Manufacturing Problems Research Articles

Bi-objective Circular-Hole Based Topology Optimization Problem in Additive Manufacturing

Bi-objective Circular-Hole Based Topology Optimization Problem in Additive Manufacturing

Determination of Rational Design and Geometric Parameters of a High-Performance Drill Based on a Mathematical Model of the Cutting Part

Processing of high-precision holes in one technological operation is an urgent problem of advanced manufacturing. Processing of precise holes in parts for aerospace and machine-building industries with a diameter of up to 30 mm is performed during countersinking, deployment or grinding operations. These operations are applied only if there already exists a pre-treated hole. Monolithic three-fluted drills have been becoming common for processing high-precision holes of 7-8 quality over the last few years. The processing of various types of materials such as stainless steels, cast iron and heat-resistant steels requires rational geometric and structural parameters of the cutting tool. The nature of the load distribution between all the teeth during drilling plays a huge role in the processing efficiency. Even load distribution between the three teeth and a positive geometry improves self-centering and reduces the deviation from the specified axis of the hole. The drill sharpening provides positive geometry along the entire main cutting edge. The influence of the geometric parameters of the cutting edge of the screw groove on the shape of the drill bit is equally important. Existing approaches to the design of the thinning do not account for the influence of the geometric parameters of the cutting edge on the section of the screw groove. Analytical approaches to modelling of the main cutting edges are typically married with difficulties associated with achieving a smooth change in the angle of inclination to the tangent of the cutting edge. The complexity of the aforementioned task is largely due to the presence of critical points at the interface of the spiral groove and thinning. Determining the rational shape of two sections of the main cutting edge performed in this study is a complicated task that includes several steps needed to find the number of nodal points. Achieving a positive rake angle in the normal section to the cutting edge at the gash area that was formed via a special sharpening is one of the most important results of this paper. The rational shape of the cutting edge and the front surface provides an increase in the strength of the cutting part by 1.3 times.

The integration of DFMA (Design for Manufacturing and Assembly) and Reverse Engineering (ER) Applied to a Landing Gear Redesign

The article addresses the redesign of a landing gear for a light aircraft that suffered from manufacturing and assembly problems. Numerical parameters of design features are related to design guidelines for identifying fabrication and assembly issues within 3D model analysis. For this work, the Lucas method of the DFMA integrated with reverse engineering was used as a methodology for the redesign, and its evaluation parameters for resizing the assembly item. Subsequently, the geometry of the redesigned component was defined, where significant reductions were obtained for the evaluation rates of the method used. Reduction from 20 pieces to just 6, which corresponds to a gain of 42% for functional analysis, reductions of 72% for power analysis and 84% for assembly analysis.

Welding Process Quality Improvement with Machine Learning Techniques

This paper presents a methodology for selecting design parameters with influence in quality product, using data analysis and machine learning techniques applied to a manufacturing problem where execution results can provide data for design improvement. Techniques are developed and used to extract the maximum information from existing process and quality data, and therefore to get feedback at design stages regarding how parameters variations affect the product performance when certain quality tests are applied. An algorithm is developed to sample multidimensional points in order to get the most representative subset, and various techniques are applied for data cleaning and outliers detection, such as Mahalanobis distance, Cook distance and HDOutliers method. Finally, inductive learning techniques, such as decision trees and association rules, are implemented to get conclusions about the relationships of the different variables. This paper finally shows this methodology in an automotive welding scenario.

Research on Localization of Serpentine Tube High Pressure Heater

It could be seen that the ultra supercritical reheating is a high-end technology in the field of power equipment manufacturing, which represents the strength and design level of national major equipment manufacturing industry. Based on the development of key components technology of millions of secondary reheat units, solving the design and manufacturing problems of serpentine high-pressure heater, optimizing the heat exchange efficiency of serpentine high-pressure heater, and improving the operation reliability, will help to promote the development process of China’s high-end power equipment, and provide important technical support for the quality supervision and management of serpentine high-pressure heater.

Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines

The additive manufacturing approach is considered a new manufacturing technology method and is evolving dynamically in recent years. It is advancing and achieving as the key enabling technology in a wide range of applications, from medical sciences to the aerospace and automotive industries. This novel approach opens a new path to overcome the conventional manufacturing problems and challenges by providing more design freedom, new ranges of materials, lightweight and complex geometries. According to demands metrics such as lightweight and high power density motors. This offers clear motivation to develop the advanced thermal management method with new materials and a novel additive manufacturing (AM) approach. The paper aims to provide a comprehensive review of all the attempts in various electrical machines' thermal management methods using AM method. It considers the opportunities and challenges that designers are facing while implementing these approaches. Finally, the authors make some comments/forecasts on how the AM could improve the performance and manufacturability of the future thermal management system of electrical machines.

Improving material quality management and manufacturing organizations system through Industry 4.0 technologies

In these changing times, Industry 4.0 technologies are being adopted in many parts of the world. These technologies are used to develop smart materials, smart factories, smart logistics, smart warehousing and smart supply chains. In the business aspect, it has the potential to solve complex manufacturing problems. This paper provides a thorough study of various Industry 4.0 technologies for improving the material quality and manufacturing systems. We have listed out various available technologies, their characteristics, and benefits of effective management in the upcoming industries. Digital technologies help companies optimize their material wastage and inventory which ends upin cost reductions. With remote collaboration, on-site and off-site employees can easily collaborate as and when needed. These technologies ultimately reduce communication gaps and help them stay up to date with the information. Smart predictive analysis will help to properly monitor the equipment in the industries using Industry 4.0 technologies. Faults in materials can be detected and resolved at an early stage. Further, this provides transparency in the systems and processes and fulfills the required expectation of the customer.

Robots Under COVID-19 Pandemic: A Comprehensive Survey.

As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.

Analytical Modeling of Residual Stress in Laser Powder Bed Fusion Considering Volume Conservation in Plastic Deformation

Residual stress (RS) is the most challenging problem in metal additive manufacturing (AM) since the build-up of high tensile RS may influence the fatigue life, corrosion resistance, crack initiation, and failure of the additively manufactured components. While tensile RS is inherent in all the AM processes, fast and accurate prediction of the stress state within the part is extremely valuable and results in optimization of the process parameters to achieve a desired RS and control of the build process. This paper proposes a physics-based analytical model to rapidly and accurately predict the RS within the additively manufactured part. In this model, a transient moving point heat source (HS) is utilized to determine the temperature field. Due to the high temperature gradient within the proximity of the melt pool area, the material experiences high thermal stress. Thermal stress is calculated by combining three sources of stresses known as stresses due to the body forces, normal tension, and hydrostatic stress in a homogeneous semi-infinite medium. The thermal stress determines the RS state within the part. Consequently, by taking the thermal stress history as an input, both the in-plane and out of plane RS distributions are found from the incremental plasticity and kinematic hardening behavior of the metal by considering volume conservation in plastic deformation in coupling with the equilibrium and compatibility conditions. In this modeling, material properties are temperature-sensitive since the steep temperature gradient varies the properties significantly. Moreover, the energy needed for the solid-state phase transition is reflected by modifying the specific heat employing the latent heat of fusion. Furthermore, the multi-layer and multi-scan aspects of metal AM are considered by including the temperature history from previous layers and scans. Results from the analytical RS model presented excellent agreement with XRD measurements employed to determine the RS in the Ti-6Al-4V specimens.

