Set Of Machines Research Articles

Towards optimization of sports bra strap design: the role of stitch pattern and stitch length (SL) on the properties of seamless knitted fabrics

The purpose of this study was to investigate the effect of SL and stitch patterns on seamless knitted fabric properties as applied to sports bra strap functionality. Different tuck stitch patterns commonly used for seamless knitted activewear, were knitted with a Santoni machine setting for stitch SL = 70. Textile testing was conducted to narrow down the patterns suitable for bra strap. Significant interactions between pattern and SL on thickness, weight, air permeability, elongation, and breaking force were found. The results confirmed some of the existing knowledge about the influence of tuck stitches on the dimensional properties of weft knitted fabrics, such as increasing their density and stability, but also revealed new insights for patterns with more complex tuck stitch repeats when varying SL. Moisture management measurements for various SL values proved to be critical in selecting the best stitch pattern (tuck pattern#10) and SL = 115 that could lead towards optimization of sports bra strap design and highlighted the inconsistent tuck pattern fabric behavior in the presence of moisture.

Price of Anarchy of Scheduling Games on Hierarchical Machines with Quadratic Social Cost

This paper studies the inefficiency of Nash equilibria (NE) for scheduling games on hierarchical machines with quadratic social cost. There is a set of hierarchical machines and a set of jobs. Each job can choose one machine from the set of machines that are permitted to process it, and the cost associated with the job is equal to the load of the selected machine. A schedule is an NE if no job can reduce its cost by ultimately moving to a different eligible machine. The social cost is the sum of squares of the loads of all the machines. We show that the Price of Anarchy for two, three and four machines is [Formula: see text], [Formula: see text] and [Formula: see text], respectively.

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

This study presents an approach that combines variational mode decomposition (VMD) and relevance vector machine (RVM) as a prediction technique for surface roughness during turning. The method helps machinists detect irregularities during machining, such as looseness in machine parts or tool wear. The study uses work hardened EN8 steel and extracts vibration signal features into five modes. PSO optimized RVM prediction models are developed using these modes with and without combining cutting parameters. The most sensitive mode is selected for accurate surface roughness prediction. The results show that the first mode of decomposition when combined with cutting parameters provides the least mean square error of 0.1095. Surface roughness was found to be primarily influenced inversly by cutting speed and directly by feed rate respectively. The signal with high surface roughness value has high amplitude noise scattered over the large frequency range. The first decomposed mode of vibration signal increases noise with a large amplitude as surface roughness increases. The approach can be integrated into a microcontroller to regulate machine settings based on vibration data.

Calculated purpose of the distance between the expansion joints of cement concrete pavements of roads

Problem. The article discusses current issues of increasing the efficiency and quality of construction of cement concrete pavements for highways. It is shown that the regulatory documents in force in Ukraine recommend reducing the distances between compression joints and expansion joints during the construction of roads made of cement concrete. However, such a reduction leads to a slowdown in the pace of road construction and a significant increase in construction costs. The increase in cost occurs due to frequent stoppages of a set of concrete-laying machines and the formation of a large amount of concrete waste. The cost of this waste can reach UAH 200 thousand per 1 kilometer of road. In addition, frequent stops of a set of machines when laying cement concrete pavements of highways slow down the time it takes to put roads into operation. Purpose of the work. The aim is to calculate the expansion of cement concrete pavement when heated under operating conditions. Methodology. The work used formulas for thermophysical calculations during thermal expansion of concrete. The paper presents the results of thermophysical calculations of the expansion of cement concrete pavements of highways during their operation at elevated temperatures. It has been shown that when there are a large number of compression joints, they can partially compensate for the space required for concrete expansion. At the same time, they can act as compression joints during low temperatures. A design of double expansion seams has been proposed, which allows increasing the distance between them. The joint work of compression and expansion seams makes it possible to increase the distance between expansion seams. Due to this, it is possible to reduce their number, while simultaneously increasing the speed of construction and maintaining the quality indicators of the coating concrete. Originality: The article presents the results of a production test of thermophysical calculations. It is shown that two experimental sections of road surfaces were built. The concept of the maximum value of thermal expansion of concrete was further developed. Practical value: On one of them, the distance between expansion joints was 101 m, and on the other – about 300 meters. In this case, the distance between the expansion joints in the double seam was 1.5 meters. These parameters were sufficient for efficient and high-quality operation of double expansion joints.

