Multi-performance Optimization Research Articles

Lane-changing and overtaking trajectory planning for autonomous vehicles with multi-performance optimization considering static and dynamic obstacles

Affected by the complex traffic environment, lane-changing and overtaking have become daily driving operations of autonomous vehicles, and providing a drivable trajectory is one of the critical tasks of planning processes. To this end, this paper aims to propose an optimization-algorithm-based double quintic polynomial trajectory planning model considering static and dynamic obstacles for lane-changing and overtaking maneuvers of the autonomous vehicle. Firstly, an improved double quintic polynomial planning model considering different motion states and sizes of obstacles is constructed by introducing the lane change transition state to ensure the autonomous vehicle’s driving safety. Secondly, a multi-objective performance function considering various influencing factors is established to improve the driving performances of the autonomous vehicle during lane-changing and overtaking. Finally, a particle swarm optimization (PSO) algorithm is used to optimize parameters of the proposed planning model, such as the lane change time, transition speed, and longitudinal displacement, to generate a driveability trajectory that meets the driving safety, comfort, stability, and low emission requirements of the autonomous vehicle during lane-changing and overtaking. The effectiveness and advantages of the proposed planning model are verified by comparing it with several existing planning models under different driving conditions.

Parametric Analysis and Optimization on Fiber Laser Micro-Milling on AZ31 Magnesium Alloy

One of the proficient machining technologies to create micro-features in a variety of materials is the laser micro-milling process. This procedure involves irradiating a highly concentrated laser beam on the sample and performing laser cutting at various cutting patterns to change the surface conditions and enhance the characteristics of that surface tribology. In this work, an effort has been made to mill magnesium alloy (AZ31) material using a laser. The pulse frequency, laser beam power, cutting speed, number of passes, and transverse feed are among the different process variables taken into account for the current research effort. Measured results include depth of cut and surface roughness. To ensure the validity of the established empirical models, this paper also presents experimental data. Several surface plots have been used to examine the test data. To get the lowest values of surface roughness and depth of cut, multiperformance optimization is also utilized. The influence of process factors on response has also been examined using optical microscopic images. Gray relational analysis approach is also applied to obtain the multiobjective optimization parametric combination and based on the calculated results of gray relational grade, it is revealed that improvement in the value of gray relational grade at predictive setting is 0.054 compared to the initial parametric combination.

Multi-performance Optimization of the Rotary Turning Operation for Environmental and Quality Indicators

In this investigation, two environmental metrics (the comprehensive energy used (TU) and turning noise (TN)) and a quality metric (surface roughness (SR)) of the rotary turning process for the Ti6Al4V were optimized and reduced using the optimal factors (the inclined angle-i, depth of cut-d, feed-f, and turning speed-V). The TU model was proposed comprising the embodied energy of the insert and lubricant. The method based on the removal effects of criteria (MEREC), an improved quantum-behaved particle swarm optimization algorithm (IQPSO), and TOPSIS were applied to select weight values and the best optimal solution. The machining cost (MC) was proposed in terms of process parameters. The outcomes presented that the optimal values of the i, d, f, and V were 35 deg., 0.30 mm, 0.40 mm/rev., and 190 m/min, respectively, while the TU, SR, TN, and MC were saved by 6.7 %, 22.3 %, 23.5 %, and 8.5 %, respectively. The turning responses were primarily affected by the feed rate and turning speed, respectively. The developed turning process could be employed for machining hard-to-cut alloys. The developed approach could be applied to deal with optimization problems for other machining operations.

Response surface methodology based synchronous multi-performance optimization of CMOS low-dropout regulator

An easy-to-use efficient auto-design approach to achieve a synchronous optimization and solution for multiple performance objectives of a CMOS low-dropout regulator (LDO) circuit is proposed. As a core algorithm, response surface methodology (RSM) is adopted to automatically implement the design parameter modeling and solving of the LDO. The precision response surface models for the temperature coefficient (TC) and power supply rejection ratio (PSRR) are established efficiently based on only 27 sets of Cadence sampling datasets, by which the optimal performance solution with a superior tradeoff on TC and PSRR can be obtained. Along with a complete auto-design flow, the pre/post-layout simulations of the LDO circuit by SMIC 180 nm/3.3 V CMOS technology are performed, it can be observed that the auto-improved LDO has a significant better 35.20 ppm/°C TC feature compared with 48.46 ppm/°C that recorded by the manual design. Synchronously, the PSRR is improved from −59.497 Hz@DC to −92.89 Hz@DC with a great increase ratio by 56 % up.

