Academic literature on the topic 'Manufacturing not elsewhere classified'

The financial facility of development of machinery building in Latgale is expected in manufacturing of electrical and optical equipment and in production of metal and metal ware. At present the proportion of production of leading machinery and equipment non-classified elsewhere, as well as of production of transport means has a tendency to reduce and also they have lesser probability to manage the financial crisis.

In recent years, many design approaches have been developed for automated manufacturing systems in the fields of reconfigurable manufacturing systems (RMSs), holonic manufacturing systems (HMSs), and multi-agent systems (MASs). One of the principle reasons for these developments has been to enhance the reconfigurability of a manufacturing system, allowing it to adapt readily to changes over time. However, to date, reconfigurability assessment has been limited. Hence, the efficacy of these design approaches remains inconclusive. This paper is the first of two in this issue to address reconfigurability measurement. Specifically, it seeks to address ‘reconfiguration potential’ by analogy. Mechanical degrees of freedom have been used in the field of mechanics as a means of determining the independent directions of motion of a mechanical system. By analogy, manufacturing degrees of freedom can be used to determine independent ways of production. Furthermore, manufacturing degrees of freedom can be classified into their production and product varieties. This paper specifically focuses on the former to measure the product-independent aspects of manufacturing system ‘reconfiguration potential’. This approach will be added to complementary work on the measurement of ‘reconfiguration ease’ so as to form an integrated reconfigurability measurement process described elsewhere [1—5].

This article analyses the reasons and impact of shift of manufacturing startups from the United States to overseas. After years of refining prototypes and perfecting pilot plants, advanced manufacturing startups frequently look overseas when it is time to scale-up for commercial production. Both manufacturing and technology companies go abroad looking for partnerships, because it is easier for investors. When startups scale their manufacturing elsewhere, the United States loses more than a possible return on the research investment that made such breakthroughs possible. The preliminary research suggests that to fully realize the economic gains associated with innovation, new products and services developed by American innovators must be scaled-up within the US economy, as well as in overseas markets. The four suggestions that have been made include the following: increase financing options for later-stage development; create institutions and incentives; change the contours of market demand; and encourage firms to raise capital through initial public offerings.

The Business Research and Services Section (BRASS) 2015 program was directed to business research professionals who may be stymied by researcher requests related to newer or niche industries. In a stimulating ninety-minute session, two top research professionals informed, confronted and engaged their American Library Association (ALA) audience with their well-paced array of smart search strategies and sources designed to meet industry challenges. The presentation was effective in identifying research workaround strategies and tying real industry problems with practical, on-the-job solutions.The material provided here and the bibliography of sources may serve as additional resources for emerging industry queries of library users. The BRASS program presentation is available via this ALA conference website (http://alaac15.ala.org/node/28603).

Abstract We conducted a phytosociological study of the communities hosting the rare and endangered fern Adiantum capillus-veneris in the foothills of the Julian Alps, in Karst and in Istria. Based on a comparison with similar communities elsewhere in the southern Alps (northern Italy) we classified most of the recorded stands into the syntaxa Eucladio-Adiantetum eucladietosum and -cratoneuretosum commutati. Releves from the southern Julian Alps, located in comparatively slightly colder and moister local climate and the dolomite bedrock are classified into the new subassociation -hymenostylietosum recurvirostri subass. nova. Stands with the abundant occurrence of the liverwort Conocephalum conicum, are classified in to the new subassociation -conocephaletosum conici subass. nova. Stands in conglomerate rock shelters along the Soča at Solkan are classified into the new association Phyteumato columnae-Adiantetum ass. nova, a community of transitional character between the classes Adiantetea capilli-veneris and Asplenietea trichomanis.

SummaryOur major classification systems (DSM and ICD) face three main problems: the high rates of ‘comorbidity’ that are produced by our present diagnostic rules, the increasing use of ‘not elsewhere classified’ (NEC) by practising clinicians, and the fact that each new edition is longer and more complex than the one preceding it. A major simplification of the chapter structure used by each classification might pave the way to address these problems.

Osteosarcomas are typically long bone tumors and rarely affect the skull, with most articles reporting single cases. As elsewhere in the body, these lesions may be classified as primary or secondary, chiefly post-Paget and post-radiation therapy. We describe two cases of primary osteosarcoma of skull one presenting with cerebellar symptoms and another with giant skull swelling. Complete evaluation with 64 slice CT and histopathological correlation was carried out.

