Optimal Setting Of Process Parameters Research Articles

Process window prediction in stainless steel selective laser melting using various energy densities: laser power, scan speed, and defocusing distance

Abstract The process window of selective laser melting (SLM), a set of optimum process parameters, is crucial for producing defect-free components with excellent mechanical properties. This study aims to predict the optimum process window for SLM of stainless steel by varying the defocusing distance (f) which changes the laser beam diameter (d) and using laser power (P) and scan speed (V) as process parameters. The process window was predicted using empirical formulae related to the energy density equations, instead of the conventional approach based on simple experimental results. To predict the process window, we analyzed the melt pool geometry of components with different features, such as depth (D), width (W), layer thickness (t), and hatch distance (h). Using the energy density equation, we correlated the effect of these process variables on the melt pool geometry and derived empirical equations. The upper limit of the process window (D/W) was strongly correlated with local applied energy and expressed as P ≤ 34Vd2. The lower limits, D/t and W/h, showed good correlation with linear energy density and laser energy density, respectively, and expressed as P > 2.16Vd and P < 0.13V. Finally, we used these empirical equations to predict the process window, which was experimentally verified.

Influence of welding environment on joint characteristics of friction stir lap welded AA7475-PPS polymer hybrid joints

This study explores the impact of air and underwater welding environments during friction stir lap welding (FSLW) of AA7475 and Polyphenylene-sulfide (PPS) polymer dissimilar materials. Response surface methodology-based desirability function approach was used to identify the optimal FSLW process parameter set in an underwater environment. The joint characteristics, such as microstructure, mechanical properties of the joints, and physicochemical changes in the polymer with the air and underwater FSLW, were compared. The maximum tensile lap shear strength was attained as 12 MPa and 19 MPa in the air and underwater weld environment, respectively. Microstructural examination revealed that the bonding mechanism in FSLWed hybrid joint in both weld environment conditions is macro/micromechanical interlocking. Based on the findings of a Differential Scanning Calorimetry study, the FSLW technique causes the loss of crystallinity in the polymer. The study found that the welding environment significantly impacted air voids formation, tensile characteristics, and loss of crystallinity in polymers.

Research on warping optimization of automobile mesh based on artificial intelligence algorithm

Automobile mesh is a typical large plastic part prone to warping deformation in the manufacturing process, which seriously affects the quality of products. The traditional method is to find the optimal combination parameters to reduce warping deformation in the forming process through repeated trial or mold flow analysis with high cost. In this study, An optimization method based on an artificial intelligence algorithm is proposed to optimize the warping deformation of the automobile mesh. In the optimization method, artificial intelligence regression algorithm-support vector machine regression was used to establish the nonlinear function relationship (prediction function) between the molding process parameters and the warpage of automobile mesh, and the performance of the prediction function was optimized by a genetic algorithm. The optimized prediction function was taken as the fitness function, the warping value was regarded as the fitness value, and genetic algorithm is used again to obtain a set of optimal process parameters and minimum warpage values. Finally, the reliability of the optimization method is verified by the modal flow analysis software.

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

The friction stir spot welding (FSSW) process is a novel technique that overcomes the limitation of resistance spot welding. Recently, FSSW used for welding of polymers which are difficult to be joined by traditional welding processes. The demand for Acrylonitrile Butadiene Styrene (ABS) for industrial applications has increased in recent years. However, to employ this technique the challenge is to get optimal FSSW parameters setting to achieve the best weld strength during the welding of ABS sheets. To achieve this, in the present work, full factorial experimental design layout was employed to investigate the effect of process parameters on weld strength i.e., ultimate tensile strength (UTS) and percentage elongation during FSSW of ABS-ABS sheet in butt configuration. To predict the UTS and percentage elongation, machine learning regression namely, linear, polynomial, support vector machine, and decision tree was used. Further, the study includes the identification of the fracture patterns post tensile test specimens based on the topography of the fracture surface under scanning electron microscopy. It was found that plunge depth is the most significant parameter followed by spindle speed and dwell time. The optimal setting of process parameters i.e., spindle speed of 1000 rpm, plunge depth of 1 mm, and dwell time of 40 s resulted in maximum UTS of 7.849 MPa. The maximum value of percentage elongation obtained was 5 at the parameter setting of spindle speed of 1000 rpm, plunge depth of 0.8 mm, and dwell time of 40 s. Polynomial regression outperformed in the prediction of UTS and percentage elongation with an R-square of 0.99.

