Process capability analysis is the main tool of statistical process control. It is used for the ongoing monitoring of product compliance with imposed requirements. The main objective and novelty of the study were to determine the capability indices for a precision milling process of AZ91D magnesium alloy. Machining was performed in terms of variable technological parameters and using end mills with protective TiAlN and TiB coatings intended for the machining of light metal alloys. The Pp and Ppk process capability indices were determined based on the measurements of the dimensional accuracy of the shaped components that were taken on a machining centre with a workpiece touch probe. Obtained results demonstrated that the type of tool coating and variable machining conditions had a significant impact on the machining effect. The selection of appropriate machining conditions enabled a terrific level of capability to be achieved at a tolerance of 12 µm, several times lower than under unfavourable conditions where the tolerance was up to 120 µm. Improvements in process capability are mainly achieved by adjusting the cutting speed and feed per tooth. It was also shown that process estimation based on improperly selected capability indices might lead to an overestimation of the actual process capability.
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http://dx.doi.org/10.3390/ma16083119 | DOI Listing |
Angew Chem Int Ed Engl
January 2025
Henan University of Technology, School of Chemistry and Chemical Engineering, CHINA.
Developing of molecular crystalline materials with light-induced multiple dynamic deformation in space dimension and photochromism on time scales has attracted much attention for its potential applications in actuators, sensoring and information storage. Nevertheless, organic crystals capable of both photoinduced dynamic effects and static color change are rare, particularly for multi-component cocrystals system. In this study, we first report the construction of charge transfer co-crystals allows their light-induced solid-to-liquid transition and photochromic behaviors to be controlled by trans-stilbene (TSB) as an electron donor and 3,4,5,6-Tetrafluorophthalonitrile (TFP) as an electron acceptor.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Grupo de Investigación Materiales Con Impacto (Mat&Mpac), Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 No. 30-65, 050026, Medellín, Colombia.
This study shows the efficiency of WH-C450, an adsorbent obtained from water hyacinth (WH) biomass, in the removal of sulfamethoxazole (SMX) from aqueous solutions. The process involves calcination of WH at 450 °C to produce an optimal adsorbent material capable of removing up to 73% of SMX and maximum SMX adsorption capacity of 132.23 mg/g.
View Article and Find Full Text PDFJ Gen Intern Med
January 2025
Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Background: Active surveillance (AS) is the guideline-recommended treatment for low-risk prostate cancer and involves routine provider visits, lab tests, imaging, and prostate biopsies. Despite good uptake, adherence to AS, in terms of receiving recommended follow-up testing and remaining on AS in the absence of evidence of cancer progression, remains challenging.
Objective: We sought to better understand urologist, primary care providers (PCPs), and patient experiences with AS care delivery to identify opportunities to improve adherence.
Acta Pharmacol Sin
January 2025
Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
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