This thesis starts with a literature review, outlining the major issues identified in the literature concerning virtual manufacturing enterprise (VME) transformation. Then it details the research methodology used – a systematic approach for empirical research. next, based on the conceptual framework proposed, this thesis builds three modules to form a reference model, with the purpose of clarifying the important issues relevant to transforming a traditional manufacturing company into a VME. The first module proposes a mechanism of VME transformation – operating along the VME metabolism. The second module builds a management function within a VME to ensure a proper operation of the mechanism. This function helps identify six areas as closely related to VME transformation: lean manufacturing; competency protection; internal operation performance measurement; alliance performance measurement; knowledge management; alliance decision making. The third module continues and proposes an alliance performance measurement system which includes 14 categories of performance indicators. An analysis template for alliance decision making is also proposed and integrated into the first module. To validate these three modules, 7 manufacturing organisations (5 in China and 2 in the UK) were investigated, and these field case studies are analysed in this thesis. The evidence found in these organisations, together with the evidence collected from the literature, including both researcher views and literature case studies, provide support for triangulation evidence. In addition, this thesis identifies the strength and weakness patterns of the manufacturing companies within the theoretical niche of this research, and clarifies the relationships among some major research areas from the perspective of virtual manufacturing. Finally, the research findings are summarised, as well as their theoretical and practical implications. Research limitations and recommendations for future work conclude this thesis.

Manufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.

The three major types of maintenance are reactive, preventive, and predictive. The research project focused on applying the principles of predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains the techniques and principles of applying the technology currently available to reduce downtime and maintenance cost. The predictive maintenance procedures and saving are compared with reactive and preventive methods to determine a value of return. The report will examine the benefits of using the Internet of Things technology to create autonomous self-diagnosing smart machines. The predictive maintenance plan in this research illustration will introduce health check equipment used to implement longer lasting machine components. In conclusion, the project developed out an entire predictive maintenance plan to reduce downtime and maintenance costs.

Robust process monitoring in real-time is a challenge for Continuous Pharmaceutical Manufacturing. Sensors and models have been developed to help to make process monitoring more robust, but they still need to be integrated in real-time to produce reliable estimates of the true state of the process. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this work, we present such a systematic framework, which for a given sensor network and measurement uncertainties will predict the most likely state of the process. As a result, real-time process decisions, whether for process control, exceptional events management or process optimization can be based on the most reliable estimate of the process state.

Data reconciliation (DR) and gross error detection (GED) have been developed to accomplish robust process monitoring. DR and GED mitigate the effects of random measurement errors and non-random sensor malfunctions. This methodology has been used for decades in other industries (i.e., Oil and Gas), but it has yet to be applied to the Pharmaceutical Industry. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. However, it requires the sensor network to be redundant (i.e., the number of measurements has to be greater than the degrees of freedom).

In this dissertation, the SSDR framework is defined and implemented it in two different continuous tableting lines: direct compression and dry granulation. The results for two pilot plant scales via continuous direct compression tableting line are reported in this work. The two pilot plants had different equipment and sensor configurations. The results for the dry granulation continuous tableting line studies were also reported on a pilot-plant scale in an end-to-end operation. New measurements for the dry granulation continuous tableting line are also proposed in this work.

A comparison is made for the model-based DR approach (SSDR-M) and the purely data-driven approach (SSDR-D) based on the use of principal component constructions. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. The reconciled measurement values generate using SSDR-M satisfy the model equations and can be used together with the model to estimate unmeasured variables. However, in the presence of nonlinearities, the SSDR-M and SSDR-D will differ. SSDR successfully estimates the real state of the process in the presence of gross errors, as long as steady-state is maintained and the redundancy requirement is met. Gross errors are also detected whether using SSDR-M or SSDR-D.

