Optimum Values Of Process Parameters Research Articles

Optimizing SLM process parameters using FE analysis for distortion mitigation

The investigation aimed to determine the optimal value of process parameters to minimize distortion while reducing the overall printing time. To achieve this, different combinations of process parameters are selected, and 25 sets of FE experiments are prepared using an orthogonal matrix. The coupled thermomechanical analysis with the element birth technique is used to mimic the behavior of Selective Laser Melting (SLM). Initially, a mesh convergence study is performed to reduce computation time with the least sacrifice in results. To minimize the temperature gradient, simulations are performed at different levels of bed temperature ranging from 303 K to 843 K. The minimum distortion was observed for a higher bed temperature of 843 K, as at this temperature, the gradient in temperature is minimum, resulting in the least development of residual stresses. The interactive behavior between laser power and scanning speed is studied, revealing an approximately linear trend with respect to distortion while keeping all other parameters constant. Additionally, it was observed that laser power and layer thickness also exhibited a similar linear trend with respect to distortion. To identify the optimal process parameter value within the design space, the Multi-Island Genetic Algorithms (MIGA) technique is employed, leading to a remarkable minimum distortion of 0.45 mm for a specific set of input process parameters.

Влияние высокоэнергетического воздействия при плазменной резке на структуру и свойства поверхностных слоёв алюминиевых и титановых сплавов

Introduction. Plasma cutting of various metals and alloys is one of the most productive processes for obtaining workpieces, especially when using reverse polarity plasmatrons. The use of plasma cutting in the production of workpieces of large thicknesses potentially allows to increase the productivity of the process. In the domestic industry plasma cutting equipment of foreign production is widely used, which poses the problem of import substitution of manufactured products and equipment with the corresponding parts of Russian companies. For this reason, at present the Institute of Strength Physics and Materials Science together with the company "ITS Siberia" develops plasma cutting equipment on reverse polarity currents. At the same time, in order to determine the peculiarities of influence of parameters and modes of plasma cutting process on the structure of metal in the cutting zone, it is necessary to conduct comparative studies on different metals and alloys. Aim of the work: is to identify the characteristics of the influence of high energy impact on the structure and properties of surface layers of aluminum and titanium alloys during plasma cutting using a plasma torch operating with reverse polarity currents. The research methods are optical metallography, microhardness measurement and laser scanning microscopy of the surface after plasma cutting. Results and discussions. The conducted researches show a wide range of possibilities to adjust the process parameters of plasma cutting of aluminum alloys AA5056 and AA2124, and titanium alloy Grade2. For the alloys used in this work there are optimal values of process parameters, deviations from which lead to various violations of cut quality. Aluminum alloys show a tendency to significant de-strengthening in the cutting zone, which is associated with the formation of a large crystalline structure and large incoherent secondary phases with simultaneous depletion of the solid solution with alloying elements. Titanium alloys are characterized by quenching effects in the cutting zone with increasing microhardness values. Oxides are also formed in the surface layers despite the use of nitrogen shielding gas. In the alloy Ti-4Al-1Mn, in the previously conducted works, the formation of oxide films with high hardness is not noted, while in the Grade2 alloy at cutting in the surface layers oxides are formed sharply increasing the values of microhardness of the material up to values of about 15 GPa. This situation can complicate mechanical processing of titanium alloys after plasma cutting. The obtained results indicate a rather low value of the allowance for further machining after plasma cutting of aluminum and titanium alloys.

The Relevance of Process Parameter Optimization and Geometric Figure for Direct Laser Deposition of Inconel 738 Alloy and Its Theoretical Modeling

In order to minimize the gaps between the direct laser deposition channels and improve the quality and performance of the formed parts, the process of direct laser deposition is utilized in laser additive manufacturing to create sequential, single- and double-channel deposition layers on 304 stainless steel plates. Under the premise of keeping the layer rate and defocusing amount unchanged, this study investigates the effects of laser power, scanning speed, and powder feeding rate on the morphology and inclusions of single- and double-channel deposited layers. The aim is to determine the optimal process parameter values for direct laser deposition of single-layer, single-channel Inconel 738. The effects of the three process parameters on the response values were investigated using a multi-factor, multi-level experimental design. The evaluation indexes for the analysis included the deposited layer wetting angle and aspect ratio. The analysis involved one-way extreme analysis and ANOVA analysis. The optimal process parameters are a laser power of 550~750 W, a scanning speed of 7~13 mm/s, and the powder feeding rate was 2.1~4.33 g/min. At the same time, the relationship between surface tension and gravity was integrated with the spherical coronal model and Young’s equation to develop a mathematical model of the direct laser deposition process at a theoretical level. The mathematical model of the direct laser deposition process was utilized to analyze the correlation between the geometric parameters of the cross-section of the deposited layer. This analysis provides a valuable data reference for future Inconel 738 direct laser deposition.