A generalized disjunctive graph model for a complex production problem

This work focuses on the modeling of the features of a real-world pharmaceutical production problem, where several limited resources must be gathered and scheduled in order to mix and process active substances. The problem can be modelled as a generalization of the job-shop scheduling problem, in which each job corresponds to an order. Besides the standard precedence and capacity constraints of a classical job-shop scheduling problem, release, no-wait and time interval production constraints are added. Furthermore, the processing of each job task must be assisted by an appropriate number of operators and performed by a specific machine, out of a set of feasible machines, that is temporarily installed in a compatible clean room. An innovative generalized disjunctive graph that models all the constraints and the resource conflicts is presented.

Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach

Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficiency attained by the acquired model. The training of supervised learning algorithms inherently adheres to predetermined loss functions during the optimization process. In this paper, we present a novel contribution to the realm of supervised machine learning: an asymmetric loss function named wave loss. It exhibits robustness against outliers, insensitivity to noise, boundedness, and a crucial smoothness property. Theoretically, we establish that the proposed wave loss function manifests the essential characteristic of being classification-calibrated. Leveraging this breakthrough, we incorporate the proposed wave loss function into the least squares setting of support vector machines (SVM) and twin support vector machines (TSVM), resulting in two robust and smooth models termed as Wave-SVM and Wave-TSVM, respectively. To address the optimization problem inherent in Wave-SVM, we utilize the adaptive moment estimation (Adam) algorithm, which confers multiple benefits, including the incorporation of adaptive learning rates, efficient memory utilization, and faster convergence during training. It is noteworthy that this paper marks the first instance of Adam’s application to solve an SVM model. Further, we devise an iterative algorithm to solve the optimization problems of Wave-TSVM. To empirically showcase the effectiveness of the proposed Wave-SVM and Wave-TSVM, we evaluate them on benchmark UCI and KEEL datasets (with and without feature noise) from diverse domains. Moreover, to exemplify the applicability of Wave-SVM in the biomedical domain, we evaluate it on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The experimental outcomes unequivocally reveal the prowess of Wave-SVM and Wave-TSVM in achieving superior prediction accuracy against the baseline models. The source codes of the proposed models are publicly available at https://github.com/mtanveer1/Wave-SVM.

Enhancing Textile Cotton Yarn Sizing: Investigating Factors Impacting Breaking Strength, Pick-Up Percentage, and Elongation Loss

ABSTRACT The paper investigates the effect of machine setting on breaking strength, breaking elongation, and pick-up percentage of sized yarn properties. The factors considered are concentration of size solution, squeezing pressure and counts of yarn, the aim to develop a regression model that shows the effect of factors on size pick-up percentage, breaking strength regain, and loss in breaking elongation. The experiment is conducted by using 16 Ne and 21 Ne of cotton yarn, 3 Bar and 5 Bar of squeezing pressure, and 1.9% and 6% of size concentration with maize starch as sizing agent. A factorial design with two factors and 5 replicants has been used to design the experiment and analyze the results. The result showed that a slight change in squeezing pressure and concentration significantly affects the pick-up percentage, breaking strength regain and breaking elongation loss while a change of yarn count would not have a significant effect on the pick-up percentage of sized yarn. Furthermore, for achieving optimum properties of the sized yarn the pressure of squeezing, the count of the yarn and the concentration of the solution should be adjusted, and a regression model is developed to investigate the properties of the sized yarn by considering considered factors.

#1944 How the patient characteristics and the prescription can be accounted for in patient-specific predictive HD models?

