Feasible Space Research Articles

Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration

AbstractWhile extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss.

Reducing the feasible solution space of resource-constrained project instances

This paper present an instance transformation procedure to modify known instances of the resource-constrained project scheduling problem to make them easier to solve by heuristic and/or exact solution algorithms. The procedure makes use of a set of transformation rules that aim at reducing the feasible search space without excluding at least one possible optimal solution. The procedure will be applied to a set of 11,183 instances and it will be shown by a set of experiments that these transformations lead to 110 improved lower bounds, 16 new and better schedules (found by three meta-heuristic procedures and a set of branch-and-bound procedures) and even 64 new optimal solutions which were never not found before.

Does stomatal patterning in amphistomatous leaves minimize the CO2 diffusion path length within leaves?

Abstract Photosynthesis is co-limited by multiple factors depending on the plant and its environment. These include biochemical rate limitations, internal and external water potentials, temperature, irradiance and carbon dioxide (CO2). Amphistomatous leaves have stomata on both abaxial and adaxial leaf surfaces. This feature is considered an adaptation to alleviate CO2 diffusion limitations in productive environments as the diffusion path length from stomate to chloroplast is effectively halved in amphistomatous leaves. Plants may also reduce CO2 limitations through other aspects of optimal stomatal anatomy: stomatal density, distribution, patterning and size. Some studies have demonstrated that stomata are overdispersed compared to a random distribution on a single leaf surface; however, despite their prevalence in nature and near ubiquity among crop species, much less is known about stomatal anatomy in amphistomatous leaves, especially the coordination between leaf surfaces. Here, we use novel spatial statistics based on simulations and photosynthesis modelling to test hypotheses about how amphistomatous plants may optimize CO2 diffusion in the model angiosperm Arabidopsis thaliana grown in different light environments. We find that (i) stomata are overdispersed, but not ideally dispersed, on both leaf surfaces across all light treatments; (ii) the patterning of stomata on abaxial and adaxial leaf surfaces is independent and (iii) the theoretical improvements to photosynthesis from abaxial–adaxial stomatal coordination are miniscule (≪1%) across the range of feasible parameter space. However, we also find that (iv) stomatal size is correlated with the mesophyll volume that it supplies with CO2, suggesting that plants may optimize CO2 diffusion limitations through alternative pathways other than ideal, uniform stomatal spacing. We discuss the developmental, physical and evolutionary constraints that may prohibit plants from reaching this theoretical adaptive peak of uniform stomatal spacing and inter-surface stomatal coordination. These findings contribute to our understanding of variation in the anatomy of amphistomatous leaves.

A*–Ant Colony Optimization Algorithm for Multi-Branch Wire Harness Layout Planning

The planning of multi-branch cable harness layouts holds significant practical importance in aircraft industrial contexts, yet it has received limited attention in prior research. This study aims to address the matter concerning the significance of managing multiple constraints and preventing loops. It formulates the problem as an optimization problem in 3D free-form space and resolves it using an extended A* path planning approach in combination with the ant colony optimization algorithm. Initially, a feasible search space for wiring is established through the repair and simplification of the input CAD model. Subsequently, the topology of a multi-branched wiring harness is identified, taking into account industrial requirements related to cable physics, turning, support, bundling, and electromagnetic compatibility constraints. Specifically, the disassembly or merging of branches and loops is employed to avoid wire loops. Ultimately, we propose an A*–ant colony optimization algorithm (A*-ACO) with an enhanced heuristic function for neighboring points, incorporating a concentration increment model. Experimental tests illustrate the effectiveness of this approach in minimizing wire loops and reducing the total cable layout cost, considering factors such as length, bundling, and turning costs. It results in a reduction of 67.0%, 68.5%, and 51.1% compared to A*, ACO, and manual wiring methods, respectively.

Autonomous Detection of Humans in Off-Limits Mountain Areas.