Business incubation is a dynamic process of business enterprise development. Incubators nurture young firms, helping them to survive and grow during the start-up period. Among various types of incubators the university-based incubators are particularly studied. Although most university incubators are quite successful in terms of the success rate and the growth rate of tenant companies, their financial contributions to the sponsoring universities, however, are still not satisfied. It is found that behind the successful history records there are still some barriers impeding the development of an efficient incubator. In this research, a new model, which integrates merits of public and private incubators into the university incubator, is proposed for the betterment of its management scheme. The goal is to develop a successful incubator, which can earn profits not only for its own financial sustainability but also be able to generate income for the university. The outcomes of this research are summarized as follows: 1. From questionnaire survey around more than 100 university incubators around the world, this research received constructive opinions from incubator experts to support the proposed concept. This inspires the author to consider the necessity of a new incubation model for long-term sustainability. 2. The method of this survey study combines the Delphi Method and Scenario Analysis, called modified Delphi method, for worldwide survey and the Microsoft Excel method for data statistic for both of the Taiwan and worldwide surveys. By breaking down long questionnaire into two successive surveys, the replied rate did significantly increase. 3. An integrative framework for the new incubation model has been proposed for the sustainable operation of university incubator. National Taiwan University has validated this model in a similar way. 4. The process of privatization of university incubator is proposed to meet the university administrative procedure. Both of the government initialized top-down and incubator initialized bottom-up processes are considered. A Business Plan to suit for the proposed incubation company is also designed in this work. The sustainability in terms of financial status has been predicted based on some reasonable assumptions. 5. In order to verify the proposed model, three case studies through on-site visits have been carried out to compare their incubation systems and financial status up-to-date. This can provide a guideline to adjust the proposed model of this work. Finally, a comprehensive conclusion and discussions are given to summarize the contribution and future work of this research.

Manufacturing facilities need control for sustainability and longevity. If no control is provided for the manufacturing facility, then chaos can be unleashed causing much alarm. Therefore, it is essential to understand how control can be utilized to support the manufacturing facility and the corresponding manufacturing processes. This thesis will walk through a tool to help provide control and that tool is a Manufacturing Execution System (MES). Thisthesis will start with research to defineMESand its implications, then will work into the development of MES from the ground up. The design process willbe systematic and utilize the Collective System Design (CSD) approach with the aiding tool of the axiomatic decomposition map. Then examples will be given for the implementation and execution of the decomposition map as it relates to inventory and traceability. Finalwork will show the 7 FRs ofmanufacturing and how they are applicable to MES with given examples. Throughout the entire design and implementation, the initial hypothesis will be evaluated to determine if MES can provide the control requiredfor a robust manufacturing facility.

Spherical agglomeration (SA) is a novel process intensification strategy for particulate manufacturing. In the context of pharmaceutical manufacturing, it has the potential to reduce the number of unit operations in downstream processing from seven to three, which significantly reduces the manufacturing cost. However, SA process development for a new API in the drug pipeline is still a challenging exercise, which has impeded its practical implementation. The major bottleneck lies in the lack of fundamental understanding of the mechanistic principles underlying agglomeration of primary crystals, which can enable rational process design. In addition, most SA processes reported in literature focus on only the API, which does not eliminate the blending and wet granulation unit operations. The major purposes of this thesis are to (i) develop a first principle mathematical framework which can identify the fundamental agglomeration mechanism (ii) develop a model based online optimization framework, which can control the process, even in the presence of model parametric uncertainties (iii) develop a rational framework for co-agglomerating APIs and excipients, guided by process analytical technology tools. It is believed that the novel technology developed in this thesis will lay the groundwork for fast and robust process development of co-agglomerating APIs and excipients in the future, thereby enabling one-step direct compression. The large-scale development and deployment of this technology will significantly reduce the time to market and the manufacturing costs for new APIs, thereby ensuring higher accessibility of life-saving drugs.

Lean Six Sigma (LSS) has been implemented worldwide for many years and has been successful in many organizations. Eloot, Huang, and Lehnich (2013) noted that achieving manufacturing excellence by using LSS was an opportunity for many companies. Liker and Rother (2011) pointed out that only 2% of companies successfully achieved the desired results with Lean plans.

The presented dissertation identified the critical success factors of LSS implementation for Chinese manufacturing companies and explored the challenges occurring during the LSS transformations. The objectives of this dissertation were:

i. to understand how the employee training process for LSS can be designed using total quality management (TQM) adoption in private manufacturing organizations in China;

ii. to understand how LSS practices can be adopted successfully in SMEs in China;

iii. to examine and explore the critical success factors (CSF) of LSS implementation;

iv. to discuss the challenges occurring during LSS transformation.