Reducing hot tearing by grain boundary segregation engineering in additive manufacturing: example of an AlxCoCrFeNi high-entropy alloy

One major hindrance that alloy design for additive manufacturing (AM) faces nowadays is hot tearing. Contrary to the previous works which either try to reduce solidification range or introduce grain refinement, the current work presents a new approach of employing segregation engineering to alter the residual stress states at the interdendritic and grain boundary regions and consequently prevent hot tearing. Here, in situ Al alloying is introduced into an existing hot-cracking susceptible high-entropy alloy CoCrFeNi. It is found that within a certain range of compositions, such as Al0.5CoCrFeNi, the hot crack density was drastically decreased. During the solidification of this specific alloy composition, Al is firstly ejected from the primary dendritic face-centred cubic (FCC) phase and segregates into the interdendritic regions. Spinodal decomposition then occurs in these Al-enriched regions to form the ordered B2 NiAl and disordered body-centred cubic (BCC) Cr phases. Due to the higher molar volume and lower homologous temperatures of these B2/BCC phases, the inherent residual strain is accommodated and transformed from a maximum 0.006 tensile strain in CoCrFeNi to a compressive strain of ~0.001 in Al0.5CoCrFeNi. It is believed that this grain boundary segregation engineering method could provide a new pathway to systematically counteract the hot tearing problem in additive manufacturing of metals and alloys, using available thermodynamic and kinetic database information.

The Impact of Consumers’ Peer-Induced Fairness Concerns on Mixed Retail and E-Tail Channels

With the rapid development of e-commerce, online retailing has become an important part of the market. In order to improve market competitiveness and increase market share, more and more retailers have opened both regular offline channel and online e-tail channel to sell products. Then how to price becomes an urgent problem for upstream manufacturers and dual-channel retailers when there is price competition between regular channel and e-tail channel, especially when consumers have peer-induced fairness concerns. However, linking consumers’ behavioral factors such as fairness concerns to pricing decisions of mixed retail and e-tail channels draws little attention in the literature on supply chain management. This paper incorporates “consumers’ peer-induced fairness concerns” (CPFC) into pricing decisions in a dyadic supply chain, where dual-channel retailer obtains products from manufacturers and then sells products to consumers through both regular channel and e-tail channel. We use game-theoretic models to analyze the equilibrium pricing strategies under the setting with “symmetry consumers’ peer-induced fairness concerns” (SCPFC) and with “asymmetry consumers’ peer-induced fairness concerns” (ACPFC), respectively. Detailed comparisons and numerical analysis are further conducted to examine the impacts of different types of CPFC on equilibrium pricing strategies and profits.

Brain Delivery of Nanomedicines: Trojan Horse Liposomes for Plasmid DNA Gene Therapy of the Brain.

Non-viral gene therapy of the brain is enabled by the development of plasmid DNA brain delivery technology, which requires the engineering and manufacturing of nanomedicines that cross the blood-brain barrier (BBB). The development of such nanomedicines is a multi-faceted problem that requires progress at multiple levels. First, the type of nanocontainer, e.g., nanoparticle or liposome, which encapsulates the plasmid DNA, must be developed. Second, the type of molecular Trojan horse, e.g., peptide or receptor-specific monoclonal antibody (MAb), must be selected for incorporation on the surface of the nanomedicine, as this Trojan horse engages specific receptors expressed on the BBB, and the brain cell membrane, to trigger transport of the nanomedicine from blood into brain cells beyond the BBB. Third, the plasmid DNA must be engineered without bacterial elements, such as antibiotic resistance genes, to enable administration to humans; the plasmid DNA must also be engineered with tissue-specific gene promoters upstream of the therapeutic gene, to insure gene expression in the target organ with minimal off-target expression. Fourth, upstream manufacturing of the nanomedicine must be developed and scalable so as to meet market demand for the target disease, e.g., annual long-term treatment of 1,000 patients with an orphan disease, short term treatment of 10,000 patients with malignant glioma, or 100,000 patients with new onset Parkinson's disease. Fifth, downstream manufacturing problems, such as nanomedicine lyophilization, must be solved to ensure the nanomedicine has a commercially viable shelf-life for treatment of CNS disease in humans.