Analysis of the Effect of the Elongation Operation on the Welding of Internal Metallurgical Discontinuities.

This article discusses the results of our research into the effect of elongation on the welding of internal metallurgical discontinuities for two different geometrical shapes of a model feedstock of a selected magnesium alloy. Model discontinuities, specifically those of the metallurgical void type, were placed in various local zones of the modelled feedstock to check the influence of their location on their welding. The numerical modelling was carried out using the Forge®NxT2.1 application based on the finite element method. The results of the numerical tests were verified in laboratory conditions using the Gleeble simulator of metallurgical processes. Based on this research, it was found that the geometric shape of the feedstock material and the location of internal metallurgical discontinuities have a significant impact on the welding of discontinuities. The optimal values of the main process parameters of the elongation operation in flat dies were also determined for use in individual forging stages in order to eliminate internal metallurgical discontinuities. On the basis of the numerical studies carried out and their verification under laboratory conditions, it was concluded that a relative draft equal to 35% should be applied to weld the metallurgical discontinuities, which would result in a favorable hydrostatic pressure distribution within the discontinuities.

Multiple criteria decision support analysis for manufacturing process parameters selection of metallic bipolar plates for polymer electrolyte membrane fuel cells

The multi-criteria decision-making method, along with experimental and finite-element (FE) methods, was employed to determine the optimal process parameter values for rubber pad forming of metallic bipolar plates. To achieve this aim, the rubber pad forming process was modeled by FE software, and the results were validated by conducting experimental tests. Three significant rubber pad forming parameters—punch force, hardness, and the thickness of the rubber layer—were considered as input variables. A full-factorial design involving 27 experiments was used to assess the alternatives. Filling percentage, thinning ratio, and stress were considered as distinct evaluation criteria. To solve the multi-criteria decision-making problem, a hybrid approach was used to weigh the alternatives. According to the results, the applied method was capable of selecting the optimal values of process parameters while considering different criteria simultaneously. The results demonstrated that a punch force of 50 kN, rubber hardness of 55 Shore A, and rubber thickness of 3.5 mm were the best processing conditions. For these parameters, the filling percentage, thinning ratio, and stress were 97.6%, 28.5%, and 1005 MPa, respectively.

Comparative investigation on wear properties of 3D-printed textured journal bearings

In this experiment study, the textured journal bearings (TJB) are developed by additive manufacturing process with three different polymers such as acrylonitrile butadiene styrene (ABS), poly-lactic acid (PLA), and nylon. The experimental analysis of these developed 3D-printed TJB is performed based on the response surface methodology based on Box-Behnken design (BBD) matrix. The process parameters such as texture depth (TD), rotor speed (S) and load (L) are considered as input variable. The wear properties of 3D-printed TJB such as wear rate (WR) and rubbing temperature (RT) are output responses that are evaluated by the experimental study. Further, the grey relational analysis (GRA) technique is considered to assess the optimum value of process parameters for each polymer (ABS, PLA and Nylon). These results show that the rotor speed is the most significant process parameter for the better wear properties of 3D-printed TJB followed by load. The Nylon is found to be the most significant polymer for 3D-printed TJB among all the polymers. For the Nylon polymer, the optimal value of texture depth, speed and load are 0.5 mm, 500 rpm and 10 N respectively. Under this optimal condition, the wear rate and rubbing temperature of 3D printed TJB are 0.000118 mg/s and 51.60 °C respectively. Moreover, the thermal deformation in 3D-printed TJB is observed for PLA polymer. However, the rubbing temperature of 3D-printed TJB reduced with increased texture depth.