Objective (i) was achieved through a descriptive single case study. This case study showed how to apply a design for Six Sigma methodology (DMADV) for staff training in quality management tools in a private organization in China. The author also discussed the problems occurring during the Six Sigma project and explored how organizational culture impacted Six Sigma implementation. Objective (ii) was achieved through a detailed descriptive single case study which recorded how LSS practices were adopted successfully in a SME-VTCL in China using DMAIC methodology. Survey data was collected to identify and explore the critical success factors of LSS implementation in SMEs, by querying the voice of top, middle, and frontline management, as well as frontline workers of these companies. Objectives (iii) and (iv) were realized utilizing descriptive, exploratory, and multi-case studies designed to gather and analyze observational and interview data. The resulting interview data, and the key factors for successful LSS transformation of these three companies were discussed from the perspective of senior management and LSS promoters within the companies. Based on interview data and the Lean iceberg model, a new LSS transformation model was proposed. The author also developed 6 propositions based on the findings from the interviews.

In summary, the results of this study provided value and references for LSS practitioners to expand the body of knowledge on the strategies used to implement LSS successfully inside organizations. The findings of this research may potentially lead more Chinese organizations to successfully adopt LSS to provide customers with high-quality products. The three LSS implementation cases described critical success factors (CSFs) and challenges that occurred during the transformation, may improve the success rate of implementation, help enterprises achieve the desired results through LSS, and enhance the sustainability of LSS implementations.

Numerous optimization problems in production systems can be considered as decision-making processes that determine the best allocation of resources to tasks over time to optimize one or more objectives in concert with big data. Among the optimization problems, production scheduling and routing of robots for material handling are becoming more important due to their impacts on system performance. However, the development of efficient algorithms for scheduling or routing faces several challenges. While the scheduling and vehicle routing problems can be solved by mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems. Thus, further research on machine learning applications to those problems is a significant step towards increasing the possibilities and potentialities of field application. In order to create truly intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules. In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution approach, a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules automatically. Finally, an optimization problem for routing of autonomous robots for minimizing total tardiness of transportation requests is analyzed by decomposing it into three sub-problems. In order to solve the sub-problems, a comprehensive framework with consideration of conflicts between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT) that incorporates the contextual bandit into operator selection is developed. The findings from my research contribute to suggesting a guidance to practitioners for the applications of ML to scheduling and control problems, and ultimately to lead the implementation of smart factories.

One way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.

Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.

Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.

However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.

A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.

To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.

Several disorders have been classified together in the American Psychiatric Association’s Diagnostic and Statistical Manual (4th ed.; DSM-IV) as impulse control disorders not elsewhere classified. These impulse control disorders have been grouped together based on perceived similarities in clinical presentation and hypothesized similarities in pathophysiologies. The question exists whether these disorders belong together or whether they should be categorized elsewhere. Examination of the family of impulse control disorders generates questions regarding the distinct nature of each disorder: whether each is unique or whether they represent variations of each other or other psychiatric disorders. Neurobiology may cut across disorders, and identifying important intermediary phenotypes will be important in understanding impulse control disorders and related entities. The distress of patients with impulse control disorders highlights the importance of examining these disorders. More comprehensive information has significant potential for advancing prevention and treatment strategies for those who suffer from disorders characterized by impaired impulse control.

Obsessive-compulsive disorder (OCD) shares features and often co-occurs with other anxiety disorders, as well as with other psychiatric conditions classified elsewhere in the Diagnostic and Statistical Manual (DSM-IV), the so-called “OCD spectrum disorders.” Neurobiologically, it is unclear how all these disorders relate to one another. The picture is further complicated by the clinical heterogeneity of OCD. This chapter will review the literature on the common and distinct neural correlates of OCD vis-à-vis other anxiety and “OCD spectrum” disorders. Furthermore, the question of whether partially distinct neural systems subserve the different symptom dimensions of OCD will be examined. Particular attention will be paid to hoarding, which is emerging as a distinct entity from OCD. Finally, new insights from cognitive and affective neuroscience will be reviewed before concluding with a summary and recommendations for future research.

A perceived inadequacy in existing post-resurrection narratives seems to have inspired the second-century author of the Epistula Apostolorum to compose a comprehensive post-resurrection dialogue. In this—after securing with some difficulty their acceptance that he is truly alive—Jesus answers his disciples’ wide-ranging questions mainly about issues of eschatology and mission. Also present in this text are retrospective summaries of his descent from the heavenly world and his earthly career. While this important though neglected text may usefully be classified with works in a similar format, from Nag Hammadi and elsewhere, its primary affinities are with the traditions of Jesus’ earthly career reflected in Matthew, Luke, and especially John. In particular, the Johannine account of Easter Day and its aftermath provides the author not so much with a normative exemplar as with a source that he exploits freely and critically to develop his proto-orthodox theological agenda.