Study on multi-performance optimization of basalt stone powder supplementary cementitious materials

In order to better apply basalt powder to supplementary cementitious materials (SCMs), this paper chooses the content of basalt stone powder, fly ash and slag as the influencing factors, and the 7d drying shrinkage rate, 28d drying shrinkage rate, fluidity, 7d and 28d compressive strength of mortar as the evaluation indexes of SCMs. Based on the response surface and multi-objective optimization method, the Box-Benken experimental design method was used to study the influence of the physical and chemical superposition of various factors on the properties of supplementary cementitious materials and optimize the ratio. The low-field nuclear magnetic resonance technique was used to detect the water change and porosity difference between the reference group and the optimization group at each age. The results show that the linear interaction and interaction of basalt powder, fly ash and slag have an effect on the strength, fluidity and drying shrinkage of cementitious materials. The optimal mixing ratio of basalt stone powder supplementary cementitious material is stone powder (10.8 %), fly ash (5 %) and slag (22.69 %). Under this mix ratio, the properties of basalt powder-assisted cementitious materials have been improved and close to the reference group. In the early hydration process, the physical filling effect of supplementary cementitious materials such as stone powder can better optimize the pore structure inside the slurry and the water loss is small, the strength is improved slowly, the dry shrinkage pure cement sample is similar, and the fluidity is improved. With the extension of hydration time, the water loss of the optimized group increased, and the strength and drying shrinkage increased.

Experimental investigation and multi-performance optimization of the leachate recirculation based sustainable landfills using Taguchi approach and an integrated MCDM method

Landfill leachates contain harmful substances viz. chemicals, heavy metals, and pathogens, that pose a threat to human health and the environment. Unattended leachate can also cause ground water contamination, soil pollution and air pollution. This study focuses on management of leachate, by recirculating the rich, nutrient-filled fluid back into the landfills, turning it to a bioreactor, thereby maximising the performance parameters of landfills favourable for electricity production by the waste to energy plants. This study demonstrates a sustainable alternative method for utilising the fluid, rather than treating it using an extremely expensive treatment process. Further, it also experimentally investigates the effect of varying levels of five input parameters of the landfill including waste particle size, waste addition, inorganic content in waste, leachate recirculation rate, and landfill age, each at five levels, on the multiple performance of the landfill using Taguchi’s L25 standard orthogonal array. Experimental results are analysed using an integrated MCDM approach i.e. MEREC-PIV method and statistical techniques such as analysis of mean (ANOM) and analysis of variance (ANOVA). The results indicate that the optimal setting of the input parameters is waste particle size at 9 ppm, waste addition at 80 Ktoe, inorganic content in waste at 2%, leachate recirculation rate at 250 l/day and landfill age at 3 years. Further, inorganic content waste is found to be the most significant parameter for the multiple performance of the landfill. This study presents a novel approach to produce input parameters for power plants which may enhance their profitability and sustainability.

Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm

PurposeThe goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.Design/methodology/approachIn the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.FindingsThe suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.Originality/valueThe findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

Sustainable Dry Machining of Stainless Steel with Microwave-Treated Tungsten Carbide Cutting Tools.

This paper presents a research investigation conducted on the turning of stainless steel 316 material under a dry environment using microwave-treated cutting tool inserts. Plain tungsten carbide WC tool inserts were exposed to microwave treatment for enhancement of their performance characteristics. It was found that a 20-min microwave treatment resulted in the best tool hardness and metallurgical characteristics. These tool inserts have been used to machine SS 316 material following the Taguchi L9 design of experimental techniques. A total of eighteen experiments have been conducted by varying three main machining parameters, i.e., cutting speed, feed rate, and depth of cut, at three levels per parameter. It has been found that tool flank wear increased with all three parameters and surface roughness decreased. At the longest dept of cut, surface roughness increased. An abrasion wear mechanism was found on the tool flank face at a high machining speed and adhesion at low speed. Chips with a helical shape and low serrations have been investigated. Turning SS 316 at optimum machining parameters of 170 m/min cutting speed, 0.2 mm/rev feed rate, and 1 mm depth of cut, as obtained by the multiperformance optimization technique grey relational analysis, resulted in the best values of all machinability indicators: 242.21 µm tool flank wear, 3.81 µm mean roughness depth, and 34,000 mm3/min material removal rate, at a single parameter setting. In terms of research achievements, the percentage reduction in surface roughness is approximately 30% and represents an almost ten-fold improvement in the material removal rate. The combination of machining parameters of 70 m/min cutting speed, 0.1 mm/rev feed rate, and 0.5 mm depth of cut is optimum for the lowest value of tool flank wear when considered for single parameter optimization.