This paper is on the autonomous detection of humans in off-limits mountains. In off-limits mountains, a human rarely exists, thus human detection is an extremely rare event. Due to the advances in artificial intelligence, object detection-classification algorithms based on a Convolution Neural Network (CNN) can be used for this application. However, considering off-limits mountains, there should be no person in general. Thus, it is not desirable to run object detection-classification algorithms continuously, since they are computationally heavy. This paper addresses a time-efficient human detector system, based on both motion detection and object classification. The proposed scheme is to run a motion detection algorithm from time to time. In the camera image, we define a feasible human space where a human can appear. Once motion is detected inside the feasible human space, one enables the object classification, only inside the bounding box where motion is detected. Since motion detection inside the feasible human space runs much faster than an object detection-classification method, the proposed approach is suitable for real-time human detection with low computational loads. As far as we know, no paper in the literature used the feasible human space, as in our paper. The outperformance of our human detector system is verified by comparing it with other state-of-the-art object detection-classification algorithms (HOG detector, YOLOv7 and YOLOv7-tiny) under experiments. This paper demonstrates that the accuracy of the proposed human detector system is comparable to other state-of-the-art algorithms, while outperforming in computational speed. Our experiments show that in environments with no humans, the proposed human detector runs 62 times faster than YOLOv7 method, while showing comparable accuracy.

Tunable Metamaterials for Impact Mitigation

AbstractTraditional methods of shielding fragile goods and human tissues from impact energy rely on isotropic foam materials. The mechanical properties of these foams are inferior to an emerging class of metamaterials called plate lattices, which have predominantly been fabricated in simple 2.5‐dimensional geometries using conventional methods that constrain the feasible design space. In this work, additive manufacturing is used to relax these constraints and realize plate lattice metamaterials with nontrivial, locally varying geometry. The limitations of traditional computer‐aided design tools are circumvented and allow the simulation of complex buckling and collapse behaviors without a manual meshing step. By validating these simulations against experimental data from tests on fabricated samples, sweeping exploration of the plate lattice design space is enabled. Numerical and experimental tests demonstrate plate lattices absorb up to six times more impact energy at equivalent densities relative to foams and shield objects from impacts ten times more energetic while transmitting equivalent peak stresses. In contrast to previous investigations of plate lattice metamaterials, designs with nonuniform geometric prebuckling in the out‐of‐plane direction is explored and showed that these designs exhibit 10% higher energy absorption efficiency on average and 25% higher in the highest‐performing design.

Satisficing Strategy in Engineering Design

Abstract In engineering-design problems, usually, there are multiple goals with different units, continuous and discrete variables, nonlinear equations, nonconvex equations, and coupled decisions. Ideally, all goals’ target are reached simultaneously within the feasible space. However, the optimal solution may not be available. To deal with all those complexities, a modeling strategy named “satisficing” was proposed in the 1980s. The satisficing strategy allows designers to find “good enough” but may not be optimal solutions. In this paper, we review the publications applying the satisficing strategy on engineering-design problems, and categorize the methods regarding the design stages they manage. We define the methods dealing with all four design stages—formulation, approximation, solution, and evaluation—as the whole process satisficing methods. We review the publications using the whole process satisficing strategy in great detail. In the past 30 years, the whole process satisficing strategy has been improved and applied to a wide variety of engineering-design problems, based on which derived methods, concepts, and platforms are developed. We generalize the specialties, advantages, and scope of applications of the methods in the whole process satisficing strategy. We expect this paper provides information on when and how designers may apply satisficing for their problems.