Abstract Background and Aims Compartmental models in HemoDialysis (HD) allow for understanding and predicting the patient-specific response to the treatment. These models, after training with consistent clinical data, can be adapted to each single patient through the estimation of a set of parameters. This work explores the relationship between the sets of estimated parameters and the anamneses of the enrolled patients, together with the settings of the HD sessions, and their time evolution. The finding of similar relationships will help properly set the models even avoiding the training phase. Method The data acquired during the INTERREG InterACTIVE-HD 2.0 observational longitudinal study, in partnership with 5 Italian and Switzerland hospitals (ASSTs Lariana, SetteLaghi, Valtellina e Alto Lario, Regional Hospital of Lugano, and Kantonsspital Graubünden of Chur Dialysis Units), were used (576 sessions). Clinical prescription, machine settings, and blood composition at the start, end, and every hour during the HDs were monitored over 7 months. The parametric multi-compartmental, multi-solute model optimized during the study was used. The patient-specific parameters are: Their default values (literature-based) are: Patient-specific parameters have been identified for each patient, by training the model with 3 session's data. Then the model can be used to predict the patient's response by knowing the clinical data at the beginning of the HD and setting the parameters equal to the mean of the 3 identified values. A correlation analysis between the patient-dependent parameters and the anamneses of the enrolled patients, together with the settings of the HD sessions, has been performed. The optimization orders have been also considered. Results k(K+), k(Urea), and k(Crea) are the first optimized and remain generally of the same magnitude. k(Na+), k(Cl-), k(HCO3-), k(Mg2+), and k(Glu), correlate with the UF rate, the therapy technique, or the concentrations in dialysate bags. They are later optimized and characterized by wider variability in values and optimization orders. k(Ca2+) remains at the default in 98% of the sessions, being optimized last. More convective therapies seem to induce patient-specific variations in active and passive cellular transport for specific solutes. η relevant correlations refer to the dialysate bag composition and the baseline conditions of the patients. They are in general late optimized, in HDF before than in SHD. η(Urea) and η(Crea) in 99% of the cases do not move from the default, suggesting a lower interaction of the non-ionic species with the filter membrane. They are optimized only in non-hypertensive insulin-dependent diabetic patients. No correlation between η and the filter material or the initial serum albumin was instead observed. ρ is related to the therapy; in HDF sessions it is over the range boundaries ([0,10]). For females undergoing SHD, it assumes the highest values (>9). ρ seems also to be affected by the use of beta-blockers (p < 0.01) and antihyperglycemic ((p < 0.01). Ca_buff coefficients for females (higher values) and males are statistically different. Diabetics using anti-hyperglycemia drugs are characterized by lower values. Conclusion Compartmental patient-specific models, if successfully trained become a useful support tool for therapy customization, mainly for ESRD patients with multiple comorbidities. The observed correlations represent the first step to causally relate the values of the modulating parameters with the patient characteristics implying the reduction or amplification of a specific response to the treatment. The next step will be an analysis of combined effects. The final goal is to be able to correctly set up the models even before the training phase is completed.

Unveiling the dynamics of seam performance: a regression analysis approach for predictive modeling

The performance of seams is crucial for the garment manufacturing industry's sustainability. This study delves into the factors impacting seam slippage, strength, and efficiency. By analyzing fabric specifications (weight and weave), machine settings (presser foot pressure and speed), and seam direction, the study uncovers their influence on seam properties. Samples of 100% cotton plain and twill fabrics, both of low (120 g/m2 to140 g/m2 ) and high (275 g/m2 to320 g/m2) weights, were chosen. Lock stitch type (301) and superimposed seam class were employed, with samples stitched in both weft and warp directions using the same stitch type, seam class, and substantial core spun sewing thread. An experimental design was created using variable parameters (fabric weight, weave, machine speed, and presser foot pressure), resulting in a 23 × 32 regression analysis. This design facilitated the development of 72 stitched samples. The regression analysis yielded an optimized solution, with square regressions indicating significant seam strength (85%) and efficiency (93%). In summary, this research provides insights into optimizing seam performance by understanding the interplay of fabric, machine, and stitching parameters, crucial for enhancing the quality and durability of garment seams.