Assessing the wear performance of 3D printed polylactic acid polymer parts

Three-dimensional (3D) printing technology is an advanced manufacturing method used for the rapid manufacturing of functional products in industrial and biomedical fields. The study aims to evaluate the wear rate of the polylactic acid polymer by determining the optimum parameters for 3D printing. The process parameters were chosen, such as extrusion temperature, fill density, and nozzle speed. Taguchi L9 array was used to frame experiments and optimized the parameters to achieve the minimum wear rate. The samples were printed using the fused deposition modeling (FDM™) technique, also known as fused filament fabrication (FFF), and the wear rate was measured using a pin-on-disc apparatus. From work, it was concluded that the fill density significantly affected the wear rate, followed by extrusion temperature and nozzle speed. At a fill density of 100% and an extrusion temperature of 220 °C, the nozzle speed at 40 mm/s was the optimum set of process parameters.

Experimental investigation on microhardness, surface roughness, and white layer thickness of dry EDM

In this research, the environment-friendly dry electrical discharge machining (EDM) process is investigated to improve the microhardness, surface finish, and white layer thickness of the machined surfaces using graphite-argon gas as a dielectric medium. The graphite powder, mixed with compressed argon gas, has been used to replace the existing dielectric medium in the EDM process. Gas pressure, discharge current, pulse width, and gap voltage were working as input parameters to reduce surface roughness and enhance the microhardness and white layer thickness. The Taguchi L16 orthogonal array is applied to the design and analysis of the experimental results. The minimum surface roughness (2.23 μm) of the HN31 steel has been attained by increasing the gas pressure up to 1.0 MPa and the minimum values of pulse width (40 μs), gap voltage (40 V), and discharge current (6 A). The maximum microhardness (501.04 HV) has been obtained at 1.2 MPa of gas pressure, 120 μs of pulse width, 60 V of gap voltage, and 18 A of discharge current. The maximum white layer thickness (16.24 μm) is achieved by the maximum values of gas pressure (1.2 MPa), pulse width (160 μs), gap voltage (70 V) and discharge current (18 A). The SEM analysis had been done to reveal the white recast layer thickness and surface roughness of the machined surfaces of the dry EDM process. The SR is increased by the recast layer, pores, and microcracks on the machined surfaces. Finally, the multi-criteria optimization technique: Weight Product Method (WPM) is applied to predict optimum process parameter settings: GP: 1.2 MPa, PW: 120 μs, GV: 50 V, and DC: 18 A to meet the best machining performances (MH = 493.32 HV, WLT = 14.28 μm, and SR = 3.82 μm). The validation tests were done to confirm the predicted results obtained by both the Taguchi and WSM methods.

Development and optimization of a novel industrial process solution for stripping of carbon dioxide and ammonia from bio-process wastewater

The production of biologic products by fermentation has grown significantly within the last decade and continues to grow further. Large volumes of wastewater are produced by anaerobic digesters, which contain dissolved CO2 and NH3 in addition to other compounds, making it difficult to treat in an economic way. In this work, we have developed a novel process solution of an NH3 recovery process that first strips off CO2 from the wastewater, then produces a concentrated NH3 solution. This solution not only avoids both precipitation of solids and the use of caustic, but also allows for adjustments of the composition of the concentrated NH3 solution, thus enabling its re-utilization in different applications. Based on this development, pilot plant tests were conducted with the aim of proving process feasibility. Then, a rigorous process optimization was carried out, where the optimal set of process parameters were determined. The results suggest that the optimal operation not only depends on the operating conditions, but also on the process configuration depending on the input and storage conditions; four different configurations were developed, which enable the selection of an optimal design that minimizes both the operating costs via external heat demand, and capital costs via process complexity.

