Optimization Of Multiple Parameters Research Articles

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

Experimental multiple parameters optimization of the injection strategies for a turbocharged direct injection hydrogen engine to achieve highly efficient and clean performance

Improving the brake thermal efficiency (BTE) and reducing NOx emissions are two main goals for hydrogen-powered internal combustion engines to achieve efficient and clean performance. This paper focuses on the multiple parameters optimization of injection and ignition both experimentally and numerically based on a 2.0L turbocharged direct injection hydrogen engine. Firstly, the effects of single and split injection on engine performance, in-cylinder mixing and combustion characteristics are investigated. Secondly, the single-factor effect of split injection parameters and spark timing on engine performance are obtained with different engine speeds. Significant interaction effects are detected between these factors. Thus, a multi-parameter optimization is conducted based on the response surface methodology method to analyze the co-relationship of factors (split injection control parameters and ignition) on responses (BTE, NOx emissions, and knock intensity) at high-load. Finally, a maximum BTE of 43.03 % and a reduction in NOx emissions by 71.19 % are achieved with the help of model prediction at 2500 rpm and a load of 1.40 MPa. Further optimization of other engine speeds is conducted, and the boundary of high-efficiency region (BTE>40 %) covers over 50 % of the load map after optimization. These results can provide valuable support for the performance improvement of efficiency and clean hydrogen engines.

Multi-parameter tunable synthetic matrix for engineering lymphatic vessels

Controlling the formation of new lymphatic vessels has been postulated as an innovative therapeutic strategy for various disease phenotypes, including neurodegenerative diseases, metabolic syndrome, cardiovascular disease, and lymphedema. Yet, compared to the blood vascular system, little is known about the molecular regulation that controls lymphatic tube formation in a synthetic matrix. In this study, we utilize hyaluronic acid (HA)-hydrogels to design a novel platform for decoupled investigation into how mechanical and biochemical cues regulate lymphatic vessel formation in a synthetic matrix. Using HA and controlling the degree of modification provides a method to preserve and modulate key lymphatic markers Prox1, LYVE-1, and Pdpn. The chemistry of the system allows for spatial and temporal patterning of specific peptides and substrate stiffnesses, and an MMP-sensitive crosslinker allowed cells to degrade and remodel their matrix. Through systematic optimization of multiple parameters, we have designed a system that allows human lymphatic endothelial cells (LECs) to self-assemble into vessels in vitro within 3 days. These engineered vessels can be cultured for up to 3 weeks and can be used for high-throughput mechanistic studies, or can be implanted into immunodeficient mice where they have demonstrated the ability to integrate and mature. Collectively, these studies report a novel, fully-defined 3D synthetic matrix system capable of generating lymphatic vessels in vitro that provide promise as an in vitro screening platform and as a therapeutic vessel transplant, which to our knowledge, is the first ever 3D lymphatic tissue engineering approach to not require the use of support cells.

Energy minimization for IRS-and-UAV-assisted mobile edge computing

Intelligent reconfigurable surface (IRS) is an emerging technology for the enhancement of spectrum and energy efficiency. We propose a novel IRS-and-unmanned aerial vehicle (UAV)-Assisted mobile edge computing (MEC) framework, where a MEC server installing on an UAV to facilitate task calculations by mobile users (MUs), and an IRS modulates channels between MUs and the UAV. Non-orthogonal multiple access (NOMA) is employed for further improving system-wide spectral efficiency. There are needs for joint optimization of multiple parameters, e.g., the task partition parameters and the transmit power of all MUs, the reflection coefficient matrix of the IRS and the movement trajectory of the UAV, and such needs raises the challenge of minimizing the long-term total energy consumption of all MUs while satisfying required transmission rate and task completion delay. We divide optimization tasks into two sub-problems and propose specific solutions respectively, i.e., relevant decisions about the UAV and MUs to be solved by deep reinforcement learning (DRL); and the reflection coefficient matrix of the IRS to be solved by block coordinate descent (BCD). A series of experiments have verified the effectiveness of the proposed communication techniques and optimization algorithms. Simulation results demonstrate that (1) NOMA-IRS technique achieves better energy efficacy compared to the cases where random IRS or no IRS is deployed and the conventional orthogonal multiple access (OMA) technique with IRS. (2) our proposed deep deterministic policy gradient (DDPG)-BCD algorithm outperforms other four benchmark algorithms in solving the complex and dynamic optimization problem.

