Manufacturing Point Of View Research Articles

Fatigue strength of single-sided fillet welds in overlapping ultra-high-strength steel sheets

From the manufacturing viewpoint, overlapping thin sheets can provide a substantial geometrical improvement in welded hollow sections compared to butt-welded cross-sectional details. However, the plate eccentricity and non-penetrating fillet welds make the joints susceptible to fatigue failures under transversal cyclic loads. This work experimentally investigates the fatigue strength of overlap joints prepared with gas metal arc welding in the single-sided fillet weld configuration. Fatigue tests were carried out on the lap joints made of S960 ultra-high-strength steel (UHSS) grade under uniaxial constant amplitude axial loading employing both pulsating tension (applied stress ratio of R ≈ 0) and pulsating compression (R ≈ -∞). In addition, the lap joints were prepared with both straight welds (the welds transverse to the loading direction) and inclined welds (the welds with a 30° inclement angle from the transversal direction) to investigate the shear stress effects on the joints’ fatigue performance. Plasma butt-welded samples were tested as a reference join type. For the plasma butt-welded joints, the recommended detail category of FAT71 in the nominal stress system for weld root failures in single-sided butt welds was observed clearly conservative—a mean fatigue strength of Δσc,50% = 130 MPa with a fixed slope parameter of m = 3 was obtained. Compared to the butt-welded joints, a significant decrease in fatigue strength capacity was found in the lap joint specimens with misalignment factors of km > 3.0. The failure locations were also different in joints subjected to the tension and compression loads. The shear load did not majorly contribute to the changes in the fatigue strength capacity compared to the joints subjected to the transversal normal stress.

Digital fabrication with FDM 3D printing on prototype development of toys: Additive manufacturing and sustainability

Additive manufacturing, commonly referred to as 3D printing, is currently one of the most dominant developments in the manufacturing sector. Consumer demands for more customized goods and services, in conjunction with the emergence of advanced manufacturing technologies, are influencing changes in the scope and distribution of manufacturing. In this work, the function of digital fabrication, one of these advanced manufacturing process technologies, is examined. The Digital Manufacturing and even other advanced manufacturing technologies have a significant footprint in all future manufacturing endeavours because of the robustness and mass customization that they provide. The Fused Deposition Modelling (FDM) is a sustainable method used extensively for the development of prototypes for toys. The present study investigates the potential of FDM process as a method for prototype development. Additionally the advantages of FDM process from the viewpoint of sustainable manufacturing integrating innovativeness and do-it-yourself approach at home driven by consumer demand for products such as toys will also be investigated. 3D printing technology such as FDM process, where objects such as toy prototypes are constructed by adding materials layer by layer has been in the forefront of various Digital Manufacturing techniques as it reduces material wastage thereby optimizing resource utilization.

Meshes with Spherical Faces

Discrete surfaces with spherical faces are interesting from a simplified manufacturing viewpoint when compared to other double curved face shapes. Furthermore, by the nature of their definition they are also appealing from the theoretical side leading to a Möbius invariant discrete surface theory. We therefore systematically describe so called sphere meshes with spherical faces and circular arcs as edges where the Möbius transformation group acts on all of its elements. Driven by aspects important for manufacturing, we provide the means to cluster spherical panels by their radii. We investigate the generation of sphere meshes which allow for a geometric support structure and characterize all such meshes with triangular combinatorics in terms of non-Euclidean geometries. We generate sphere meshes with hexagonal combinatorics by intersecting tangential spheres of a reference surface and let them evolve - guided by the surface curvature - to visually convex hexagons, even in negatively curved areas. Furthermore, we extend meshes with circular faces of all combinatorics to sphere meshes by filling its circles with suitable spherical caps and provide a remeshing scheme to obtain quadrilateral sphere meshes with support structure from given sphere congruences. By broadening polyhedral meshes to sphere meshes we exploit the additional degrees of freedom to minimize intersection angles of neighboring spheres enabling the use of spherical panels that provide a softer perception of the overall surface.