Impact of process parameters on the specific volume of wholemeal wheat bread made using sourdough- and baker's yeast-based leavening strategies

The final quality of wholemeal wheat bread is determined by the process parameter settings and leavening strategy. We hypothesise that the used leavening strategy may influence the optimal process parameter settings and, as such, the specific volume of the bread loaf. To analyse this interaction, bread was leavened with (i) a type 1 sourdough (SB), (ii) a type 1 sourdough combined with baker's yeast (YSB), or (iii) baker's yeast (YB). For each leavening strategy, the specific volume of bread, in response to variations in mixing time (4–10/4–14 min), water absorption (60–85 %), and proofing time (1–7/1–3 h), was analysed using an I-optimal response surface experimental design. Data modelling identified a substantially lower maximal specific volume of SB (2.13 mL/g), compared to YSB (3.30 mL/g) and YB (3.26 mL/g). The proofing time and water absorption mostly influenced the specific volume of the SB and YSB, respectively. However, the mixing and proofing times mainly affected the specific volume of YB. The type 1 sourdough reduced the mixing time and water absorption required for an optimal specific volume of bread compared to baker's yeast. These results challenge the idea of yielding higher volumes upon using sourdough compared to baker's yeast and highlight the importance of optimisation of bread dough formulations and breadmaking processes.

Functional dyeable polypropylene fabric development and process parameter optimization. Part II: Development of graphene thermal insulation dyeable polypropylene fabric with process parameter optimization

This series of studies developed a nanographene modified dyeable polypropylene yarn with far-infrared properties making it suitable for winter clothing fabrics. In this paper, part II, the dyeable polypropylene granules developed in part I are used as a base material and, with the addition of graphene nanopowder, melt-mixed and melt-spun to produce 75 d/24 f fully drawn yarn. The physical properties of the yarn, namely, yarn count, tensile strength, elongation at break, far-infrared emissivity, and far-infrared temperature rise, are investigated, and the impact of the melt-spinning process parameters, namely, graphene nanopowder content, mold temperature, melt temperature, gear pump speed, hot roller speed and take-up speed on the quality of the yarn, is determined. The Taguchi method, combined with gray relational analysis, is used to design experiments through which an optimal set of melt-spinning process parameters maximizing the multi-characteristic quality of the yarn is obtained. This optimized nanographene modified dyeable polypropylene yarn has a tensile strength of 3.5 g/d, elongation at break of 41.0%, yarn count of 75.3 d, far-infrared emissivity of 82%, far-infrared temperature rise of 21.0°C, washing fastness grade of 3–4 and surface resistance of 3 × 108 Ω.

Effect and optimization of tribological parameters upon wear responses of TiAlN coating

This study investigated the relative effects of tribological process parameters and their optimization for wear responses of a TiAlN thin film deposited on heat-treated and plasma-nitrided novel MDC-K hot work tool steel. The effects of tribological process parameters were studied by conducting a tribological test on the TiAlN thin film against a tungsten carbide (WC) counterbody. The researchers considered three tribological process parameters – namely, sliding velocity, applied load and sliding distance – to conduct the tribological test and study the effect of the parameters on five different wear responses – namely, friction coefficient, surface roughness (R a), wear depth, wear mass loss and hardness. The relative effects of the process parameters were studied using contour plots, and the individual effects and contributions were analyzed using an analysis of variance test. Further, the overall evaluation criteria method was employed to select the optimal settings of tribological process parameters based on the experimental results. Finally, the surface morphology of the worn TiAlN surface against the best and worst parametric settings was evaluated using scanning electron microscopy integrated with energy-dispersive X-ray spectroscopy to investigate the underlying wear mechanisms.