Exploring Enzyme-Mimicking Metal-Organic Frameworks for CO2 Conversion through Vibrational Spectra-Based Machine Learning.

In pursuing the benefits of natural enzyme catalysts while overcoming their limitations, we find metal-organic frameworks (MOFs), renowned for their highly tunable functionalities, stand out in biomimetic applications. We used unsupervised machine learning on density functional theory-computed vibrational infrared and Raman spectral features to screen 300 Zn-MOFs for CO2 conversion, similar to carbonic anhydrase (CA). Our findings confirmed that MOFs with spectroscopic attributes closely resembling those of CA hold the potential for replicating CA's electronic and catalytic properties. Unlike previous studies that relied on heuristic or trial-and-error methods and focused on geometric configurations, our research uses vibrational spectral features to explore structure-property relationships, making them more accessible through spectroscopy. Moreover, we highlight vibrational spectral features as efficient carriers for highly dimensional chemical information, enabling the simultaneous optimization of multiple performance parameters. These findings pave the way for pioneering designs of enzyme-mimetic MOFs and concurrently expand the application scope of spectroscopic tools in biomimetic catalysis.

The influence of various optimization procedures on the dynamics and efficiency of nonlinear energy sink with synergistic effect consideration

The vibro-impact system under consideration consists of a primary structure, which is a linear oscillator (LO) and a vibro-impact damper coupled with it. The damper is a vibro-impact nonlinear energy sink (VI NES). The dynamics of this system and damper efficiency in attenuation vibrations of the primary structure are studied using numerical simulation. The paper examines the problem of choosing optimal damper parameters that provide the best mitigation of the primary structure energy. The optimization procedure was carried out in several stages. The multiple parameters optimization manifested a synergistic effect. This made it possible to select the most effective VI NES design. The chosen damper design provided the best energy reduction. Thanks to it, the irregular regimes of the system movement turned into the periodic ones. The damper hits both the primary structure directly and an obstacle rigidly connected to the primary structure.

All-in-one: Multi-parameter engineering on γ-Fe2O3 for ultra-broadband microwave absorption

The functionality and property of materials are normally controlled by changing their composition, defects, morphology, lattice, crystallinity, etc. However, achieving a wide effective absorption bandwidth through multiple-parameter optimization is less reported. Simultaneously adjusting these parameters can serve as an effective control knob to increase dielectric loss, including conduction and polarization, thereby providing a broadband absorption toward radiated microwave for potential applications. Herein, magnetic γ-Fe2O3 in a two-dimensional configuration was synthesized through a molten salt process, which was further modified by Co and Al heteroatoms to facilitate more polarization processes. The reinforcement of conduction loss is achieved by increasing the crystallinity of the absorber, which facilitates the rapid transfer of free electrons and reduces scattering. Controlled alkaline etching was employed to extract a portion of the intercalated Al atoms. Henceforth, appropriate lattice distortion and numerous O vacancies are constructed, which can not only generate new polarization centers on the absorber matrix but also maintain a high crystalline structure with good conduction loss feature. In addition, the magnetic loss and impedance matching characters are optimized. As a result, the composition, structure, defects, and crystallinity of magnetic γ-Fe2O3 nanosheets have all been optimized, which enable an impressive microwave absorption performance with an effective absorption bandwidth of 7.82 GHz and a minimum reflection loss of −54.9 dB. This work explores improving the electromagnetic parameters of absorbers through collaborative regulation of multiple parameters and provides guidance for a novel and facile strategy for controlling microwave absorbers.

Stimulating bioelectric generation and recovery of toxic metals through benthic microbial fuel cell driven by local sago (Cycas revoluta) waste.