Effect of Aircraft Health Management on Aircraft Maintenance Program Development by Aircraft Manufacturer

The objective of the research is to propose a system modeling method for aircraft maintenance program development applying the condition-based maintenance using AHM (Aircraft Health Management) from the viewpoint of aircraft manufacturer. The proposed model based on the MSG-3 (Maintenance Steering Group - 3) considers the uncertainty of aircraft maintenance environment related to the airline operation and assumed system degradation levels. Then, the proposed model is formulated by using the concept of a robust optimization method.

How do well-performed food businesses manage suppliers in emerging economies?

PurposeThis study aims to investigate the supplier selection criteria, relationship quality and level of collaboration in Asian food businesses, while also examining the link between these practices and business performance. The research focusses on food manufacturing and exporting companies in two emerging economies, China and Vietnam.Design/methodology/approachFood manufacturing and exporting companies in two emerging economies – China and Vietnam were surveyed and analysed to extract factors that reflect supplier management and business performance along with their relationship by factor analysis and hierarchical regression. Then, a two-step cluster analysis was applied to identify clusters based on supplier management and explore how different business performance groups manage their suppliers.FindingsFour clusters that are distinct sets of food firms with detailed references about their typical characteristics revealing their business performance and supplier management practices. Also, the study confirms that certificates, reliability and inspection results constitute the factor of quality-related criteria for food firms. It is an interesting insight into what firms prioritise in selecting and maintaining collaboration and relationships with suppliers that reflect actual demanding specifications for supplier conformity.Research limitations/implicationsThe study reveals the business status of the studied companies and each group's specific references, such as the criteria they prefer to select suppliers, the relationship quality and the level of collaboration.Originality/valueThe study is a useful reference for both researchers and practitioners to have a comprehensive view of supplier management in the food industry based on the viewpoint of Asian food manufacturers.

Research on User Stickiness of Logistics Platform and its Influencing factors

Nowadays, our society has undergone a change in the viewpoint of production and manufacturing, and will pay more attention to the needs of the majority of users, and gradually began to make demand-oriented research topics for the development of the times. Logistics platform is also under such a trend, pay more attention to user needs and experience. As the business focus of logistics platforms, how to cultivate user stickiness and continuously cultivate new users and retain old users under the competitive market situation has become a major problem for enterprises. Around these issues, this paper conducts a systematic and in-depth study. Based on the S-O-R theory, this paper uses the stimulus-organism-response model to answer the question of how to continuously improve the participation and willingness of platform users by finding factors that affect the stickiness of logistics platform users. Systematic analysis and process research of logistics platform-related literature guided by the research question identifies four factors that influence logistics platform user stickiness, namely platform gu-enterprise interaction, platform atmosphere cues, user fit and consumer trust, and includes platform network effect as a moderating variable in the research framework. Through the empirical study, the following conclusions were drawn: platform gu-enterprise interaction has a significant effect on user fit, platform atmosphere cues have a significant effect on consumer trust, and user fit and consumer trust have a significant effect on user stickiness. User fit mediates the influence of information request, switching cost, and personnel communication on platform user stickiness, and consumer trust mediates the influence of platform reputation, personalized recommendation, and service remediation on platform user stickiness. Finally, certain management suggestions and research limitation outlooks are given to managers of logistics platforms.

Study and comparison of different Machine Learning-based approaches to solve the inverse problem in Electrical Impedance Tomographies

Electrical Impedance Tomography (EIT) is a non-invasive technique used to obtain the electrical internal conductivity distribution from the interior of bodies. This is a promising method from the manufacturing viewpoint, since it could be used to estimate different physical inner body properties during the production of goods. Nevertheless, this technique requires dealing with an inverse problem that makes its usage in real-time processes challenging. Recently, Machine Learning techniques have been proposed to solve the inverse problem accurately. However, the majority of prior research is focused on qualitative results, and they typically lack a systematic methodology to determine the optimal hyperparameters appropriately. This work presents a systematic comparison of six popular Machine Learning algorithms: Artificial Neural Network, Random Forest, K-Nearest Neighbors, Elastic Net, Ada Boost, and Gradient Boosting. Particularly, the last two algorithms were based on decision tree learners. Furthermore, we studied the relationship between model performance and different EIT configurations. Specifically, we analyzed whether the measurement pattern and the number of used electrodes could increase the model performance. Experiments revealed that tree-based models present high performance, even better than Neural Networks, the most widely-used Machine Learning model to deal with EIT. Experiments also showed a model performance improvement when the EIT configuration was optimized. Most favorable metrics were attained using the tree-based Gradient Boosting model with a combination of both adjacent and mono measurement patterns as well as with 32 electrodes deployed during the tomographic process. With this particular setting, we achieved an accuracy of 99.14% detecting internal artifacts and a Root Mean Square Error of 4.75 predicting internal conductivity distributions.

