On Characterization and Optimization of Engineering Surfaces

Swedish manufacturing industry in collaboration with academia is exploring innovative ways to manufacture eco-efficient and resource efficient products. Consequently, improving manufacturing efficiency and quality has become the priority for the manufacturing sector to remain competitive in a sustainable way. To achieve this, control and optimization of manufacturing process and product’s performance are necessary. This has led to increase in demand for functional surfaces, which are engineering surfaces tailored to different applications. With new advancements in manufacturing and surface metrology, investigations are steadily progressing towards re-defining quality and meeting dynamic customer demands. In this thesis, surfaces produced by different manufacturing systems are investigated, and methods are proposed to improve specification and optimization.The definition and interpretation of surface roughness vary across the manufacturing industry and academia. It is well known that surface characterization helps to understand the manufacturing process and its influence on surface functional properties such as wear, friction, adhesivity, wettability, fluid retention and aesthetic properties such as gloss. Manufactured surfaces consist of features that are relevant and features that are not of interest. To be able to produce the intended function, it is important to identify and quantify the features of relevance. Use of surface texture parameters helps in quantifying these surface features with respect to type, region, spacing and distribution. Currently, surface parameters Ra or Sa that represent average roughness are widely used in the industry, but they may not provide adequate information on the surface. In this thesis, a general methodology, based on the standard surface parameters and statistical approach, is proposed to improve the specification for surface roughness and identify the combination of significant surface texture parameters that best describe the surface and extract valuable surface information.Surface topography generated by additive, subtractive and formative processes is investigated with the developed research approach. The roughness profile parameters and areal surface parameters defined in ISO, along with power spectral density and scale sensitive fractal analysis, are used for surface characterization and analysis. In this thesis, the application of regression statistics to identify the set of significant surface parameters that improve the specification for surface roughness is shown. These surface parameters are used to discriminate between the surfaces produced by multiple process variables at multiple levels. By analyzing the influence of process variables on the surface topography, the research methodology helps to understand the underlying physical phenomenon and enhance the domain-specific knowledge with respect to surface topography. Subsequently, it helps to interpret processing conditions for process and surface function optimization.The research methods employed in this study are valid and applicable for different manufacturing processes. This thesis can support the guidelines for manufacturing industry focusing on process and functional optimization through surface analysis. With increase in use of machine learning and artificial intelligence in automation, methodologies such as the one proposed in this thesis are vital in exploring and extracting new possibilities in functional surfaces

On Deterministic feature-based Surface Analysis

Manufacturing sector is continuously identifying opportunities to streamline production, reduce waste and improve manufacturing efficiency without compromising product quality. Continuous improvement has been the primary objective to produce acceptable quality products and meet dynamic customer demands by using advanced techniques and methods. Considering the current demands from society on improving the efficiency with sustainable goals, there is considerable interest from researchers and industry to explore the potential, to optimize- and customize manufactured surfaces, as one way of improving the performance of products and processes.Every manufacturing process generate surfaces which beholds certain signature features. Engineered surfaces consist of both, features that are of interest and features that are irrelevant. These features imparted on the manufactured part vary depending on the process, materials, tooling and manufacturing process variables. Characterization and analysis of deterministic features represented by significant surface parameters helps the understanding of the process and its influence on surface functional properties such as wettability, fluid retention, friction, wear and aesthetic properties such as gloss, matte. In this thesis, a general methodology with a statistical approach is proposed to extract the robust surface parameters that provides deterministic and valuable information on manufactured surfaces.Surface features produced by turning, injection molding and Fused Deposition Modeling (FDM) are characterized by roughness profile parameters and areal surface parameters defined by ISO standards. Multiple regression statistics is used to resolve surfaces produced with multiple process variables and multiple levels. In addition, other statistical methods used to capture the relevant surface parameters for analysis are also discussed in this thesis. The selected significant parameters discriminate between the samples produced by different process variables and helps to identify the influence of each process variable. The discussed statistical approach provides valuable information on the surface function and further helps to interpret the surfaces for process optimization.The research methods used in this study are found to be valid and applicable for different manufacturing processes and can be used to support guidelines for the manufacturing industry focusing on process optimization through surface analysis. With recent advancement in manufacturing technologies such as additive manufacturing, new methodologies like the statistical one used in this thesis is essential to explore new and future possibilities related to surface engineering

Evaluation of locally-administered controlled-release doxycycline hyclate gel in smokers and non-smokers in the management of periodontitis: An Indian study