Benthic microbial fuel cell (BMFC) is the most promising type of bioelectrochemical approach for producing electrons and protons from natural organic waste. In the present work, a single-chamber BMFC was used, containing sago (Cycas revoluta) waste as the organic feed for microorganisms. The local wastewater was supplemented with heavy metal ions (Pb2+, Cd2+, Cr3+, Ni2+, Co2+, Ag+, and Cu2+) and used as an inoculation source to evaluate the performance of BMFC against the toxic metal remediations. According to the experimental results, the maximum power density obtained was 42.55 mW/m2 within 25days of the BMFC operation. The maximum remediation efficiency of the metal ion removal from the wastewater was found to be 99.30% (Ag+). The conductive pili-type bacteria species (Acinetobacter species, Leucobacter species, Bacillus species, Proteus species. and Klebsiella pneumoniae) were found in the present study during isolation and identification processes. This study's multiple parameter optimization revealed that pH 7 and room temperature is the best condition for optimal performance. Finally, this study included the mechanism, future recommendations, and concluding remarks.

Application of grey system model with intelligent parameters in predicting regional electricity consumption

PurposeGiven the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.Design/methodology/approachFirst, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.FindingsThe study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.Originality/valueAcknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Fabrication of electro-conductive thin films of nanoparticles and MXene sheets using reclaimed Zn, Mn, and C from spent batteries

Farseeing sustainable consumption concept, major parts of zinc‑carbon battery i.e. zinc, manganese and central graphite rod were up cycled. Low temperature roasting technique was employed for the extraction of zinc and manganese. Optimization of multiple parameters was meticulously adjusted that resulted in the recovery of 99 % Zn and Mn. Respective nanoparticles were derived from leached solutions that were characterized through XRD, FESEM-EDX and further employed in fabrication of semi-conductive thin films via Layer by Layer technique. Central carbon rod of spent cells and recycled aluminum were incorporated for the synthesis of MAX phase composite followed by its conversion to MXene sheets. The MAX phase composites symbolized as MAX/Al2Ti-800 and MAX/Al2Ti-900 were successfully prepared at relatively lower temperatures i.e. 800 °C and 900 °C respectively. The prepared composites were further etched via MILD method for preparation of MXene800 and MXene900 derived from MAX/Al2Ti-800 and MAX/Al2Ti-900 and were characterized using XRD and SEM. Presence of Al2Ti within MXene800 made it quite stable against hydrolysis as depicted by Raman and UV–visible spectra. Electrical conductivity of MXene800 measured by resistance meter was 3.9 × 102 S/cm; moreover, MXene800 was tailored in free-standing thin films having architecture [CNER-MXene800@PVC] that showed significant thermal resistance as well as exhibit semi-conductive nature.

Parametric Analysis and Optimization for Thermal Efficiency Improvement in a Turbocharged Diesel Engine with Peak Cylinder Pressure Constraints

While the original equipment manufacturers are developing engines that can withstand higher PCP, the methodology to maximize the thermal efficiency gain with different PCP limits is still not well-known or documented in the literature. This study aims to provide guidance on how to co-optimize air system parameters, compression ratio, and intake valve closing (IVC) timing of heavy-duty turbocharged diesel engines to enhance thermal efficiency with peak cylinder pressure (PCP) constraints. In this study, a one-dimensional turbocharged engine model is established and validated by experimental data. The effects of turbocharger efficiency, boost pressure, high-pressure exhaust gas recirculation (HP EGR) ratio, compression ratio (CR), and IVC timing on diesel engine efficiency are assessed under PCP constraints through parametric analysis. The results indicate that for enhancing engine thermal efficiency under limited PCP, an increment in boost pressure and CR, and late IVC timing compared to baseline is required. By multiple parameter optimization, the best parameter combination under different PCP constraints is proposed. At a PCP limit of 20 MPa, the combination of a compression ratio of 18.57, boost pressure of 298 kPa, and IVC timing of −95.2 °CA ATDC yields a 1.56% (absolute value) improvement in ITEn over the baseline condition. Raising the PCP limits from 20 MPa to 25 MPa requires increasing the compression ratio to 21.92, boost pressure to 308 kPa, and delaying the intake valve closing timing to −88.7 °CA ATDC, which results in an absolute improvement of 0.86% in ITEn. Baseline engine configuration is updated accordingly to validate the thermal efficiency improvement strategy at a 25 MPa PCP limitation. Experimental results demonstrate a 2.2% (absolute value) improvement in brake thermal efficiency and 1.98% (absolute value) improvement in overall energy efficiency.