AbstractCAR-T cells for clinical application are classified as advanced therapy medicinal products (ATMPs), and their manufacture is subject to laws and regulations governed by the European Medicines Agency (EMA) and by federal and regional authorities. CAR-T cells must be manufactured to achieve good manufacturing practice (GMP) compliance and are defined as potent products manufactured safely according to standardized methods under closely controlled, reproducible, and auditable conditions. BioPharma supplies the vast majority of CAR-T products for patients, but some academic centres have developed point-of-care cGMP CAR-T manufacturing capability, striving to uphold the same stringency of product quality while improving patient access to CAR-T cells and streamlining the costs of therapy. Point-of-care CAR-T manufacturing can only be performed in facilities with the appropriate regulatory approvals in place.

AbstractCloud manufacturing (CMfg) is a service-oriented manufacturing paradigm that distributes resources in an on-demand business model. In the cloud manufacturing environment, scheduling is considered as an effective tool for satisfying customer requirements which has attracted attention from researchers. In this case, quality of service (QoS) in the scheduling plays a vital role in assessing the impacts of the distributed resources in operation on the performance of scheduling functions. In this paper, a queuing system is employed to model the scheduling problem with multiple servers and then scheduling in cloud manufacturing is classified based on various QoS requirements. Moreover, a set of heavy traffic limit theorems is introduced as a new approach to solving this scheduling problem in which different heavy traffic limits are provided for each of QoS-based scheduling classes. Finally, the number of operational resources in the scheduling is determined by considering the results obtained in the numerical analysis of the heavy traffic limit with different queue disciplines. The results show that different numbers of active machines in various QoS requirements classes play a vital role in that the required QoS metrics such as the expected waiting time and the expected completion time which are critical performance indicators of the cloud’s service are intimately related.

AbstractThe growing aviation market puts first tier suppliers of aircraft interior under great pressure. Cabin monuments, not only consist of various assemblies with a wide range of parts, they are also highly customized by the airliners. Historically grown, poorly optimized manual processes offer the required flexibility, but limit the production rate of the individual products. The aviation industry responds with an increased use of automation technology. Recent standardization and automation approaches for efficient manufacturing, lead to an increase in productivity of these low volume products. However, complementary approaches to increase the degree of automation during assembly of aircraft interior components are missing. To reach a higher degree of automation this paper presents a derivation of cabin specific assembly processes with a varying degree of automation. First the range of components and processes in pre-assembly is analyzed with respect to automation. Based on the analysis, components and processes are classified in standardized groups. Fully automated and flexible automation processes are introduced to develop a semi-automated system. Furthermore, the required flow of information is described. Discussion of the results shows that the presented solution allows a flexible pre-assembly of low-volume interior parts and sets a baseline for further digitalization approaches.

AbstractThe phrase ‘designer maker’ is being employed increasingly in the contemporary craft and design marketplace, especially among those seeking to make a full-time living from their practice. It marks those makers who may undertake original design and prototyping themselves, but who, in order to scale up their production in ways not always possible for a solo hand maker, outsource some or all subsequent aspects of production to other makers or machine-assisted manufacturing processes. But despite widespread use of this phrase, some makers remain keen to manage the scale of their business. As a result, many of those craftspeople and designer makers we spoke to who were in a position to scale-up their production while stepping back from the making themselves were reluctant to go down this path. Elsewhere we have explored these issues in terms of balancing making income with quality of life, as well as in terms of the desire to be a maker, to be doing the creative work oneself, and thus not ‘get too big’ with the added pressures and responsibilities of being an employer (Luckman, Cultural Trends, 27(5), 313–326 (2018)). In this chapter, we home in more on what upscaling and outsourcing reveals about competing definitions of, and attitudes towards, the idea of ‘the handmade’. We also explore attitudes towards handmaking versus other forms of production, including outsourcing and the use of digital tools.

