a spatial resolution of ~20 μm. These parameters are correlated with the gradients of hardness, morphology-microstructure and with the changes in N and C concentrations and/or retained austenite formation/depletion in all three model samples. Finally, the correlative micro-analytics approach indicates the complexity of near surface structure-property relationships as well as the importance of innovative cross-sectional characterization, which allows for assessing gradual near-surface physical and/or chemical changes accompanying thermo-chemical and mechanical surface treatments.