High Functionalization and Multi-functionalization of Dental Materials from the Viewpoint of Manufacturing

High Functionalization and Multi-functionalization of Dental Materials from the Viewpoint of Manufacturing

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms' characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario.

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries.

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization’s decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer’s, policymaker, and researchers’ viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption.

Perceived impact of the patent linkage system on pharmaceutical market from the viewpoint of the domestic manufacturers in South Korea

BackgroundThe United States requires a patent linkage system in other countries as part of free trade agreements. However, introducing a patent linkage system could be a significant barrier to the timely approval of generic drugs. This study aimed to evaluate the perceived impact of the patent linkage system in South Korea held by domestic manufacturers and analyze variations in evaluating the system according to the characteristics of domestic manufacturers.MethodsIn 2020, we conducted a questionnaire survey of 39 domestic manufacturers. The survey consisted of perceptions of the system, factors affecting patent challenges, and the perceived impact of the system. A 5-point Likert scale was used to rate each item. Domestic manufacturers were categorized into three groups based on their experience of listing a patent and acquiring first generic exclusivity.ResultsMore than half of the manufacturers surveyed had experience of listing a patent. The patent linkage system could protect the involved patents. However, manufacturers perceived that they could successfully challenge the validity of the involved patents and then obtained market approval for generic drugs. Manufacturers responded that market size, expectations for succeeding in litigation, and expectations for manufacturing the drug were the most relevant factors when they initiated patent challenges. Manufacturers reported that the system, in particular the first generic exclusivity, enhanced the research and development capability of generic manufacturers, increased their domestic sales, and improved access to generic drugs.ConclusionsThe perceived impact of the patent linkage system was limited to the domestic market and generic drugs. In narrowing the impact to the effects on the domestic industry, the system had positive impacts of the system on generic manufacturers. The first generic drug exclusivity lies at the center of this positive perception. However, manufacturers perceived that the current system did not provide enough incentives for domestic manufacturers to be granted first generic drug exclusivity through patent challenges.

Optimization Model in Manufacturing Scheduling for the Garment Industry

The garment industry in Vietnam is one of the country's strongest industries in the world. However, the production process still encounters problems regarding scheduling that does not equate to an optimal process. The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint. A number of constraints were considered in the model and is applied to a real case study of a factory in order to view how the tardiness and lateness would be affected which resulted in optimizing the scheduling time better. Specifically, the constraints considered were order assignments, production time, and tardiness with an objective function which is to minimize the total cost of delay. The results of the study precisely the overall cost of delay of the orders given to the plant and successfully propose a suitable production schedule that utilizes the most of the plant given. The study has shown promising results that would assist plant and production managers in determining an algorithm that they can apply for their production process.

Fast and efficient approach for multi-component quantum wells analysis based on FFT

Elaboration of the technology of the multicomponent Quantum Wells (QWs) based devices requires repeated time consuming diffraction curves analysis proceeded for each of the grown structures to determine their basis structural parameters. Optimization of the diagnostic procedures and making them more efficient is the key point from the manufacturer point of view. In this paper we present the use of Fast Fourier Transform (FFT) employed to fast determination of the thickness of epitaxial layers. The presented method combines the approach using the Rocking Curves (RC) measured by X-ray Diffraction (XRD) together with the analysis of the FFT spectrum. In this work GaInNAs/GaAs MQWs structures grown by atmospheric pressure-metalorganic vapour phase epitaxy (AP-MOVPE) were investigated. In the case of the series of the defined type samples, this method allows replacement of the time consuming simulation method used for diagnostic of the thickness of deposited layers in favor of FFT spectrum analysis.