Experimental investigation of tribological performance of 3D printed textured journal bearings for various polymers

In this study, the 3D-printed textured journal bearings (TJBs) are developed by fused deposition modelling (FDM) process with three different polymers such as ABS, PLA and nylon. For the study, the process parameters such as texture depth (TD), rotor speed (S) and load (L) are considered as input parameters. The experimental analysis of 3D-printed TJB is performed based on the response surface methodology (RSM). With the RSM technique, the influence of these input parameters on the tribological performance of 3D-printed TJBs. The tribological performance of 3D-printed TJBs are wear resistance (WR) and wear temperature (WT). Further, the GRA analysis is performed to evaluate the optimum value of these process parameters for ABS, PLA and nylon polymer. These results demonstrate that the wear resistance (WR) of TJBs is first increases with the texture depth and then starts decreasing. Among all the polymers, the ABS polymer is the most significant, and nylon is the least significant polymer for the 3D-printed TJBs. The maximum WR (i.e. 76.576 m/mm3) of 3D-printed TJBs is observed for the PLA polymer. Whereas, nylon provides the least WR (i.e. 9.572 m/mm3) of 3D-printed TJBs. At the optimal value of process parameters (TD = 1.5 mm, S = 500 r/min and L = 10N), the WR and WT of 3D-printed TJBs (for ABS polymer) are 62.126 m/mm3 and 323.75 K respectively.

Optimization of Process Parameters in Laser Powder Bed Fusion of SS 316L Parts Using Artificial Neural Networks

Additive manufacturing is rapidly evolving and revolutionizing the fabrication of complex metal components with tunable properties. Machine learning and neural networks have emerged as powerful tools for process–property optimization in additive manufacturing. These techniques work well for the prediction of a single property but their applicability in optimizing multiple properties is limited. In the present work, an exclusive neural network is developed to demonstrate the potential of a single neural network in optimizing multiple part properties. The model is used to identify the optimal process parameter values for laser power, scan speed, and hatch spacing for the required surface roughness, relative density, microhardness, and dimensional accuracy in stainless steel parts. In-house-generated experimental data are used to train the model. The model has seven neurons in the hidden layer, which are selected using hyperparameter optimization. K-fold cross-validation is performed to ensure the robustness of the model, which results in a mean squared error of 0.0578 and R2 score of 0.59. The developed model is then used to predict the optimal process parameters corresponding to the user-required part properties. The model serves as a significant pre-processing step to identify the best parameters before printing, thus saving time and costs for repeated part fabrication. The study provides more insights into the usage of a single artificial neural network for the optimization of multiple properties of printed metal parts.

Process optimization and kinetic study of biodegradation of dimethyl phthalate by KS2, a novel strain of Micrococcus sp.

The present study elucidates identification and characterization of dimethyl phthalate (DMP) degrading novel bacterial strain, Micrococcus sp. KS2, isolated from soil contaminated with municipal wastewater. Statistical designs were exercised to achieve optimum values of process parameters for DMP degradation by Micrococcus sp. KS2. The screening of the ten important parameters was performed by applying Plackett–Burman design, and it delivered three significant factors (pH, temperature, and DMP concentration). Further, response surface methodology involving central composite design (CCD) was implemented to examine mutual interactions among variables and achieve their optimal response. The predicted response indicated that maximum DMP degradation (99.67%) could be attained at pH 7.05, temperature 31.5 °C and DMP 289.19 mg/l. The strain KS2 was capable of degrading up to 1250 mg/l of DMP in batch mode and it was observed that oxygen was limiting factor in the DMP degradation. Kinetic modeling of DMP biodegradation indicated that Haldane model fitted well with the experimental data. During DMP degradation, monomethyl phthalate (MMP) and phthalic acid (PA) were identified as degradation metabolites. This study provides insight into DMP biodegradation process and proposes that Micrococcus sp. KS2 is a potential bacterial candidate to treat effluent containing DMP.

Performance of ANN in predicting the depth to width ratio and tensile strength of UNS S32750 laser weld joints

The effectiveness of the pulsed mode laser weld joint is characterized by the supply of optical energy to the interface. The laser welding process parameters, such as laser power, pulse duration, travel speed and focus, determine the efficacy of the weld, which is defined by full penetration and free of pores and defects. However, expressing the relationship between various process parameters and mechanical strength is intricate due to the prevalence of non-linear relationship. The development of computational approaches aids in optimizing the laser welding parameters and reducing trial and error. Hence, the artificial neural network (ANN) model is developed, in a python environment, to predict the optimum process parameter values for a desirable depth to width ratio and maximum tensile strength of UNS S32750 laser butt weld joints. The developed model was assessed by experiments, not utilized for training. The ANN model predicts the depth to width ratio and tensile strength of the weld joints with an accuracy of 90% and less than 10% divergence from the experimental result. Furthermore, for the process, parametric conditions are: (power: 550 W, focus: –1 mm, pulse duration: 13 Hz and travel speed: 136 mm/min) to attain maximum tensile strength.