Delamination between thin films is classified into two types: opening mode and sliding mode. Corresponding to each mode, there is the interface strength between thin films. This paper aims to evaluate interface strength between the sub-micron thin films for opening mode and sliding mode, respectively. We already developed the evaluation method of interface fracture toughness for opening mode on the basis of fracture mechanics concept elsewhere. Moreover, the evaluation method of sliding mode is proposed and the interface strength between thin films for an advanced LSI is evaluated as the fracture toughness by using both methods. In both modes, the stress singularity appears in the vicinity of the edge of interface and governs the delamination. The criterion of crack initiation for each mode is evaluated as the interface toughness. The fracture toughness at the edge of interface in sliding mode is lower than that in opening mode.

This paper reviews the state-of-the-art research related to the dimensional accuracy in additive manufacturing (AM) processes. It is considered that the improvement of dimensional accuracy is one of the major scientific challenges to enhance the qualities of the products by AM. This paper analyzed the studies for commonly used AM techniques with respect to dimensional accuracy. These studies are classified by process characteristics, and relevant accuracy issues are examined. The accuracies of commercial AM machines are also listed. This paper also discusses suggestions for accuracy improvement. With the increase of the dimensional accuracy, not only the application of AM processes will diversify but also their value will increase.

The effect of temperature exposure encountered both during assembly and in fielded products, has a known influence on glass transition temperature of printed-circuit board (PCB) laminate materials. Printed circuit board laminates such as FR4 are composites of epoxy resin with woven fiberglass reinforcement. Interaction between manufacturing process variables that impact the changes in glass transition temperature (Tg) has been studied. The laminates studied have been broadly classified into high-Tg, and mid-Tg laminates. Different sets of reflow profiles were created by varying the process variables including, time above liquidus, peak temperature, ramp rate and cooling rate. The effect of multiple reflows encountered in normal assembly or board re-work has been studied by exposing the assemblies to multiple reflows between 2x–6x. Changes to the glass transition temperature have been classified by measurement of the glass transition temperature were measured via Thermo Mechanical Analysis (TMA). Statistical analysis of the variables has been used to determine the statistical significance of the measured changes for large populations.

Abstract In a multi-axis metal hybrid layered manufacturing system, a combined laser material deposition and material removal system with more than 3-aixs mobility, sometimes support structures are necessary in the part building process, which prevent object from falling during deposition process. Since the support material is the same as build material, it is very difficult to remove the support material in the post processing stage. Thus, the support material must be machined off. Therefore, the geometry information of support structures is important for the machining process. In this paper, a method is presented to extract support structures automatically. Two types of support structures, trapped and non-trapped, are classified in this paper and their extraction methods are discussed respectively.

A basic shop course was typical in the early days of mechanical engineering studies. However, in the late seventies, the shop course was dropped from the engineering curriculum in most schools for various reasons. The need for a preparatory manufacturing course became apparent after recognizing the lack of modest shop skills exhibited by our students in design-build projects which are routinely assigned in several junior and senior level courses. The traditional manufacturing course, that all students are required to take during the senior year, requires higher-level prerequisites. In addition, being a senior level course, it provides very little opportunity for the students to utilize those skills in other courses. To address this void, a new engineering workshop course has been objectively designed and implemented for the sophomore mechanical and aerospace engineering students, beginning fall 2003. The goals of the course are to develop an appreciation for manufacturing in engineering design, prepare students for follow-up manufacturing course, and develop necessary skills for design-build projects at various levels of the curriculum. The engineering shop course is designed around nine laboratory content modules that introduce the students to the fundamentals of shop safety, measurement, and manufacturing. The present paper describes the development and implementation of the course. The effectiveness of the course in meeting the goals is also assessed through surveys conducted both before and after the course is completed. Long-term success will be measured in the future by a survey of graduating seniors to assess the effectiveness of the engineering shop course in the students’ ability to successfully complete design-build projects assigned in other courses. The modular design would allow this course to be adapted for achieving similar objectives elsewhere.

A single-phase of Li7La3Ce2O12 samples that formed garnet-type crystal structure were prepared using the conventional solid-state method. Li7La3Ce2O12 formed a tetragonal garnet-type structure with the space group of I4/mmm. Impedance spectroscopy data show that Li7La3Ce2O12 can be classified as the ionic conductor as it has a mixture of ionic and electronic conductivities. Their electrical conductivities also obey Jonscher universal power law.