A method to bridge energy and process system optimization: Identifying the feasible operating space for a methanation process in power-to-gas energy systems

The growing adoption of renewable energy is driving the integration of new, complex process technologies into energy systems, presenting operational optimization challenges. Simple models are necessary for computational tractability, yet detailed models are essential for feasible operating decisions. This work introduces a method for optimally operating an energy system with integrated complex process technologies. The proposed method bridges the mathematical modeling and optimization at energy and process system levels. Simple surrogate models and detailed process models are used to represent complex process technologies at their respective system levels. The resulting operational problems are solved through a sequential iterative approach. Applied to a power-to-gas energy system with an integrated methanation process, our method outperforms a benchmark method by capturing interactions between energy and process system levels, and among different process variables. The proposed method integrates more detailed constraints and ramping costs, yielding an optimal, feasible operating strategy within tractable computational time.

A trajectory planning method for a casting sorting robotic arm based on a nature-inspired Genghis Khan shark optimized algorithm.

In order to meet the efficiency and smooth trajectory requirements of the casting sorting robotic arm, we propose a time-optimal trajectory planning method that combines a heuristic algorithm inspired by the behavior of the Genghis Khan shark (GKS) and segmented interpolation polynomials. First, the basic model of the robotic arm was constructed based on the arm parameters, and the workspace is analyzed. A matrix was formed by combining cubic and quintic polynomials using a segmented approach to solve for 14 unknown parameters and plan the trajectory. To enhance the smoothness and efficiency of the trajectory in the joint space, a dynamic nonlinear learning factor was introduced based on the traditional Particle Swarm Optimization (PSO) algorithm. Four different biological behaviors, inspired by GKS, were simulated. Within the premise of time optimality, a target function was set to effectively optimize within the feasible space. Simulation and verification were performed after determining the working tasks of the casting sorting robotic arm. The results demonstrated that the optimized robotic arm achieved a smooth and continuous trajectory velocity, while also optimizing the overall runtime within the given constraints. A comparison was made between the traditional PSO algorithm and an improved PSO algorithm, revealing that the improved algorithm exhibited better convergence. Moreover, the planning approach based on GKS behavior showed a decreased likelihood of getting trapped in local optima, thereby confirming the effectiveness of the proposed algorithm.

Machine Learning Aided Optimization of Non-Metallic Seals in Downhole Tools

Machine learning-based optimization analysis is conducted to improve the performance and reliability of non-metallic seals that undergo dynamic loading. Non-metallic seals, mostly made from elastomeric materials such as rubber, are widely used in the oil and gas industry due to their combination of hyper-elastic behavior and lack of plastic deformation under pressure and temperature cycles. However, these elastomeric materials could soften at elevated temperatures or during cyclic loading which makes them more prone to extrude from the seal region. For simple seals such as O-rings, thermoplastic anti-extrusion backup rings can be used to resist rubber extrusion. For more complex molded or bonded seals subject to cyclic loads and temperature cycles, minimizing the rubber damage and maintaining the seal integrity becomes more challenging as there may be inadequate room for backup rings. Additionally, the rubber may also experience repeated long stroke lengths during service which can cause surface abrasion. Non-metallic seals are extensively used in Sand Control Tools, a class of completion equipment. In the development of these molded and bonded seals, advanced optimization analysis techniques are important to make the seal design sufficiently robust to meet the stringent requirements of industry codes and regulations. Machine learning-based optimization analysis allows for increasing the seal performance and reliability. The Mullins effect is included in the elastomeric material model to capture the material softening effects under cyclic loading. Machine learning-based optimization starts with a feasible design space that is defined based on the controlling design parameters related to the geometry of the seal. Comprehensive Finite Element Analysis is performed on each design sample under the target cyclic load and temperature cycles. The key response parameters of the non-metallic seal are selected as the principal strain and the contact stress in the rubber which together indicate the damage and seal-ability states of the design. A Performance Objective Index is defined as a function of the key response parameters. The FEA results covering the entire design space provide the simulation-driven training data to build the surrogate model using multi-layer neural networks. The optimum design solution is found from the surrogate model which correlates the Performance Objective Index to the design parameters. The results of this study reveal that the performance of the seal is greatly affected by the ratio of the elastomeric seal volume to the gland volume into which the seal is molded. As opposed to the base design, the optimum design shows a solid performance with no signs of extrusion when the principal strain of the rubber is kept below the elongation limit throughout the load cycles, and when sufficient contact stress is developed to maintain the seal.