Insulation systems based on foamed plastic

In the design and construction of residential and industrial buildings outbuildings, it is necessary to use effective thermal insulation materials that provide a comfortable microclimate in the room, as well as the required conditions for implementing technological processes. An essential factor is the reduction of the negative impact on the environment, which is expressed both in the reduction of energy consumption and in the possibility of rational use of industrial waste for partial replacement of the main raw material components. To increase the efficiency of heat-insulating materials based on foamed polyethylene, it was necessary to develop a technique for selecting the composition of synthetic polyethylene foam matrix, considering the possibility of using secondary modified polyethylene foam at different foaming modes. The compositions of modified polyethylene foam were selected using statistics methods and analytical optimization. The developed bases of optimization and composition selection methodology allowed to establish optimal consumption values of main components and process parameters corresponding to the specified requirements. Using alignment charts, the solution of the predictive problem of estimation of values of strength and average density of foamed polyethylene depending on the value of consumption of main components was realized.

Multi-objective optimization and prediction of surface roughness and printing time in FFF printed ABS polymer

In this study, fused filament fabrication (FFF) printing parameters were optimized to improve the surface quality and reduce the printing time of Acrylonitrile Butadiene Styrene (ABS) polymer using the Analysis of Variance (ANOVA), it is a statistical analysis tool. A multi-objective optimization technique was employed to predict the optimum process parameter values using particle swarm optimization (PSO) and response surface methodology (RSM) techniques. Printing time and surface roughness were analyzed as a function of layer thickness, printing speed and nozzle temperature. A central composite design was preferred by employing the RSM method, and experiments were carried out as per the design of experiments (DoE). To understand the relationship between the identified input parameters and the output responses, several mathematical models were developed. After validating the accuracy of the developed regression model, these models were then coupled with PSO and RSM to predict the optimum parameter values. Moreover, the weighted aggregated sum product assessment (WASPAS) ranking method was employed to compare the RSM and PSO to identify the best optimization technique. WASPAS ranking method shows PSO has finer optimal values [printing speed of 125.6 mm/sec, nozzle temperature of 221 °C and layer thickness of 0.29 mm] than the RSM method. The optimum values were compared with the experimental results. Predicted parameter values through the PSO method showed high surface quality for the type of the surfaces, i.e., the surface roughness value of flat upper and down surfaces is approximately 3.92 µm, and this value for the other surfaces is lower, which is approximately 1.78 µm, at a minimum printing time of 24 min.

Preparation and application of a surfactant‐modified Halloysite nanotubes for the adsorption of nitrates from aqueous solutions: Kinetic and equilibrium studies

AbstractThe current research proposes the hexadecyltrimethylammonium bromide‐modified Algerian halloysite nanotubes (HNTs‐CTAB) as adsorbent for nitrate ions removal from aqueous solution via electrostatic interactions. X‐ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), Fourier‐transform infrared spectroscopy (FT‐IR), and nitrogen adsorption–desorption analysis techniques have been used to characterize the prepared adsorbants. The different operating condition that may affect the adsorption of nitrate was carried out. HNTs and HNTs‐CTAB adsorption capacities are considerably pH dependent. The optimum conditions for maximum removal of nitrate from aqueous solution were as follows: adsorbent masse was 40 mg, initial pH was 4, temperature was 25°C, initial nitrate concentration of 100 mg/L and equilibrium time was 3 h. Under optimal value of process parameters, maximum NO3− adsorption capacity of 47.4 mg/g was obtained. Kinetic investigation confirmed that the nitrate adsorption on modified HNTs followed a pseudo‐second‐order with correlation coefficients greater than 0.98 (R2 = 0.9870) and that empirical Freundlich isotherm was best fitted nitrate equilibrium adsorption. The thermodynamic studies indicated that the adsorption of nitrate onto HNTs‐CTAB is endothermic and spontaneous. HNTs‐CTAB displayed high regeneration ability by retaining a capacity above 40 mg/g after four reuse cycles. HNTs‐CTAB containing plentiful nitrate could be returned to the soil directly as an economic slow release fertilizers or a soil amendment due to its environmental‐friendly performance. Therefore, HNTs‐CTAB used for remediation of wastewater might be employed easily as farmland fertilizer.