Multi-Objective Optimization of Fused Deposition Modeling for Mechanical Properties of Biopolymer Parts Using the Grey-Taguchi Method

The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the on-demand necessity to perform surgery during space missions. Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing. Among all 3D printing techniques, fused deposition modelling (FDM) is a low-cost and more rapid printing technique. This article proposes the fabrication of surgical instruments, namely, forceps and hemostat using the fused deposition modeling (FDM) process. Excellent mechanical properties are the only indicator to judge the quality of the functional parts. The mechanical properties of FDM-processed parts depend on various process parameters. These parameters are layer height, infill pattern, top/bottom pattern, number of top/bottom layers, infill density, flow, number of shells, printing temperature, build plate temperature, printing speed, and fan speed. Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid (PLA) parts printed by FDM. The experiments have performed through Taguchi's L27 orthogonal array (OA). Variance analysis (ANOVA) ascertains the significance of the process parameters and their percent contributions to the evaluation indexes. Finally, as a multi-objective optimization technique, grey relational analysis (GRA) obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties. Scanning electron microscopy (SEM) examines the types of defects and strong bonding between rasters. The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength (42.6 MPa) and modulus of elasticity (3274 MPa).

Measurement of pressure–volume–temperature diagrams based on simulated melt temperature and actual cavity pressure

Pressure–volume–temperature (pvT) diagrams illustrate the behavior of a polymer melt during injection molding and can be used to determine the optimal process parameter settings for producing quality parts. However, the measurements of pvT diagrams are costly and time-consuming and have limitations for process control. This study generated an economic pvT diagram based on sensor data and simulation analysis. To avoid the use of expensive infrared sensors and inaccurate pressure simulations, a pvT curve was constructed by combining the melting temperature, which was simulated using computer-aided engineering, and the actual cavity pressure. The specific volume of the polymer melt was then calculated using a modified Tait model. The pvT diagram could be used to investigate the changes in specific volume at various injection stages, which could be applied to quality prediction. In this study, a flat plate with uneven thickness was used as the research vehicle. The specific volume of each sensor position of the plastic component cooled to room temperature was calculated. Thereafter, a correlation analysis between the changes in specific volume and the shrinkage ratio of the plastic part was conducted. The experiment indicated that both variables were highly correlated. Therefore, monitoring changes in specific volume could predict part size. Additionally, the proposed measurements of the pvT diagram could be used to compute the cooling time, which greatly affects the quality and production efficiency of injection-molded parts. A 25-s cooling time setting demonstrated a stable molding quality compared with the traditional heat transfer formula (16 s) and the commercial simulation (17 s).

Improving the sustainability of porcelain tile manufacture by flowsheet simulation

Porcelain tiles manufacturing is energy-intensive since it contains several stages in which the product is subjected to thermal treatments. The energy used in the process mainly comes from the combustion of natural gas, pushing the industry to decrease its consumption to upkeep its competitiveness in the coming targets of modern sustainable policies. This work presents a simulation-based strategy for the holistic optimization of energy consumption in the tile manufacturing process. Mechanistic models for all involved processing units, describing the respective thermal and electrical energy consumptions, have been implemented in the Dyssol framework and validated against available experimental data. The energy consumption optimization procedure was performed using a coupled Dyssol-Matlab framework. The proposed cost function, which considered electric and thermal energies, was used as the main criterion to determine the optimal design set of process parameters. Various optimization approaches to minimize the overall cost function and the required computational time have been compared. The proposed strategy allows the computing of improved process parameters resulting in minimization of energy consumption and CO2 emissions while keeping product quality intact. Among the solutions, a reduction of 30.2% of fuel per ton of fired tile can be obtained with modification of the processing parameters.