Thermoeconomic analysis and multiple parameter optimization of a combined heat and power plant based on molten salt heat storage

The global energy supply is transitioning to sustainable, low-carbon energy. Power-to-heat technology with molten salt thermal energy storage (TES) is a potential way to accommodate renewable power, and the stored heat can be converted to heat and electricity for residential heating and power supply with a combined heat and power plant (CHP). In this study, a CHP-TES system with valley electricity as the input energy source is proposed. Thermodynamic models are developed, and an economic evaluation index is established by considering electricity price, heat price, TES scale, and CHP layout. Then, multiparameter optimization of the proposed CHP-TES system is conducted with the genetic algorithm, and the energy-exergy-economic feasibilities of the system are evaluated. The CHP-TES system can operate under CHP and condensing modes in heating and non-heating periods, and its output power can be regulated during peak and valley load time, respectively. The exergy efficiency of the CHP-TES system is 45.07% and 36.67% in condensing modes with the peak and valley output power, respectively. The exergy efficiency of the CHP-TES system is 46.16% and 42.96% in CHP modes with the peak and valley output power, respectively. The exergy is mainly destructed in the electric heater, which accounts for over 65% of the total exergy loss in all operation modes. The equipment investment of the TES, steam generator, and CHP systems account for 87.59%, 3.53%, and 8.88% of the total, respectively. The electric heater takes the largest portion of the total equipment investment, which accounts for 37.99%. When the peak-valley electricity price changes from 0.121 $ kWh−1 to 0.259 $ kWh−1, the net present value increases from $592 million to $5.79 billion.

Enhanced heat dissipation of ribbed channels based on the coupling optimization of multiple structural parameters

The heat dissipation of ribbed channels is dominant by the strong coupling of multiple structural parameters, while it is less concerned due to the lack of efficient methodology both in experimental and numerical solutions. In the present work, the movable experimental facility and the CFD solutions based on the conjugate gradient method are established. Firstly, the coupling impacts of different parameters on the airflow field in the channel are identified. It is found that the longitudinal vortices coupling with transverse secondary flows are in charge of the heat dissipation of the ribbed channel. And the longitudinal vortices are affected by the relative roughness spacing, and the transverse secondary flow is mainly dependent on the aspect ratio for L-shaped rib, while for the V-shaped and W-shaped ribs, the transverse secondary flow mainly is mainly dependent on the angle of attack. Secondly, the coupling optimization of different ribs is completed and the optimal structural parameters are obtained using the developed experimental and numerical methodology. Overall, compared with the smooth channel, the optimal L, V and W-shaped ribs deliver the Nusselt number increasing by 83.04%, 155.18% and 197.17%.

Exergy analysis and optimization of multiple injection parameters of a diesel engine with Taguchi method

ABSTRACT Multiple injection parameters have a significant effect on performance and emission formation in diesel engines. This study presents the optimization of these parameters with Taguchi method for different engine operating conditions. The experiments were conducted using four injections per cycle named Pilot1 (P1), Pilot2 (P2), Main (M), and Post (Po). The input parameters considered were injection timings and fuel quantities for each injection, while the output parameters were brake specific fuel consumption (BSFC) and nitrogen oxides (NOx) emissions. According to the results, the analysis of variance (ANOVA) shows that the durations of the P2 (D_P2) and Po (D_Po) and the start of the Main (SOI M) injection are very significant on BSFC and NOx emissions as independent of the engine speed. Based on contribution ratios at 1750 rpm and 2250 rpm operating conditions, D_Po is the most effective parameter on BSFC with 47.28% and 51.30%, while D_P2 has the greatest impact on NOx emissions with 37.96% and 61.80%, respectively. It is found from the optimization model obtained by using the Signal/Noise (S/N) ratios that injecting 5% of the total fuel in the post-injection phase could simultaneously improve BSFC and NOx emissions. Furthermore, the optimization model generally reduces heat loss exergy, exhaust exergy, and exergy destruction compared to the experimental values.