Application and Development of 3D Printing Technology in Clothing Design

Abstract As traditional hand knitting is limited, it can only make simple three-dimensional stacking and flat knitting texture of the garment. This paper applies 3D printing technology to improve this drawback. Establishing research variables for clothing design by combining the geometric characteristics of the STL model after layering with the characteristics of 3D printing technology to construct an optimization model for 3D printing technology. The contradiction between molding quality and molding efficiency is a typical multi-factor and multi-objective optimization problem. To solve this kind of optimization problem, a genetic algorithm is used to search for the optimal solution of the objective function in the feasible domain space, aiming to optimize the parameters of 3D printing technology. An in-depth analysis of the application practice of 3D printing technology in apparel design is presented, which further points out the implementation architecture of apparel design incorporating 3D printing technology, and the performance test of apparel incorporating 3D printing technology is carried out. The data show that the average values of ST (thickness difference), B1 (warp bending stiffness), B2 (weft bending stiffness), E51 (warp elongation), and E52 (weft elongation) are 8.386mm, 5.22 μNm, 0.16 μNm, 0.421%, 0.252%, which indicates that the garments designed based on 3D printing technology have good tensile properties and hand feel appearance, and are more in line with the comfort and aesthetic demands of consumers for garment design. It shows that the garments designed using 3D printing technology have good stretchability and a hand-feel appearance, which is more in line with the comfort and aesthetic demands of consumers nowadays.

A Monte Carlo approach to understanding the impacts of initial-condition uncertainty, model uncertainty, and simulation variability on the predictability of chaotic systems: Perspectives from the one-dimensional logistic map.

The predictability of the logistic map is investigated for the joint impact of initial-condition (IC) and model uncertainty (bias + random variability) as well as simulation variability. To this end, Monte Carlo simulations are carried out where IC bias is varied in a wide range of 10-15-10-3, and, similarly, model bias is introduced in comparable range. It is found that while the predictability limit of the logistic map can be continuously extended by reducing IC bias, the introduction of the model bias imposes an upper limit to the predictability limit beyond which further reductions in IC bias do not lead to an extension in the predictability limit, effectively restricting the feasible joint space spanned by the IC-model biases. It is further observed that imposing a lower limit to the allowed variability among ensemble solutions (so as to prevent the ensemble variability from collapse) results in a similar constraint in the joint IC-model-bias space; but this correspondence breaks down when the imposed variability limit is too high (∼x>0.7 for the logistic map). Finally, although increasing the IC random variability in an ensemble is found to consistently extend the allowed predictability limit of the logistic map, the same is not observed for model parameter random variability. In contrast, while low levels of model parameter variability have no impact on the allowed predictability limit, there appears to be a threshold at which an abrupt transition occurs toward a distinctly lower predictability limit.

General Pairwise Modification Framework for Reversible Data Hiding in JPEG Images

Pairwise modification is one of the most effective ways to solve the critical issues of balancing the embedding capacity, image distortion, and file expansion in JPEG reversible data hiding (RDH). To design a satisfactory scheme based on pairwise modification, existing schemes focus on improving pairing rules, two-dimensional (2D) mappings, or ordering strategies separately while neglecting the connections between them. As a result, once pairing rules are changed, the correlative 2D mapping and ordering strategies are no longer available. To address such issues, this study proposes a framework that automatically generates optimal 2D mappings and efficient ordering strategies only by carefully initializing pairing rules. To construct optimal 2D mappings, a 2D mapping mathematical model is built to form a feasible 2D mapping solution space, in which optimal solutions are found, to assign efficient mappings for pairs with high probability. To design efficient ordering strategies, all frequencies are ranked according to an embedding evaluation model to determine more suitable ACs for data embedding. To initialize better pairing rules, this study selects nonzero ACs within ±2 for interblock pairing to form a more centralized pairwise histogram. The experimental results show that the proposed scheme introduces minor file expansion and obtains better visual quality than existing JPEG RDH schemes when the payload is the same as.