Influence of process parameters on mechanical strength, build time, and material consumption of 3D printed polylactic acid parts

AbstractThe purpose of the study is to examine and analyze the effect of different process parameters of fused deposition modeling process, namely, nozzle diameter, build orientation, infill density, raster pattern, layer height, and print speed on the response variables, namely, tensile strength, build time, and material consumption. Taguchi L27 orthogonal array experimental design has been adopted to print the PLA samples for experimental investigation. The optimum values of process parameters are obtained from the signal‐to‐noise ratio values of responses. The performed confirmation tests revealed that optimum process parameters combination determined through Taguchi method improves the tensile strength, reduce the build time, and material consumption significantly. Confirmation from analysis of variance (ANOVA) results revealed that the build orientation and nozzle diameter are the most influential process parameters for all the considered responses. The infill density is found to be an influential process parameter for material consumption. Further, the developed regression models for prediction of response characteristics and normal probability plots show significance of coefficients for predicted models. Results from the confirmation test revealed that the PLA part printed with optimum settings ((Nd)3‐(Bo)3‐(Id)3‐(Rp)1‐(Lh)2‐(Ps)1)) for tensile strength shows maximum tensile strength of 61.24 MPa with an improvement of 6.26%. Similarly, the part printed with the optimum settings ((Nd)3‐(Bo)1‐(Id)2‐(Rp)1‐(Lh)3‐(Ps)3) for build time takes 0.32 h of time with a reduction of 8.57%. The part printed with the optimum settings ((Nd)1‐(Bo)1‐(Id)1‐(Rp)2‐(Lh)1‐(Ps)2) for material consumption consumes material of 7.6 g with a reduction of 2.56%. Fracture mechanics results depict that variation of process parameters during the fabrication of samples has significant influence on the tensile strength.

Evaluation of the effect of fermentation conditions on the functional and technological characteristics of the semifinished meat product

The article proposes the implementation of a software application that allows assessing the quality of meat products, the formulation of which includes biomodified secondary raw materials. The authors conducted a study of the process of developing a recipe for a meat product with a modified component, developing a mathematical model that takes into account the effect of enzyme dosages and fermentation conditions on the functional and technological characteristics of a meat semi-finished product. This includes determining the optimal values of process parameters and compounding a new product, software implementation of product quality assessment using secondary meat raw materials. The authors of the article have developed new technologies focused on the intensification of a complex of biochemical transformations that take place in meat raw materials in the process of salting and precipitation in the production of sausages. One of the ways to solve this problem is connected with the biotechnological principle of modification of meat raw materials – directed regulation of the course of biotechnological, physico-chemical and microbiological processes, as a result of which the structure, color and flavor characteristics of the finished product are formed. The practical interest of this study is the development of a software module that allows you to calculate the optimal composition of the formulation, taking into account the quality indicators of the resulting product.

Experimental investigations on effect of graphite loading on melt flow behaviour of ABS-Gr composite for fused filament fabrication (FFF) process

ABSTRACT In FFF process, the melt flow rate (MFR) of the polymer matrix composite material is an important factor in selecting the appropriate process parameters for printing parts. Concerning FFF, MFR and the extrusion temperature (ET) are important parameters due to their influence on the degree of layer adhesion and strength. The current study aims to investigate the MFR of ABS composite by varying the proportion of graphite (Gr) powder. The effects of Gr loading (5, 10, and 20 wt%) on MFR of composite and suitability for FFF process were investigated using MFI machine under different extrusion temperatures and extrusion loads. ANOVA and S/N ratio analysis were used to determine the significance of each process parameter and percentage of error involved in experiments. The experiments were performed three times at set of levels to find optimum response and reduces the error. The results shows that ETmost influential parameter among others, error was less than 1% in the experimental study. A signal-to-noise (S/N) ratio analysis is performed to optimise process parameters, with ‘larger is the better’ criterion. The combination ET3-EL3-FL1 i.e. ET 240°C, EL 5 kg and FL 5 wt% is observed as optimal process parameters values to get optimal MFR.

Comparative Performance Analysis and Optimization of Acrylic Evacuated Tube Solar Collector with Water in Glass Tube Collector Through RSM

The dominant technique used to utilize solar energy to heat water for domestic, industrial, and commercial requirements is a solar water heater. This research work aims comparative performance analysis of acrylic evacuated tube solar collector with water in glass evacuated tube solar collector and also, to investigate the optimal value of process parameters affecting the performance of water in glass and acrylic evacuated tube solar collectors. In this research work, the intensity of solar radiation, tilt angle of the collector, and the time are considered as independent variables. The temperature difference of water of glass evacuated tube solar collector and acrylic tube solar collector is measured as a response. The input parameters are optimized by using CCD method in RSM for the maximized value of the temperature difference. The result of the experiment shows that optimal values of solar radiation 843 W/m2, tilt angle of solar collector 32.1°, and time period is 59.2 min are for maximized value of temperature difference of water 7.4°C for glass and 7.7°C for acrylic evacuated tube solar collectors respectively. The experimental result deviates 1.33% in glass and -1.3% in acrylic tube solar collector from simulated values.