Effect of heat treatment process parameters on the microstructure and properties of GH4720Li superalloy

The solution temperature and aging temperature of GH4720Li superalloy are the critical factors in determining its microstructure stability and mechanical properties. However, the matching of solution temperature, primary aging temperature, and second aging temperature greatly influences the final microstructure and mechanical properties, and the mechanism was rarely studied. In this paper, the effects of solution temperature, aging temperature, and other process parameters on the grain size, precipitated phase, and mechanical properties of GH4720Li superalloy were systematically studied by orthogonal experiment, and a set of optimal heat treatment process parameters were found. The results show that the sample was solution treated in the temperature range of 1060 °C ∼ 1120 °C. When the solution temperature was 1160 °C ∼ 1100 °C, although the primary γ ′ phase gradually dissolved, the secondary γ ′ phase gradually increased, and the primary γ ′ phase was pinned at the grain boundary to hinder the grain growth, and the hardness of the alloy gradually increases. When the solution temperature exceeded 1100 °C, the primary γ ′ phase dissolved in large quantities, the grains grew up rapidly, and the hardness of the alloy decreased. The sample was subjected to two-stage aging treatment in the temperature range of 650 °C ∼ 770 °C and 760 °C ∼ 880 °C. As the aging temperature increased, the primary γ ′ phase of the sample gradually grew, and the ability to pin the grain boundary weakened. The volume fraction of the secondary γ ′ phase of the sample gradually decreased and coarsened, resulting in grain growth and a gradual decrease in the hardness of the sample. When the samples were treated by 1100 °C/OC × 4 h + 650°C /AC × 8 h + 760 °C/AC × 8 h, the grain size of the sample was the smallest, the average grain size was 4.5 μm, the distribution of γ ′ phase was the most uniform, and the mechanical properties are the best, reaching 47 HRC.

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.

Fabrication of Copper of Harmonic Structure: Mechanical Property-Based Optimization of the Milling Parameters and Fracture Mechanism.

A severe plastic deformation process for the achievement of favorable mechanical properties for metallic powder is mechanical milling. However, to obtain the highest productivity while maintaining reasonable manufacturing costs, the process parameters must be optimized to achieve the best mechanical properties. This study involved the use of response surface methodology to optimize the mechanical milling process parameters of harmonic-structure pure Cu. Certain critical parameters that affect the properties and fracture mechanisms of harmonic-structure pure Cu were investigated and are discussed in detail. The Box-Behnken design was used to design the experiments to determine the correlation between the process parameters and mechanical properties. The results show that the parameters (rotation speed, mechanical milling time, and powder-to-ball ratio) affect the microstructure characteristics and influence the mechanical performance, including the fracture mechanisms of harmonic-structure pure Cu specimens. The best combination values of the ultimate tensile strength (UTS) and elongation were found to be 272 MPa and 46.85%, respectively. This combination of properties can be achieved by applying an optimum set of process parameters: a rotation speed of 200 rpm; mechanical milling time of 17.78 h; and powder-to-ball ratio of 0.065. The superior UTS and elongation of the harmonic-structure pure Cu were found to be related to the delay of void and crack initiation in the core and shell interface regions, which in turn were controlled by the degree of strength variation between these regions.

Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling

Aiming at the problems of large rolling deviation and low stability in limit specification of hot strip rolling, the optimal rolling suggestions were obtained based on back propagation (BP) neural network and genetic algorithm. According to equipment state and strip specification to select excellent sample set, in the sample set based on the data of application of neural network to build the mapping relationship between process parameters and the rolling stability, limit specifications of the mapping model is set up, and then using the genetic algorithm for the search of this mapping model, the search model of rolling stability of ideal point, determine a set of process parameters optimal advice accordingly. Taking the rolling of MRTRG00201_1276_3 as an example, a set of optimal process parameters are obtained by simulating rolling of MRTRG00201_1276_3. Then the sample distribution and rolling stability of each process are analyzed in turn. The results show that the process parameters obtained by optimizing the model accord with the distribution law of rolling samples, can obtain high rolling stability, and can play a guiding role in limit specification rolling.