Compliant Intramedullary Stems for Joint Reconstruction.

The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.

Exploratory modelling and analysis to support decision-making under deep uncertainty: A case study from defence resource planning and asset management

Emerging approaches and tools in the literature for model-based decision support under deep uncertainty are rapidly growing. Exploratory modelling and analysis is one of the unifying ideas behind these approaches. This research leverages exploratory modelling and analysis in the context of a case study within defense resource planning for the Royal Australian Navy. We use a multi-method approach that combines exploratory modelling and analysis and a simulation model. Our research analyses a strategy from a deterministic optimization problem to assess the impact of uncertainties and to identify different model behaviours exhibited under uncertainties and the uncertainty space in which the strategy maintains reasonable performance. To achieve these objectives, we draw upon concepts from Robust Decision-Making, Behaviour-Based Scenario Discovery, and Feasible Scenario Space. The results are then analysed to derive decision-making insights and highlight the potential consequences of neglecting uncertainties. For the first time in defense resource planning, we compare the results from these three approaches and tools. These approaches and tools complement each other, leading to similar conclusions with unique insights. The results also reveal the misleading nature of considering the best approximate conditions in the predictive use of simulation models under deep uncertainty.

Simultaneous Sizing of a Rocket Family with Embedded Trajectory Optimization

This paper presents a sizing procedure for a rocket family capable of fulfilling multiple missions, considering the commonalities between the vehicles. The procedure aims to take full advantage of sharing a common part across multiple rockets whose payload capability differs entirely, ultimately leading to cost savings in designing a rocket family. As the foundation of the proposed rocket family design method, an integrated sizing method with trajectory optimization for a single rocket is first formulated as a single optimal control problem. This formulation can find the optimal sizing along with trajectory results in a tractable manner. Building upon this formulation, the proposed rocket family design method is developed to 1) determine the feasible design space of the rocket family design problem (i.e., commonality check), and 2) if a feasible design space is determined to exist, minimize the cost function within that feasible space by solving an optimization problem in which the optimal control problem is embedded as a subproblem. A case study is carried out on a rocket family composed of expendable and reusable launchers to demonstrate the novelty of the proposed procedure.

A 3D Monte Carlo tree search method for railway alignment optimization

Railway alignment design is an important process, which fundamentally affects the construction, operation and maintenance of a railway. However, optimizing an alignment is challenging due to, e.g., the usually huge search space, infinite number of possible alternatives and numerous constraints. To address this problem, we propose a three-dimensional Monte Carlo Tree Search (3D-MCTS) method for alignment optimization. Specifically, a time-varying selection approach is first designed for efficiently exploring the search space. Then, the feasible search space is dynamically delineated with a customized tree expansion operator to accelerate the search process. In addition, a simulation strategy with global reward estimation is proposed to balance global exploration and local exploitation during optimization, which contributes to enhancing the quality of the optimized alignment. Finally, the proposed 3D-MCTS is applied to a complex real-world railway case. It shows that the 3D-MCTS can find better solutions compared to the best alignments that are manually designed by experienced engineers or produced by a previous distance transform algorithm. Two sensitivity analyses also reveal the 3D-MCTS's performance and robustness with respect to cost optimization and search efficiency.