Multi-performance optimization of the diamond burnishing process in terms of energy saving and tribological factors

Surface characteristics can be improved using diamond burnishing. In this work, an ecological indicator (energy efficiency- EF) and tribological factors (the coefficient and friction- COF and specific wear rate- SWR) of a single diamond burnishing process were optimized. The process parameters were the burnishing speed ( V), burnishing depth ( D), feed rate ( f), the diameter of the tip ( DT), and initial average roughness ( IR). An efficient cooling-lubrication device comprising the minimum quantity lubrication and Ranque–Hilsch vortex tubes was utilized to facilitate burnishing trials. The Kriging models of burnishing performances were developed regarding parameters. The CRITIC method, Crow Search Algorithm, and TOPSIS were employed to select weights and optimality. The optimizing outcomes indicated that optimal values of the V, D, f, DT, and IR were 160 m/min, 0.10 mm, 0.05 mm/rev., 7 mm, and 2.00 μm, respectively. The EF was improved by 34.1%, while the COF and SWR were decreased by 33.2% and 8.1%, respectively. The optimal data could be applied to practical diamond burnishing to improve energy efficiency and tribological parameters for burnished external surfaces. The optimizing technique comprising the Kriging models, CRITIC, Crow Search Algorithm, and TOPSIS could be efficiently utilized to solve complex issues for burnishing operations and other machining processes. A developed system could be applied to enhance cooling-lubrication efficiencies for different machining operations, such as turning, milling, and grinding processes.

Fabrication and multi- performance optimization of mechanical properties of novel Babbitt-Ilmenite metal matrix composite using entropy based hybrid Taguchi-grey relational analysis

In the present study, a Babbitt-Ilmenite composite was developed via stir casting and the mechanical properties (hardness and tensile strength) of the composite were optimized using the Taguchi philosophy. The effects of variations in the percentage weight of Ilmenite, stirring speed and aging time on the mechanical properties of the composite were studied. Energy dispersive spectroscopy analysis confirmed the presence of Ilmenite in the metal matrix and uniform dispersal of Ilmenite in Babbitt was established via scanning electron microscopy. A Morphological analysis of the fractured tensile specimen was conducted to ascertain the type of failure. The Taguchi methodology and analysis of variance technique were proposed to predict the optimum levels of controllable parameters and to determine their percentage contribution to the optimization of mechanical properties of the composite. The percentage weight of Ilmenite and stirring speed were found to have significant effect on hardness while aging time was a significant parameter for the tensile strength of the composite. The predicted results were validated by confirmation experiments with a deviations of 1.6% and 2.1% for hardness and tensile strength respectively for single objective optimization. The weights of the responses were determined objectively and the multi-performance optimization of mechanical properties of the composite was performed using entropy based grey relational analysis (EGRA). The percentage weight of Ilmenite was only significant parameter with contribution of 30.65% for multi-performance optimization of mechanical attributes of composite using hybrid Taguchi grey relation analysis (TGRA). An overall improvement of 0.445 and 0.105 in the grey relation grade was reported using EGRA and TGRA respectively. The study concludes that the Babbitt/Ilmenite composite has superior mechanical properties both for single and multi-objective optimized conditions than the unreinforced tin Babbitt. The developed composite can be used for bearings in crankshafts, connecting rods and axles of automobiles.

Investigation and Optimization of Tribological Aspects of Babbitt-Ilmenite Composite using Weighted Grey Relation Analysis

Babbitt-Ilmenite composite prepared via stir casting method with controllable parameters which include percentage weight of Ilmenite, stirring speed and aging time was investigated for its sliding wear characteristics and coefficient of friction (COF) with varying applied load, sliding velocity and sliding distance. Morphological tests were conducted for characterization of the composite and to ascertain the wear mechanism under mild and severe conditions. Taguchi philosophy with analysis of variance (ANOVA) statistical technique was used for multi-objective optimization of the tribological characteristics of the composite. The entropy based grey relation analysis (EGRA) performed for weighted multi-performance optimization of tribological aspects of the composite and hybrid Taguchi grey relation technique (TGRA) confirmed that the percentage weight of Ilmenite, applied load, sliding distance and stirring speed were the significant parameters with regard to multi - criteria optimization with contributions of 44.46%, 23.82%, 11.33%, and 5.66%, respectively. There is an overall improvement in wear resistance and COF at optimal TGRA conditions. Babbitt-Ilmenite composite can be used in steel slide bearing bushings for power, automobile and aerospace.