Improving word similarity computation accuracy by multiple parameter optimization based on ontology knowledge

Improving word similarity computation accuracy by multiple parameter optimization based on ontology knowledge

Inverse design of operational parameters for iron-rich phase separation from solid waste melts in induction furnace through transport phenomena analysis and multi-objective optimization

Improving the removal efficiency of iron-rich impurities and the uniformity of component distribution is an indispensable step in recycling aluminosilicate solid waste to prepare the raw materials of glass ceramics, which can be achieved by smelting reduction separation in the induction furnace. In this work, a multi-physics real-time coupling model that integrates carbothermal reduction and interfacial mass transfer between two-phase melts in the induction furnace was proposed to improve the conventional methods that simulate the interface profile and multi-physics separately. By solving the numerical model, the reduction degree, energy consumption and component distribution can be predicted and the characteristics of mass-heat-component transport and interphase mass transfer can be obtained. For the collaborative optimization of multiple parameters, including current, frequency, and melting duration, an inverse design method that integrates CFD-ANN-NSGA-II was proposed to minimize the iron oxide (FeO) residual and energy consumption, and maximize the uniformity of components in the glass phase simultaneously. The optimal operating parameters set were determined by analysis of the Pareto front and the selection criteria. Further analysis of the Pareto solution set provides a fundamental basis for regulating the operational parameters of different reduction degrees. Overall, the inverse design method that synthesized the proposed numerical model is an effective way of predicting and optimizing the operating parameters of induction heating furnaces of iron removal from solid wastes.

Multiple parameter optimization methodology by integrating a game theory principle into priority-based decision making

Mathematical model development for multivariate decision-making problems (i.e., multiple parameter and response optimization problems) has received significant attention and has been investigated by many researchers and practitioners. Although many different optimization models and methods, including priority and weight allocation problems, have been proposed in the literature, there is significant room for improvement. The majority of existing optimization methods may have difficulties in determining reliable priorities and weights for practical industrial situations. In addition, because they often focus on tradeoffs only among multiple output responses, these methods may not incorporate the multiple priority and/or weight effects directly from input factors to output responses (i.e., quality characteristics). To address these problems, the primary objective of this research is to propose a new multi-objective optimization approach by integrating a game theory principle (i.e., the Stackelberg leadership game) into a robust parameter design model to determine the optimal factor settings. First, a multi-response robust design optimization (RDO) problem was formulated using a mean squared error model and response surface methodology. A Stackelberg leadership game is then integrated into this RDO problem, where multiple responses play the role of game participants. Second, the integrated RDO model using the Stackelberg game-based multi-response (SGMR) formulation approach is analyzed by decomposition into various sequential optimization models in terms of different leader–follower relationships. Finally, non-dominated solutions are obtained from the proposed model by evaluating the overall quality loss (OQL). A pharmaceutical numerical example was performed to demonstrate the proposed integrated model and to examine whether this model can determine the most efficient solutions when the relative importance among multiple responses is unidentified. In addition, a comparative study associated with the conventional multi-objective optimization methods and the newly proposed RDO model is presented, and the proposed RDO model provided significantly better solutions than the conventional methods in the relevant comparative study.