Study of thermal energy storing beeswax microcapsules by in situ polymerization method

Bioproduct beeswax has good potential to serve as Phase Change Material (PCM) for thermal energy storage applications. But leakage problems during the thermal transition process of beeswax demand for its encapsulation. This study demonstrates the use of resorcinol modified phenol-formaldehyde shell for beeswax microencapsulation. The optimum values of process parameters for microencapsulation were determined with the help of the Taguchi orthogonal array. Taguchi method helps to minimize experimental formulation numbers for determining optimum values of process parameters. The optimum values for control parameters core to shell ratio, surfactant concentration and agitation speed were 1:1, 3% and 800 rpm respectively. The microencapsulation process carried out with optimum parameter values yielded microencapsulated PCM. FTIR, DSC and particle size analysis confirmed microencapsulation of beeswax with resorcinol modified phenol-formaldehyde shell. Microcapsules had high melting enthalpy of 148.93 J/g with the peak phase transition temperature of 57.86 oC. Morphological analysis with SEM and optical microscope reported spherical geometry of beeswax microcapsules. Heat transfer profile of microcapsules captured with thermal imaging camera reciprocated with DSC results. The prepared thermal energy storing microcapsules will enhance the performance of building materials, solar devices, electronic systems and packaging materials.

Parametric optimization of friction stir welding process parameters of dissimilar welded joints using grey relational analysis and desirability function approach

Purpose The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey relational analysis and desirability function approach (DFA). Design/methodology/approach The welded sheets were fabricated as per Taguchi orthogonal array design. The effects of tool rotational speed, transverse speed and tool tilt angle process parameters on ultimate tensile strength and hardness were analyzed using grey relational analysis, and DFA and optimum parameters combination was determined. Findings The tensile strength and hardness values were evaluated from the welded joints. The optimum values of process parameters were estimated through grey relational analysis and DFA methods. Similar kind of optimum levels of process parameters were obtained through two optimization approaches as tool rotational speed of 1150 rpm, transverse speed of 24 mm/min and tool tilt angle of 2° are the best process parameters combination for maximizing both the tensile strength and hardness. Through these studies, it was confirmed that grey relational analysis and DFA methods can be used to find the multi response optimum values of FSW process parameters. Research limitations/implications In the present study, the FSW is performed with L9 orthogonal array design with three process parameters such as tool rotational speed, transverse speed and tilt angle and three levels. Practical implications Aluminium alloys are widely using in automotive and aerospace industries due to holding a high strength to weight property. Originality/value Very limited work had been carried out on multi objective optimization techniques such as grey relational analysis and DFA on friction stir welded joints made with dissimilar aluminium alloys sheets.

Friction stir welding and friction stir spot welding of polymethyl methacrylate (PMMA) to other materials: A review

Polymers are widely used in packaging and automobile industries because of their desirable properties such as lightweight, transparency, strength to weight ratio, good hardness and stiffness, low water absorption, ultraviolet rays' protection. Keeping Polymethyl methacrylate (PMMA) polymer in focus, this review research is being done here to collect the literature available best of our knowledge on the joining of PMMA to PMMA/Polymers/ Metal alloys by Friction stir welding/ Friction stir spot welding (FSW/FSSW). Friction stir welding is a solid-state joining process in which the temperature remains below the material's melting temperature. This friction stir welding technique is widely applied on materials that are difficult to weld by fusion welding, such as aluminium alloys, titanium alloys, and polymers. Friction stir welding is an ecological weld method and environmentally friendly as it does not produce harmful radiation toxic gases, requires less energy, produces less waste than conventional fusion welding processes. It was found out that by applying Friction stir welding/ Friction stir spot welding technique, the Polymethyl methacrylate can be welded to itself, to polymers, and metal alloys. In this review, efforts are made to point out the optimum level of process parameters of FSW concluded by researchers in their studies of PMMA joining. For each study, its parameter range and the optimum value of process parameters are presented in the form of tables and graphs, and explanations and discussions are made to better understand welding Polymethyl methacrylate polymer by Friction stir welding process.