Multi-Performance Optimization of the Mechanical Characteristics of Basalt Fiber and Silicon Carbide-Filled Aluminum Matrix Composites

In the existing state, aluminum metal matrix composites (AlMMCs) are a category of materials that have successfully fulfilled the majority of demanding requirements in applications where moderate strength, high stiffness, and lightweight are necessary. This paper is focused on processing aluminum hybrid composites by reinforcing the aluminum alloy with a novel combination of fillers: basalt fibers and silicon carbide via stir casting. The main aim is to study the impact of processing conditions on the properties of the developed composite. Nine samples are produced by varying the reinforcement content, stirring rate, and duration based on the L9 Taguchi Array. SEM analysis is utilized to examine the microstructure of the developed composites. The samples were also machined and tested for their mechanical, physical, and wear behavior as per ASTM standards. The maximum density and hardness of 2883.3 kg/m3 and 45.6 HRB, respectively, are observed at higher filler content conditions. In contrast, the minimum specific wear rate, maximum ultimate tensile, and impact strength of 1.86·10–5 mm3/(N·m), 263.5 MPa, and 93 N/mm are observed in higher stirring duration conditions. So, to avoid conflicting combinations of optimal input factors, grey relational analysis (GRA) tied with principle component analysis (PCA) is employed to determine the multi-objective performance parameter and the optimal combination of input factors for better response. Confirmatory tests were also performed to verify and validate the same. ANOVA analysis is also utilized to assess the significance of the process parameters on the responses.

Mathematical Modeling of Multi-Performance Metrics and Process Parameter Optimization in Laser Powder Bed Fusion

This study aims to develop mathematical models to improve multi-performance metrics, such as relative density and operating costs, in laser powder bed fusion (LPBF), also known as selective laser melting, a metallic additive manufacturing technique, by optimizing the printing process parameters. The work develops a data-driven model for relative density based on measurements and an analytical model for operating costs related to the process parameters. Optimization models are formulated to maximize relative density or minimize operating costs by determining the optimal set of process parameters, while meeting a target level of the other performance metrics (i.e., relative density or operating costs). Furthermore, new metrics are devised to test the sensitivity of the optimization solutions, which are used in a novel robust optimization model to acquire less sensitive process parameters. The sensitivity analysis examines the effect of varying some parameters on the relative density of the fabricated specimens. Samples with a relative density greater than 99% and a machine operating cost of USD 1.00 per sample can be produced, utilizing a combination of low laser power (100 W), high scan speed (444 mm/s), moderate layer thickness (0.11 mm), and large hatch distance (0.4 mm). This is the first work to investigate the relationship between the quality of the fabricated samples and operating cost in the LPBF process. The formulated robust optimization model achieved less sensitive parameter values that may be more suitable for real operations. The equations used in the models are verified via 10-fold cross-validation, and the predicted results are further verified by comparing them with the experimental data in the literature. The multi-performance optimization models and framework presented in this study can pave the way for other additive manufacturing techniques and material grades for successful industrial-level implementation.

A Sampling-Based Motion Assignment Strategy With Multi-Performance Optimization for Macro-Micro Robotic System

This article proposes a sampling-based motion assignment strategy for coordinated motion planning of macro-micro robotic systems. It is used to achieve performance enhancements while solving joint trajectories. The sampling strategy is implemented by traversing a series of feasible sets generated by the trajectory constraints of the micro robot. Meanwhile, two kinds of performance index maps are introduced to achieve normalization and integration of multiple performance indices. They are used for iterative generation of trajectories and overall performance evaluation, respectively. Comparative numerical results prove the validity of the proposed strategy.

Multi-performance optimization of static shading devices for glare, daylight, view and energy consideration

Exterior static shading devices of buildings, if correctly designed, can control sunlight and daylight to reduce glare while guaranteeing interiors' natural illumination and connection to the outside which increase occupants' physiological and psychological wellbeing. The shadings can also control solar radiation, which has strong influence on buildings' energy use. Most of the existing research investigated the effects of static shadings on either energy use, or daylight availability and distribution or glare using single-view assessments. A number of studies integrated the analysis of two or the three performances. Very few analyzed the effects on view out. This research investigates through simulations the potential of exterior static shadings in reducing disturbing glare, and the effects on daylight provision, view out, and energy use in two classrooms in the northern city of Tallinn, Estonia. The novelty resides in the integration of the four performances analysis through a multi-objective optimization workflow, allowed by a spatial glare assessment method. Results showed that, for the building type and location, static shadings reduced visual discomfort by up to 89.8% while reduced primary energy use by up to 29.1% and provided adequate levels of daylight and view out. The most performative shading types and detailed results are presented and discussed.