Ultrathin and high-efficiency Pancharatnam–Berry phase metalens for millimeter waves

Applying the Pancharatnam–Berry (PB) principle to half-wave plate (HWP) metasurfaces allows the manipulation of wavefronts along with the conversion of the handedness of circularly polarized incident waves by simply rotating the meta-atoms that compose the metasurface. PB metasurfaces (PBM) working in transmission mode with four or more layers have been demonstrated to reach levels of transmission efficiency near 100% but also have resulted in bulky structures. On the other hand, compact tri-layer ultrathin (λ/8) designs have reached levels near 90% but are more challenging than single- or bi-layer structures from a manufacturing viewpoint. Here, we propose a compact ultrathin (<λ/13) transmissive PBM with only two layers (which significantly simplifies the fabrication process) achieving a transmission efficiency level of around 90%, focusing the wavefront of a circularly polarized incident wave and converting its handedness. The metasurface is composed of identical bi-layered H-shaped unit cells (meta-atoms) whose transmission phases are chosen by introducing different rotation angles to each unit cell according to a lens spatial phase profile. The structure is analytically and numerically studied and experimentally measured, verifying an excellent behavior as an HWP PB metalens at 87 GHz.

Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology (OT) which forms the main attack surface of manufacturing systems. As some intrusion detection systems (IDS) already involve some AI-based techniques, we focus on existing machine-learning and data-mining based techniques in use for intrusion detection. This article presents the major strengths and weaknesses of the main techniques in use. We also discuss an assessment of their relevance for application to OT, from the manufacturer point of view. Another part of the paper introduces the essential drivers and principles of Industry 4.0, providing insights on the advent of AI in manufacturing systems as well as an understanding of the new set of challenges it implies. AI-based techniques for production monitoring, optimisation and control are proposed with insights on several application cases. The related technical, operational and security challenges are discussed and an understanding of the impact of such transition on current security practices is then provided in more details. The final part of the report further develops a vision of security challenges for Industry 4.0. It addresses aspects of orchestration of distributed detection techniques, introduces an approach to adversarial/robust AI development and concludes with human–machine behaviour monitoring requirements.

Synthesis and Experimental Characterization of a MWCNT-Filled Bio-Based Adhesive

In the present paper, a novel epichlorohydrin/cardanol adhesive was reinforced by multi-walled carbon nanotubes (MWCNTs) and characterized experimentally. The adhesive was reinforced by MWCNTs in weight ratios (wt %) of 0.5%, 1.0% and 2.0%. The bulk properties of the reinforced adhesive were characterized through dynamic mechanical analysis tests, tension tests, and fracture toughness tests, while its shear behavior was characterized through single-lap shear tests on aluminum and composite bonded specimens. The morphology of the reinforced adhesive was characterized using scanning electron microscopy tests. Due to the high viscosity of the bio-based adhesive, special efforts were placed on the dispersion of the MWCNTs into the adhesive, which was achieved through mechanical mixing. The results from the tests show that the presence of the MWCNTs increases the glass transition temperature, the Young’s modulus and the fracture toughness of the reinforced bio-based adhesive, while it decreases its tensile strength. This contradictory finding is attributed to the formation of MWCNT agglomerates into the adhesive. For the content of 2.0 wt %, the shear strength of the reinforced adhesive is increased by 57% for the aluminum joints and by 10.4% for the composite joints. The findings of the study reveal that the reinforcement of the bio-based adhesive by MWCNTs is feasible from a manufacturing viewpoint and may increase the efficiency of the adhesive in structural applications.