Analisis Pengendalian Kualitas dengan Metode Six Sigma Pada Produksi Packaging PT. ABC

The development of industry in this current era is progressing rapidly, marked by increasing competition among companies. The method used is the Six Sigma method with the DMAIC approach (Define, Measure, Analyze, Improve, and Control). With this method, data obtained includes a total number of both good and defective products, which are then classified into several predefined defect categories by the company. Based on the data analysis results from July 2022 to December 2022, PT ABC identified four types of defects in packaging products. These defects include sticking with a defect percentage of 54%, cracking at 27%, coloring at 18%, and scratching at 2%. Improvement efforts can be made through training, guidance, and supervision of operators to enhance product quality. Additionally, strict supervision of raw materials in packaging production is necessary to ensure their quality is maintained. Furthermore, the machine setting process also requires supervision and guidance, with the implementation of clear Standard Operating Procedures (SOP) for each product. Keywords: DMAIC, Quality Control, Six Sigma

Optimasi Setting Mesin Cutting roll Menggunakan Design off Experiment (Studi Kasus: CV. Surya Gemilang)

The economic growth of the manufacturing sector in Indonesia is increasing rapidly every year. One that contributes to this economic growth is the textile industry. Along with the growth that occurs, textile companies strive to achieve a more effective and efficient production process, namely by reducing defective products or defects from the production carried out. CV. Surya Gemilang is a company that faces similar problems. CV. Surya Gemilang is a company engaged in the manufacturing industry sector, one of whose products is textile products, namely list webbing. list webbing is a superior product from CV. Surya Gemilang which has collaborated with 4 companies with requests for up to 15 rolls per month per company, where each 1 roll contains approximately 60 list webbing. In the production process list webbing still has a defect value produced after a fairly large production process. One of the factors that cause defective products to occur is the machine used, the machine used for the production of list webbing is a cutting roll machine. For this reason, it is necessary to conduct research using the Design of Experiment method using a Full Factorial Design approach with 2 replications. The factors used are roll rotational speed (rpm), feeding speed (rpm), and horizontal moving blade speed (rpm). This study aims to find factors that have a significant effect and find the optimal cutting roll machine settings to reduce the defects produced. From the results of this study, the factors that have a significant effect are the factor of feeding speed, horizontal speed, and the interaction between the factors of rotating speed and feeding speed and the optimal cutting roll machine settings are rotating speed with a level of 50 rpm, feeding speed with a level of 125 rpm and horizontal speed with a level of 50 rpm

Application of Taguchi Method, ANOVA Analysis, and TOPSIS Technique in Optimization of Process Parameters for Surface Roughness and Material Removal Rate in Electrochemical Machining of Al-SiC MMCs

Objectives: To evaluate the significance of advanced machining techniques, such as EDM, ECM, and USM, in increasing productivity and overcoming challenges associated with outdated Al-SiC MMC machining. To assess the surface roughness, tool wear, and machining cost implications of employing advanced machining methods for Al-SiC MMCs. Methods The parameters studied were voltage (V), feed rate (F), and electrolyte concentration (C) in electrochemical machining (ECM) of Al/15%SiC composites. To optimise process parameters, the Taguchi method for Design of Experiments (DOE) with an L27 orthogonal array was used. Signal responsiveness is optimised using the Taguchi approach. The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is used to find optimal machining settings. Findings: The outcome of this research is that the parameters affecting surface roughness and material removal rate are voltage, electrolyte concentration and feed rate. The minimum Surface Roughness achieved by selecting the best combination level is A2, B3, C3 (smaller is better) i.e., voltage 20 V, feed rate (f) 0.4 mm/min., electrolyte concentration (c) 30 g/lit. The maximum Material Removal Rate achieved by selecting the best combination level is A3, B3, C3 (larger-is-better) i.e., voltage 25V, feed rate (f) 0.4 mm/min., electrolyte concentration (c) 30 g/lit. Novelty : In this work, TOPSIS technique paired with Taguchi method is used which is rarely studied by other researchers. TOPSIS technique provides the best optimal solution as compared to other techniques. Keywords: Al-SiC MMCs, ECM, MRR, Ra, Taguchi method, TOPSIS

Recycling potential of carbon fibres in the construction industry: From a technical and ecological perspective.