Multi-performance optimization on hard-turning for improving the product quality of high-chromium stainless steel

As steel continues to expand its current range with stronger, more ductile automotive brands, automakers are turning their attention to this. The future of vehicle weight reduction does not require a shift to alternative material systems and a complete overhaul of existing manufacturing infrastructure. When it comes to enhancing surface quality and dimensional accuracy in hardened steel, hard turning technology has been shown to be quite effective. In order to do this, it is employed in some important aviation components where the material hardness surpasses 45 HRC, such as 300 M ultra high strength steel, for finishing operations. In this context, this study focuses on optimizing cutting conditions in hard turning to reduce surface roughness, roundness, energy consumption, tool wear, and increase MRR. To this end, an experimental design (DoE) is used to study the effect of specimens with processing conditions such as cryogenic, heat-treated and obtained, cutting speed (V), feed rate (f), depth of cut (D), and heat treatment for hard turning. The L9 orthogonal matrix was performed using multiple response gray-relational analysis (GRA), and ANOVA was performed with individual characteristics and their effects.

Vibration Analysis Using Vibration Coupling Power Transmissibility of Two-degree-of-freedom System

In recent years, there has been a growing demand in the manufacturing field for more efficient product design and development, and for the development of more valuable products. To achieve this, it is necessary to have technologies that can achieve both diverse performance at an early stage of design, such as by eliminating "Temodori," as well as technologies that encourage the creation of new value. The models in “1D CAE” and “MBD” are useful as the technology. We have been studing in utilizing these technologies for designing highly efficient, Temodori-free designs through multi-performance optimization at an early stage of design, and creating new value in the process. In this paper, energy transfer characteristics（CLF）between two masses of two degree of freedom vibration system are newly derived. And the interpretation of vibration phenomena based on the CLF and its application to design are demonstrated. Finally, an example of vibration reduction of an automobile engine shake is discussed in comparison with the results of a reference.

A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

Abstract In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R2 = 0.9893 and R2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

Allocation Optimization of Multi-Axis Suspension Dynamic Parameter for Tracked Vehicle

The dynamic parameter allocation of the suspension system has an important influence on the comprehensive driving performance of the tracked vehicle. Usually, the allocation of suspension parameters is based on a single performance index, which has the disadvantage of not being able to achieve multi-performance optimization. Therefore, a novel optimization method using multi-performance index-oriented is presented. Firstly, considering the vertical vibration excitation caused by road roughness, the input (excitation) model of road roughness is embedded to establish the parametric dynamic model of the tracked vehicle. Then, the evaluation index and its quantitative algorithm, which reflect the multi-aspect performance of the suspension system, are proposed. Moreover, the parameter allocation objective function based on multi-index information fusion is designed. Finally, two allocation optimization methods are presented to solve the parameter allocation, i.e., equal weight allocation and expert knowledge-based weight allocation. By comparing the results obtained by the two methods, it is found that the performance of the suspension system can be improved effectively by optimizing the parameters of suspension stiffness and damping. Furthermore, the optimization of weight allocation based on expert knowledge is more effective. These provide a better knowledge reference for suspension system design.

Multi-performance optimization in wire EDM of Inconel 617 using GRA and genetic algorithm

Inconel 617 superalloy is a candidate material for various high temperature components employed in ultra-supercritical power plants. Superalloys are hard to machine materials that easier to process using nontraditional methods such as wire electrical discharge machining. This paper discusses multi performance optimization in machining of Inconel 617 using grey relational analysis and genetic algorithm. The performance characteristics of wire cut EDM are surface roughness, material removal rate, wire wear rate, form and orientation tolerances. Parameters under consideration are pulse on time, pulse off time, current, voltage, flushing pressure, wire tension, table feed and wire speed. The performance measures are consolidated as three performance indices using the grey relational grade. The mathematical models to estimate the performance objectives are developed using regression analysis. Multiobjective optimization is applied through genetic algorithm and a pareto front containing the optimized responses is computed. The optimized parameters are validated experimentally and the results are reported.