Manufacturing and performance of environment-friendly lightweight aggregates with core-shell structure

The production of lightweight aggregates (LWAs) involves much energy consumption and emission of pollution during the sintering process. The increasing worldwide demand for functional concrete for use in modern construction has promoted this negative trend to a more serious level. This study reports an artificial lightweight capsule aggregates (CAs) based on an encapsulation technology from the viewpoint of green manufacturing. The novel CAs are composed of a fly ash-cement inorganic shell and sodium alginate organic core. A series of experiments were conducted to study the influences of the rotation rate, duration time, curing conditions and surface conditions for sodium alginate beads (SAB) on the physical and mechanical characteristics of the CAs. The results indicate that CAs with a wet density of 1340 kg/m3 and corresponding crush strength of 2.46 MPa can be achieved. The CAs density can be further decreased by a third via sodium alginate dehydration. CAs with optimized performance can be synthesized by using a rotation rate of 150 rpm, duration time of 30 s and C/A ratio of 2.0. A low rotation rate and high duration time have a negative effect on the CAs characteristics. A saturated surface dry (SSD) condition process for the SAB was found to greatly contribute to the optimization of CAs density. Findings from this study can provide a novel and viable way to reuse industrial wastes and biomass resources to manufacture core-shell structured LWAs with stable and adjustable performance. These artificial LWAs have great application potential in the preparation of the advanced lightweight concrete.

Temperature-Dependent Electrical Characteristics of Disc-Shaped Compacts Fabricated using Calcined Eggshell Nano Powder and Dry Cassava Starch

Abstract Disc-shaped compacts were fabricated from two mix proportions of calcined eggshell nanopowder and dry cassava starch and then used as test samples. The electrical resistance (R), thermal sensitivity index (β) and electronic activation energy (Ea) of the samples measured over a temperature range from 35 to 75oC were found to decrease non-linearly in values with increasing temperature. It was also observed that the results obtained (R = 3.691E6 Ω – 6.210E7 Ω, β = 3812K – 5316K and Ea = 0.33 eV – 0.46 eV) fulfill market requirements by comparing very well with the established values for NTC thermistors. Hence, from manufacturing viewpoint, recycling of chicken eggshell wastes and cassava effluents can avail electronic industry with promising and alternative materials for fabrication of temperature sensing / monitoring / control devices suitable for engineering applications. This will also help to reduce environmental pollution.

Single Machine Scheduling with Outsourcing Under Different Fill Rates or Quantity Discount Rates

In this paper, we consider two single machine scheduling problems with outsourcing under different fill rates or quantity discount rates. From the manufacturer point of view, to maintain a predefined high service level under limited production capacity, it is required that the number of outsourced jobs must belong to a given interval [Formula: see text]. We call this model “scheduling with outsourcing under different fill rates”. Meanwhile, from the outsourcers point of view, in order to encourage more outsourcing actions from the manufacturer, the outsourcers might provide an attractive discount scheme. Here, we assume that all discount rates depend only on the quantity (number) of the outsourced jobs. That is, the actual outsourcing cost equals to the original outsourcing cost multiplied by the corresponding discount rate. This model is called “scheduling with outsourcing under different quantity discount rates”. The objective is to minimize the sum of the makespan of the processed jobs and the (actual) outsourcing costs of the outsourced jobs. For the above two problems, some optimal algorithms, approximation algorithms and approximation schemes are proposed.

Assessment of water-based cutting fluids with green additives in broaching

In order to improve the cutting performance in broaching, the lubrication and cleaning effects offered by water-based cutting fluids with green additives need to be studied from the viewpoint of green manufacturing. Therefore, water-based solutions with castor oil, surfactant (linear alkylbenzene sulfonate, LAS), and nanographite were prepared by ultrasonic agitation and sprayed into the zone of broaching via atomization. The performances of the cutting fluids, in terms of the viscosity, specific heat, wetting angle, and droplet size, were evaluated to discuss their effects on the broaching load. Among the fluids, the addition of LAS into oil-in-water (WO-S), where its cutting fluid with 10 wt.% castor oil and 1.5 wt.% surfactant, exhibited the lowest broaching force. With regard to the lubricating and cleaning mechanisms, WO-S has good wettability and permeability, and hence, can lubricate the cutting edge of the tool to decrease the cutting load, cool the cutting edge to keep it sturdy, and clean the surface of the cutting edge to keep it sharp. The results reveal that the simultaneous addition of castor oil and LAS had remarkable effects on the lubrication and cleaning, and resulted in a broaching load reduction of more than 10% compared to commercial cutting fluids. However, the addition of nanographite could not improve the lubrication owing to its agglomeration.