An approach for optimum performance-based seismic design of 3D steel moment frames

An optimal performance-based design (PBD) of a three-dimensional (3D) structure requires repeated evaluation; thus, proposed approaches should attempt to significantly reduce the number of evaluations required during optimization. The current study proposes a multi-step approach for optimal PBD of a 3D steel moment frame in which the structure is examined at each step for one performance level in a specific direction. To advance to the next step, the acceptance criteria for linear static analysis of the steel moment frame from ASCE 41-17 should have been satisfied in the previous step. An objective function has been formulated using the proposed stepped penalty function to achieve a feasible space, the value of which can be determined based on the number of unexamined steps. Finally, the optimal PBDs for 3D three-story and nine-story steel moment frames were evaluated using EVPS and WSA metaheuristic algorithms to evaluate the proposed process. The final optimal designs were assessed using the acceptance criteria of nonlinear static analysis. The number of performance level evaluations in each direction during optimization showed approximately 60 % and 70 % reductions during analysis by the proposed method for the WSA and EVPS algorithms, respectively.

The SMAA-MABAC approach for healthcare supplier selection in belief distribution environment with uncertainties

Supplier selection is critical to the healthcare industry, especially in emergencies. Accurate and timely supply of healthcare resources can reduce economic cost and ensure the safety of people’s lives. However, the healthcare supplier market is enormous and complex; making accurate evaluations of different suppliers and helping managers choose the most suitable one when considering many factors is becoming an important task. Many multicriteria decision-making (MCDM) methods have been applied to supplier selection in existing literature. However, the research on healthcare supplier selection still needs to be completed. In this paper, to deal with healthcare supplier selection problems when there is no sufficient time and information to decide with uncertainty, the stochastic multicriteria acceptability analysis (SMAA) method and multi-attributive border approximation area comparison (MABAC) method are first combined by taking both the advantages of SMAA and MABAC methods when the assessments are expressed by belief distributions (BDs). In the SMAA-MABAC method, the global ignorance in BDs and the uncertain criteria weights are considered by judging the feasible space to derive the final rank of healthcare suppliers with the most possible. The more straightforward calculation process of SMAA-MABAC makes it faster to make an accurate choice among various suppliers. Moreover, it is flexible to generate different recommended suggestions when diverse preference information is added in SMAA-MABAC. Finally, a case study of healthcare supplier selection is examined for managers based on distributed reviews, and targeted selection advice is given concerning different demands. Besides, the sensitive analysis is provided to better understand the influence of the criteria weights on the proposed model. The results of the comparative analysis also show the effectiveness and rationality of the proposed method.

Optimization of Butt-joint laser welding parameters for elimination of angular distortion using High-fidelity simulations and Machine learning

The high temperature gradient and high cooling rate induced in the butt-joint laser welding process generate a high residual stress, which can result in significant distortion of the final weld. Previous researches usually employed experimental approach to search for optimal parameters which is costly and time consuming. Additionally, there was several studies that used simulation to compute the distortion and to find optimal parameters, however, these studies neglect the effect of solid cooling rate and size of the heat-affected zone (HAZ) in the process of finding optimal parameters. Accordingly, the present study proposes a new systematic framework based on finite element (FE) simulations, regression analysis, and two artificial neural networks (ANNs) for determining the laser welding parameters (i.e., the laser power and welding speed) which minimize the angular distortion of the laser-welded butt joint and enhance the mechanical properties. In the proposed approach, to determine the variables of the selected double-conical heat source model for any combination of the laser power and welding speed is studied within the feasible design space. The variables are then imported into a three-dimensional COMSOL Multiphysics FE model to predict the melt pool dimensions, solid cooling rate, and size of the HAZ as a function of the laser power and welding speed. Finally, the processing map produced by the ANN is screened by four quality criteria based on the melt pool depth, angular distortion, cooling rate, and HAZ width, respectively, to determine the optimal laser welding parameters which minimize the angular distortion and maximize the mechanical properties. It shows that SS316L tensile test specimens fabricated using the optimal parameters have a tensile strength (662 MPa) which is higher than that of the base metal (660 MPa). Furthermore, the angular distortion of the butt-joint samples is less than 564 µm. Thus, the effectiveness of the proposed optimization procedure is confirmed.