Purpose: To assess the clinical and biological effect of locally-administered controlled-release 10 % doxycycline hyclate gel in smokers and non-smokers for the management of periodontitis. Methods: Forty periodontitis patients were enrolled in this study from December 2012 to February 2013 at the Department of Periodontology and Implantology of the Institute of Dental Sciences and Dental Unit of Rohilkhand Medical College, Bareilly, Uttar Pradesh (UP), India. For each patient, probing pocket depth (PPD), clinical attachment level (CAL), plaque index (PI), gingival index (GI), and sulcular bleeding index (SBI) were recorded. Changes in microbial counts were assessed by measuring colonyforming units (CFU) of three major periodontal pathogens. Clinical and microbial parameters were recorded at baseline and one month after scaling and root planing plus controlled local drug delivery of 10 % doxycycline hyclate gel in smokers and non-smokers. Results: A statistically significant change (p < 0.01) in PPD was observed among smokers between baseline (4.26 ± 0.12mm) and re-evaluation at one month (3.20 ± 0.11) with a change of 24.88 %. A statistically significant difference was found between smokers and non-smokers in PPD at the end of a 1-month re-evaluation (p < 0.05). None of the other parameters showed improvement in smokers following treatment. Conclusion: These results indicate that 10 % doxycycline hyclate gel, when administered locally into the periodontal pocket, shows clinical and microbial improvement, among smokers and non-smokers, in the management of periodontitis. Therefore, 10 % doxycycline gel is potentially an effective therapeutic strategy in the management of periodontitis

From Traditional Manufacturing to Digital Manufacturing: Two Swedish Case Studies

Digital manufacturing can produce new and advanced tools more rapidly and at lower cost than traditional manufacturing. This new technology means manufacturers need to develop innovative business models adapted to this change in the manufacturing landscape. With digital manufacturing, companies have both an opportunity and a challenge. They can enter new markets where large-scale production provides competitive advantage. They can enter niche markets that become more attractive as old boundaries and structures lose relevance. Yet their additive manufactured components must meet the same standards set for conventional manufactured components. However, we know little about how companies manage this change as they make the transition from traditional manufacturing to digital manufacturing. This chapter presents two co-creation digital manufacturing projects between university researchers and Swedish companies. In each project, the goal was to develop sustainable and efficient digital production methods that offer tailor-made product solutions. Various technical methods used in the projects are described as materials, and prototypes are developed, tested, and analyzed

Toward identifying reproducible brain signatures of obsessive-compulsive profiles: rationale and methods for a new global initiative

Background Obsessive-compulsive disorder (OCD) has a lifetime prevalence of 2–3% and is a leading cause of global disability. Brain circuit abnormalities in individuals with OCD have been identified, but important knowledge gaps remain. The goal of the new global initiative described in this paper is to identify robust and reproducible brain signatures of measurable behaviors and clinical symptoms that are common in individuals with OCD. A global approach was chosen to accelerate discovery, to increase rigor and transparency, and to ensure generalizability of results. Methods We will study 250 medication-free adults with OCD, 100 unaffected adult siblings of individuals with OCD, and 250 healthy control subjects at five expert research sites across five countries (Brazil, India, Netherlands, South Africa, and the U.S.). All participants will receive clinical evaluation, neurocognitive assessment, and magnetic resonance imaging (MRI). The imaging will examine multiple brain circuits hypothesized to underlie OCD behaviors, focusing on morphometry (T1-weighted MRI), structural connectivity (Diffusion Tensor Imaging), and functional connectivity (resting-state fMRI). In addition to analyzing each imaging modality separately, we will also use multi-modal fusion with machine learning statistical methods in an attempt to derive imaging signatures that distinguish individuals with OCD from unaffected siblings and healthy controls (Aim #1). Then we will examine how these imaging signatures link to behavioral performance on neurocognitive tasks that probe these same circuits as well as to clinical profiles (Aim #2). Finally, we will explore how specific environmental features (childhood trauma, socioeconomic status, and religiosity) moderate these brain-behavior associations. Discussion Using harmonized methods for data collection and analysis, we will conduct the largest neurocognitive and multimodal-imaging study in medication-free subjects with OCD to date. By recruiting a large, ethno-culturally diverse sample, we will test whether there are robust biosignatures of core OCD features that transcend countries and cultures. If so, future studies can use these brain signatures to reveal trans-diagnostic disease dimensions, chart when these signatures arise during development, and identify treatments that target these circuit abnormalities directly. The long-term goal of this research is to change not only how we conceptualize OCD but also how we diagnose and treat it

The thalamus and its subnuclei—a gateway to obsessive-compulsive disorder

Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T-1-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered (https://osf.io/73dvy) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (<12 years) had larger lateral (d = 0.46), pulvinar (d = 0.33), ventral (d = 0.35) and whole thalamus (d = 0.40) volumes at unadjusted p-values <0.05. Adolescent patients showed no volumetric differences. Adult OCD patients compared with controls had smaller volumes across all subregions (anterior, lateral, pulvinar, medial, and ventral) and smaller whole thalamic volume (d = -0.15 to -0.07) after multiple comparisons correction, mostly driven by medicated patients and associated with symptom severity. The anterior thalamus was also significantly smaller in patients after adjusting for thalamus size. Our results suggest that OCD-related thalamic volume differences are global and not driven by particular subregions and that the direction of effects are driven by both age and medication status