Even though carbon fibres (CFs) have been increasingly used, their end-of-life (EOL) handling presents a challenge. To address this issue, we evaluated the use of recycled CFs (rCFs), produced through pyrolysis, as rovings to be used in textile reinforced concrete structures. Mechanical processing (hammer mill) with varying machine settings was then used to assess EOL handling, considering the separation potential of rCFs and the length of separated rCFs. The results showed that rCF rovings can be separated from concrete with an average of 87 wt.-%, whereas the highest rCF length and separation yield were observed in different machine settings. In addition, a techno-environmental assessment on the mechanical process was performed to compare different machine settings. The machine settings with the highest yield of rCF rovings also had the highest fine fraction that cannot be further separated. Furthermore, life cycle assessment (LCA) was conducted covering three life cycles of CFs and an additional LCA for comparing rCF with virgin CF. LCA results revealed that CF reinforced plastic and concrete productions are the two main contributors to environmental impacts. The comparative LCA between virgin CF and rCF also showed that using rCF is environmentally advantageous, as virgin CF production causes 230% more global warming potential compared to rCF. Future studies assessing different allocation approaches, quantifying the quality of rCF, and its inclusion in LCA are relevant.

Performance analysis of EDM parameters on silicon nitride-titanium nitride composite for improving geometrical tolerances: an integrated GRA-RSM approach

ABSTRACT In the modern context, ceramic composites are being developed not just for increased strength and hardness but also for the ability to manufacture difficult to machine geometries that can use Electrical Discharge Machining. Sparking parameters such as current (I), pulse on time (Ton), pulse off time (Toff), and dielectric flushing pressure (P) are improved with multi-response factors such as material removal rate, Electrode Wear Rate, and Geometrical Errors. The results of the confirmation experiments indicate that machining time was decreased from 12.517 to 10.121 min, Material Removal Rate was increased from 0.0183 to 0.0215 g/min, Electrode Wear Rate was reduced from 0.0024 to 0.0002 g/min, circularity was decreased from 0.175 mm to 0.098 mm, cylindricality was decreased from 0.761 to 0.541 mm, and perpendicularity was decreased from 1.071 to 0.085 mm. By optimizing the data to its optimum level, the gray relational grade has been enhanced to 0.16464. Consequently, the experimental findings suggest that gray relational analysis can be effectively employed to optimize the machining settings of Spark Electrical Discharge Machining for various performance aspects.

Problems of safe operation of saw chain sets of forest machines

Most of the volumes of timber harvesting in Russia and in developed foreign countries are produced by machine, using sorting technology. At the same time, in most cases, either harvesters (producing sorting in apiaries) or felling-packing machines and processors (producing sorting in the upper warehouse) are used to perform processing operations. The vast majority of these machines are equipped with saw chain headsets, the correctness of maintenance and operation of which directly affects not only the effectiveness of basic logging operations, but also the safety of their performance. The article discusses the basic rules of safe operation and proper maintenance of saw sets of forest machines.