Abstract In this paper, finite element analyses were conducted to investigate the stress and strain states resulting from varying the deformation of stainless steel 316L under biaxial loading. To that end, a biaxial specimen geometry was designed in collaboration with the US National Institute of Standards and Technology (NIST) to achieve large and uniform strain values in the central pocket region. Special care was taken to ensure that the specimen design could be readily manufactured with available resources. Simultaneously, the specimen design criteria required an acceptable strain uniformity in a sufficiently large pocket section to allow for accurate deformation and austenite to martensite phase fraction measurements. This demonstrates the concept of altering the final material properties through stress superposition. Numerical results show that nearly linear curves were observed in the strain path plots. The minimum uniform deformation area for the 4:1 case had a radius of ∼1 mm, which is sufficient for experimental analyses, e.g., digital imaging correlation and electron beam backscatter diffraction. As an application for such heterogeneous materials, patient specific trauma fixation hardware, which are surgically implanted to set broken bones during healing, require high strength in areas where screws are located, i.e., martensite phase, yet low weight elsewhere.

This study focuses on algorithm for generation non-interference tool-path for manufacturing blades of integral impeller. The interference is classified into three kinds according to occurring area. The algorithm mentioned in the paper can be used to check not only interference between the cutter and the machined area of an integral impeller part, but also interference between the cutter and the tip area of blades, including the machining blade and the adjacent blade. The correction for tool-path is done to avoid the interference. Computer simulations of machining an integral impeller and correction for tool path are conducted to verify the efficiency of the algorithm.

Compared with other metals, titanium has a wide range of applications in laser induced forward transfer (LIFT) due to its unique properties of low thermal conductivity and high melting point. In general, the titanium film is used as a sacrificial layer or transferred material in LIFT with different laser fluence. In this study, four different topography types are classified under the laser irradiation of ultraviolet nanosecond pulses. For Ti films with different thicknesses, probability distribution of these types is provided to demonstrate how topographies evolve with the increasing laser fluence. Through the research, the understanding of the physical mechanism of titanium film would be deepened.

The Food Standards Agency commissioned Fera Science Ltd. to carry out a survey to obtain a snapshot of CBD products on sale in England and Wales in order to inform FSA risk assessment of CBD products. Thirty CBD products were purchased from a range of online sellers from England and Wales. Samples comprised of two broad categories: oils and sprays, and edibles (including beverages). The sampling followed a scheme suggested by FSA. This is not a statistically representative sample of the market and instead provides a snapshot of the current market, to assist the design of future sampling and surveillance activity. There is the potential for residues of chemicals to be present in CBD products as a result of their natural occurrence in the raw material or arising from the manufacturing process, for example, mycotoxins, metals, pesticides, and the residues of solvents used to extract CBD. This study informs the FSA’s understanding of the type and levels of contaminants that may arise in CBD products. A wide range of analysis on CBD products was undertaken using accredited methods, for heavy metals, Polycyclic Aromatic Hydrocarbons (PAHs), pesticides, mycotoxins, CBD content and cannabinoid profiles. Analysis for residual solvents and additional mycotoxins was also carried out, but these were not accredited. The results of testing found the following: Heavy metals (cadmium, mercury & lead) and arsenic were not detected in the majority of samples, meaning levels were below the limits of quantification of the method. Seven samples contained lead, four samples arsenic and two samples contained cadmium. Mercury was not found in any sample. A definitive statement as to whether products exceed maximum levels cannot be made due to uncertainty as to whether products would be classified as a food (i.e. oil) or a food supplement. A low incidence of low levels of mycotoxins, with Fusarium mycotoxins found more frequently than aflatoxins and ochratoxin A, mostly at the methods reporting limit. Three samples were found to contain ochratoxin A at the methods reporting limit. A total of seven pesticide residues were found across all of the products (each product was tested for over 400 pesticides). There are no specific Maximum Residue Limits (MRL) for CBD products. One oil product was found to have PAHs above the regulated levels, if classed as a product for direct consumption. If classed as a food supplement the PAHs were within regulated levels. Three samples contained residual solvents. One product was over the MRL. Most products contained CBD close to the declared value. Two oils had substantially different levels than that declared (one higher and one lower). CBD was not detected in one of the drink products. These are potentially non-compliant with compositional and standards requirements. Delta 9-THC was detected in 87 % (26) of the samples analysed. Of these 40% (12) were found to have THC+ (the total sum of illicit cannabinoids in the product) above the 1mg threshold outlined in current Home Office guidance (Opens in a new window).