Toward identifying reproducible brain signatures of obsessive-compulsive profiles: rationale and methods for a new global initiative

Background Obsessive-compulsive disorder (OCD) has a lifetime prevalence of 2–3% and is a leading cause of global disability. Brain circuit abnormalities in individuals with OCD have been identified, but important knowledge gaps remain. The goal of the new global initiative described in this paper is to identify robust and reproducible brain signatures of measurable behaviors and clinical symptoms that are common in individuals with OCD. A global approach was chosen to accelerate discovery, to increase rigor and transparency, and to ensure generalizability of results. Methods We will study 250 medication-free adults with OCD, 100 unaffected adult siblings of individuals with OCD, and 250 healthy control subjects at five expert research sites across five countries (Brazil, India, Netherlands, South Africa, and the U.S.). All participants will receive clinical evaluation, neurocognitive assessment, and magnetic resonance imaging (MRI). The imaging will examine multiple brain circuits hypothesized to underlie OCD behaviors, focusing on morphometry (T1-weighted MRI), structural connectivity (Diffusion Tensor Imaging), and functional connectivity (resting-state fMRI). In addition to analyzing each imaging modality separately, we will also use multi-modal fusion with machine learning statistical methods in an attempt to derive imaging signatures that distinguish individuals with OCD from unaffected siblings and healthy controls (Aim #1). Then we will examine how these imaging signatures link to behavioral performance on neurocognitive tasks that probe these same circuits as well as to clinical profiles (Aim #2). Finally, we will explore how specific environmental features (childhood trauma, socioeconomic status, and religiosity) moderate these brain-behavior associations. Discussion Using harmonized methods for data collection and analysis, we will conduct the largest neurocognitive and multimodal-imaging study in medication-free subjects with OCD to date. By recruiting a large, ethno-culturally diverse sample, we will test whether there are robust biosignatures of core OCD features that transcend countries and cultures. If so, future studies can use these brain signatures to reveal trans-diagnostic disease dimensions, chart when these signatures arise during development, and identify treatments that target these circuit abnormalities directly. The long-term goal of this research is to change not only how we conceptualize OCD but also how we diagnose and treat it

LEAD FREE BRASS : Study and Analysis of the surface integrity of lead brass and unleaded brass.

The addition of lead to the copper alloys increases its machinability and reducesthe overall production cost, despite copper being expensive, which makes achallenging task to replace lead. But the alarming effects of lead on human healthand the recycling problems has led to the increase in concern forreducing/eliminating the use of lead in brass and other copper alloys. Manymaterials are considered to replace lead in brass; silicon is one such alternative.The turned brass sample are investigated using the state of the artequipments at Halmstad University. The results obtained are controlled readingsof surface parameters and is categorized using surface imaging and mappingsoftware, Mountains Map.This thesis characterizes the lead and the lead free brass's surfaceintegrity for a certain cutting data. The study deals with the evaluation ofselection of appropriate surface integrity parameters and summarizes theappropriate combination of cutting data to maintain the surface of the ecobrass/unleaded brass on par with the leaded brass surface. The 2D and 3Dsurface parameters illustrates the surface functionality and its effect on thematerial in contact.The research results suggest a detailed methodology for the analysis ofsurface topography and a comparison exemplifying differences between the twomaterials under study. The research provides a perplexed results and forms thebasis for further investigations of the samples machined at different cutting data.Second set of test includes comparing the Leaded brass with the unleaded brasssamples machined at 0.06, 0.1, 0.15 and 0.2mm/rev respectively. The studyfocuses on the correlation of cutting feed and the surface parameters. Comparingthe results of two tests, the unleaded brass machined @ feed rate 0.2mm/rev,200m/min, 1.5mm depth of cut posses similar surface functionality as leadedbrass

Effect of Soil Nutrient Status on Yield and Quality of Sweet Orange (Citrus sinensis (L.) Osbeck) in YSR District of Andhra Pradesh

The present study was conducted to determine the effect of soil nutrient status on fruit yield and quality of sweet orange (Citrus sinensis (L.) Osbeck) in YSR district of Andhra Pradesh, India. To carry out this investigation fifty sweet orange orchards aged between 12 to 13 years were selected and soil samples were collected from these orchards at 0-30 cm and 30-60 cm depth. Majority of the soils of the study area were deficit in available nutrients such as Zn, Fe, N, P and Mn, but Ca, Mg, S, K and Cu were in optimum to high range. The soil mineral nutrients like N, P and K influenced the fruit weight significantly and positively (r = 0.469**, r = 0.446** and r = 0.415**, respectively), but fruit yield and fruit juice per cent had significant positive relation with soil N (r = 0.519** and r = 0.353*) and P (r = 0.409** and r = 0.364**) only. Soil P had a significant positive correlation with TSS (r = 0.438**). Soil Fe and Mn had a significant negative correlation with titrable acidity (r = -0.371** and r = -0.292*, respectively). Soil Mn had a significant negative correlation with fruit TSS (r = -0.311*)