Design optimization of 3D printed flow path plates in high-performance bioethanol fuel cells

PurposeEffective performance of a direct ethanol fuel cell (FC) stack depends on the satisfactory operation of its individual cells where it is always challenging to manage the temperature gradient, water flow and distribution of reactants. In that, the design of the bipolar fuel flow path plate plays a vital role in achieving the aforementioned parameters. Further, the bipolar plates contribute 80% of the weight and 30%–40% of its total cost. Aim of this study is to enhance the efficiency of fuel to energy conversion and to minimize the overall cost of production.Design/methodology/approachThe authors have specifically designed, simulated and fabricated a standard 2.5 × 2.5 cm2 active area proton exchange membrane (PEM) FC flow path plate to study the performance by varying the flow fields in a single ladder, double ladder and interdigitated and varying channel geometries, namely, half curve, triangle and rectangle.FindingsUsing the 3D PEMFC model and visualizing the physical and electrochemical processes occurring during the operation of the FCs resulted in a better-performing flow path plate design. It is fabricated by using additive manufacturing technology. In addition, the assembly of the full cell with the designed flow path plate shows about an 11.44% reduction in total weight, which has a significant bearing on its total cost as well as specific energy density in the stack cell.Originality/valueSimultaneous optimization of multiple flow path parameters being carried out for better performance is the hallmark of this study which resulted in enhanced energy density and reduced cost of device production.

Optimized ultrasonic total focusing imaging of diverse and multiple defects in crossply CFRP: Floquet wave theory, numerical simulation, and experimental validation

Carbon fiber reinforced polymers (CFRPs) with large thickness (≥20 mm) are being extensively used for aerospace structures, and are prone to the manufacturing and in-service defects due to the complexity and difficulty of manufacturing. This study addresses an intensive interrogation of multi-parameter optimization towards a high-quality ultrasonic total focusing method (TFM) imaging for diverse and multiple defects in crossply CFRP. Firstly, material parameters of unidirectional laminate are inversely constructed based on Christoffel equation by inputting the experimentally measured wave speed, based on which the wave speed and homogenization region for the crossply CFRP are obtained with the Floquet wave theory. Then, the wave propagation at crossply CFRP with preset side drilled holes (SDHs) at depths of 4 mm and 12 mm is modelled, from which the full matrix data are extracted for TFM imaging using multiple parameter optimization, including wave velocity correction, propagation restriction angle, and wave central frequency. With the proposed signal-to-noise ratio as the criterion of TFM imaging quality, the combined parameters for optimized imaging are sorted out, which are strongly related to the feature of propagating and non-propagating waves calculated with the Floquet wave homogenization theory. A further experimental validation is performed to image multiple SDHs and delamination with size over 2 mm and depths covering 2.5 mm to 17.5 mm. This systematic analysis of parameter optimization for TFM imaging in thick CFRP sets up the foundation for the TFM towards a real engineering application of CFRP detection.

Dimension synthesis of a 2T2R curved layer FDM 3D printer

3D printing or Additive Manufacturing (AM) is significant in various applications. The authors’ previous research indicates that higher-degree-of-freedom (DOF) 3D printers have the potential for curved layer printing and multi-direction AM. Hence, this paper focuses on designing a novel 2T2R FDM 3D printer. Performance-chart-based design methodology (PCbDM) has been used for multiple parameters optimization problems due to its reliability, globality, and intuition. While its limitation is the number of optimization parameters less than four. The printer’s print head is attached to a planar parallel 1T1R mechanism (4R1P, 3R1P, and passive P). Objectives and requirements require the print head to move over a more considerable distance with a constant swing range and proper pressure angle. So, the 4R1P five-bar (virtual double-rocker, crank-rocker, and rocker-crank) has been investigated to facilitate PCbDM. Meanwhile, one DOF remote center of motion (RCM) mechanism has been integrated for decoupling motion control. Finally, the selected parameters are verified by comparing the numerical and simulation results and experiments. Besides, this novel 3D printer’s initial feasibility of flat and curved layer printing is studied with several printed parts to give qualitative analyses. This early-stage work generally intends to investigate the dimension synthesis and show the resulting 2T2R-type rotary 3D printer’s initial feasibility for curved printing. The research can help people understand how to carry out the dimension synthesis of 4R1P and a novel mechanism with multi-chain with the help of Grashof criteria to reduce the design space dimensionality.