Perfluorocarbon Liquid-Silicone Oil Exchange: The Simplicity and Reliability of the Single Port Technique and a Review of Literature

Purpose: To report the “Single Port Technique” for perfluorocarbon liquid (PFCL)-silicone oil (SO) exchange for the management of giant retinal tear detachments (GRTD). Methods: The previously reported techniques of direct PFCL-SO exchange used 2 ports to achieve the influx and outflux of fluid. The term 'Single Port' refers to the use of only one port as the exclusive port for the inflow of SO and the outflow of fluid meniscus and PFCL, performed alternately. This technique is proposed, with illustrated instructions and a surgical video. Results: The newly proposed “Single Port Technique” offers a straightforward step using a switch between active aspiration by the vitreous cutter and active silicone oil injection through a single port without the need for assistance. This method has been performed by the authors on 29 patients, including 22 GRTD patients and 7 patients with rhegmatogenous retinal detachment caused by dialysis, and it has demonstrated its simplicity and reliability for use with only standard vitrectomy tools and machine settings. Conclusion: The Single Port Technique for PFCL-SO exchange for the management of GRTD and rhegmatogenous retinal detachment caused by dialysis has been tried and tested, and it is a valuable tool that can be easily used by any vitreoretinal surgeon.

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

Real-time adaptable fault analysis of rotating machines based on Marine predator algorithm optimised LightGBM approach

ABSTRACT Intelligent fault diagnosis (IFD) techniques are important for fault analysis. It is challenging to constitute a real-time adaptable technique that can handle different types of fault conditions and fault severity values under variable machine settings. Based on this challenge, this study proposes an effective approach called Marine Predator Algorithm optimised Light Gradient Boosting Machine (MPA-LightGBM) for fault diagnosis and severity analysis of rotating machines regarding both single and compound faults. For the fault diagnosis, the MPA-LightGBM classifier is employed. The suitability of the proposed approach for real-world applications is validated by performing a real-time analysis using unseen data collected from a rotating machine operated at different rotational speeds. The computational load is alleviated by eliminating the redundant features up to 85.00% via MPA. According to experimental findings, the suggested method was able to accurately identify the individual cases for the testing set by 99.59% and 94.88% for compound faults. Regarding real-time analysis, the identification performance is 99.48% for individual faults while it is 92.33% for compound faults. The fault severity prediction error of the proposed approach considering the testing set is 0.55% and 1.15% for the individual and compound faults, while it is 0.50% and 1.88% regarding the real-time analysis.

Influence of Novel Al-Ni Electrodes on Roughness and Machining Time in Inconel 625 EDM Machining: A Comprehensive RSM Performance Analysis

In the context of Inconel 625 machining, this study explores the effects of novel Al-Ni nano electrodes on surface roughness and machining time. Thorough performance study was undertaken to clarify the complex connections between the machining settings, electrode composition, and final surface quality. Important insights were obtained via methodical experimentation and data analysis, which helps optimize the machining procedures for improved productivity and surface quality when milling Inconel 625. This research presents an experimental investigation of the effects of the Electric Discharge Machining (EDM) input parameters, namely current, pulse-on time, and pulse-off time, on the surface roughness and machining time output parameters. Inconel 625 was the material of choice for the work piece. Electric discharge machining oil was used as the dielectric fluid and aluminium nanocomposite as the tool electrode. Response surface methodology (RSM) approach was employed in the experimentation. ANOVA was used to optimize the parameters and obtain the lowest possible surface roughness (SR) and machining time. The findings show that pulse-off time is the most significant motivating factor for SR. The current is the main determining factor for machining time. The Central Composite Design approach was used for optimization.

On the parameterized complexity of interval scheduling with eligible machine sets

We provide new parameterized complexity results for Interval Scheduling on Eligible Machines. In this problem, a set of n jobs is given to be processed non-preemptively on a set of m machines. Each job has a processing time, a deadline, a weight, and a set of eligible machines that can process it. The goal is to find a maximum weight subset of jobs that can each be processed on one of its eligible machines such that it completes exactly at its deadline. We focus on two parameters: The number m of machines, and the largest processing time pmax. Our main contribution is showing W[1]-hardness when parameterized by m. This answers Open Problem 8 of Mnich and van Bevern's list of 15 open problems in parameterized complexity of scheduling problems [Computers & Operations Research, 2018]. Furthermore, we show NP-hardness even when pmax=O(1) and present an FPT-algorithm with for the combined parameter m+pmax.