Influence of cooling conditions on tensile lap shear strength and microstructure of friction stir welded aluminum alloy 5052-H32 and polycarbonate light weight hybrid joint

The joining of dissimilar materials, notably polymer and metal, is incredibly demanding due to the differences in the materials' mechanical, chemical, and thermal characteristics. In this present study, the feasibility analysis of dissimilar materials joints of aluminum alloy AA5052-H32 and polycarbonate (PC) was performed utilizing Friction Stir Welding (FSW) in the lap joint taper threaded tool pin profile. Response surface methodology (RSM) was implemented as a statistical design of the experiment tool for working process parameter optimization. Optical and Field emission scanning electron microscopy was employed to characterize the detailed microstructure of friction stir welded hybrid joint of AA5052-H32 alloy and PC. The mechanical and metallurgical findings demonstrate that AA5052-H32 and PC sheets were effectively bonded together utilizing a mix of macro mechanical interlocking and secondary chemical adhesive bonding of the materials, providing a viable relaxation to adhesive bonding or mechanical fastening. The hardness of the polymer in as-received conditions welded normally and in underwater conditions was measured onshore D hardness scale. The joint obtained at a rotational speed 1118 rpm and welding speed 48 mm/min with a 2° tilt angle had the maximum tensile lap shear strength of 14.86 MPa in air-cooled and 12.47 MPa in a water-cooled environment. Additionally, shore D hardness for optimal set of process parameters were 72 and 75 for air-cooled and water-cooled conditions respectively.

Study of Ultimate Tensile Strength and Wear Rate of Squeeze Cast Al-Si-Cu Alloy with DOM and TOPSIS Approaches

Aluminium alloys are widely used to produce aerospace and agricultural sector components. Squeeze casting (SQC) is a liquid forging process to enrich the mechanical characteristics of light metal alloy castings. Al-Si-Cu castings were fabricated through SQC process adopting Response Surface Methodology (RSM) following Box-Behnken experimental design. Fifteen castings were fabricated by varying input factors such as squeeze pressure, pouring temperature and dwell time. Optimal process parameter settings were predicted for the responses Ultimate Tensile Strength (UTS) and Wear rate by Desirability Optimization Methodology (DOM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The details of experiments and analysis are presented. The examination of microstructure and worn-out surface of squeeze castings indicated that the refi ned grain structure due to squeeze pressure application improved UTS and Wear resistance. Confi rmatory tests were conducted for settings suggested by DOM and TOPSIS. The setting suggested by DOM resulted in greater improvement. The value of UTS and wear rate predicted with DOM were verifi ed through confi rmatory tests and the measured values are in agreement with predicted values. An optimal improvement of 99.3% and 53.75% were achieved on ultimate tensile strength and wear resistance respectively in comparison with gravity die-cast components.

A Study on the Residual Stress of the Co-Based Alloy Plasma Cladding Layer.

The distribution law of residual stress in multi-channel scanned plasma cladding of Co-based alloy under different process parameters was studied by means of simulation and tests, and the optimum process parameters were optimized. The simulation model of the plasma cladding stress field was established by ABAQUS software, and the influence law of the working current, scanning speed, and scanning mode on the residual stress of the Co-based alloy multi-channel scanning was studied. A set of optimal cladding process parameters were obtained. The residual stress of the cladding layer was measured by the blind hole method and compared with the stress value in the finite element model. The results show that there is residual tensile stress on the surface of the cladding layer. The residual stress along the direction of the scanning path is greater than that along the direction of the scan sequence. The residual stress increases with the increase of the working current. The scanning speed is greater, and the residual stress is smaller. The residual stress of the short-edge scanning is greater than that of the long-edge scanning. The residual stress of the successive scanning is greater than that of the reciprocating scanning. The long-edge reciprocating scanning is the best scanning mode. The best combination of process parameters is the working current of 90 A, the scanning speed of 100 mm/min, and the long-edge reciprocating scanning mode.