A thermal multi-phase flow model for directed energy deposition processes via a moving signed distance function

Thermal multi-phase flow analysis has been proven to be an indispensable tool in metal additive manufacturing (AM) modeling, yet accurate and efficient simulations of metal AM processes remains challenging. This paper presents a flexible and effective thermal multi-phase flow model for directed energy deposition (DED) processes. Departing from the data-fitted or presumed deposit shapes in the literature, we first derive a deposit geometry model based on an energy minimization problem with a mass conservation constraint. Then, an interface-capturing approach based on a signed distance function that moves with the laser is constructed to represent the air–metal interface evolution. The approach can be applied to any type of mesh without requiring the activation process of solid elements in a mesh. The coupled multi-phase Navier–Stokes and energy conservation equations are solved by a variational multi-scale formulation (VMS). A density-scaled continuous surface force (CSF) model is employed to incorporate the Marangoni effect, no penetration boundary condition, and the heat source on the air–metal interface. We utilize the proposed method to simulate two representative metal manufacturing problems. The simulated results are carefully compared with available experimental measurements and computational results from others. The results demonstrate the accuracy and modeling capabilities of the proposed method for metal AM problems.

A General Variable Neighborhood Search approach based on a p-median model for cellular manufacturing problems

One of the practical application in cellular manufacturing systems is the cell formation problem (CFP). Its main idea is to group machines into cells and parts into part families in a way that the number of exceptional elements and the number of voids are minimized. In literature, it is proved that p-median is an efficient mathematical programming model for solving CF problems. In the present work, we develop a modified p-median based model dedicated to solve CFP respecting the objective of minimizing the sum of dissimilarities of machines. For this aim, we applied a General Variable Neighborhood Search algorithm and we collaborated it with an Estimation of Distribution Algorithm maximizing the group capability index and the grouping efficacy evaluation criteria. Thirty CF problems are taken from the literature and tested by our proposed algorithm and the experimental study demonstrated that the proposed method guided by p-median model provides high quality cells in speed running times and beats other state-of-the-art algorithms particularly for CF instances with large sizes.

Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop

As an indispensable part of workshops, the normalization of workers’ manufacturing processes is an important factor that affects product quality. How to effectively supervise the manufacturing process of workers has always been a difficult problem in intelligent manufacturing. This paper proposes a method for action detection and process evaluation of workers based on a deep learning model. In this method, the human skeleton and workpiece features are separately obtained by the monitoring frame and then input into an action detection network in chronological order. The model uses two inputs to predict frame-by-frame classification results, which are then merged into a continuous action flow, and finally, input into the action flow evaluation network. The network effectively improves the ability to evaluate action flow through the attention mechanism of key actions in the process. The experimental results show that our method can effectively recognize operation actions in workshops, and can evaluate the manufacturing process with 99% accuracy using the experimental verification dataset.

Flow capacity of the capillary copper tube as its figure of merit

The use of capillary copper tube is popular for manufacturing of gauges and evaporators of refrigeration units. Its main feature is a small inner diameter (no more than 0.9 mm), which makes it difficult to measure it along the whole length of the tube, thus, indirect test methods are used for its determination. Main problem in manufacturing of quality tubes is providing the necessary flow capacity. This article features these under-investigated issues by determining correlation between technological parameters and figures of merit of the capillary copper tubes. Specifically, it was found how flow capacity changes by the influence of such parameters as heat treatment (annealing), tube channel diameter and length of the treated product.

The problem of capsule manufacturing for hot isostatic pressing

The article deals with new materials, problems of manufacturing capsules for gas-sealing, the use of various types of welding to improve productivity and improve the quality of welds.

Reliability-based topology optimization as effective strategy for additive manufacturing: Influence study of geometry uncertainty on resulting layouts

The effectiveness of Additive Manufacturing (AM) is to fabricate very complex configurations that can be resulting layouts from topology optimization. The topology optimization can be classified into two models: Deterministic Topology Optimization (DTO) and Reliability-Based Topology Optimization (RBTO). The DTO may lead to unrealistic designs since it provides a single solution for a given design space. However, the RBTO is considered as a topology generator which provides several layouts. This way the designer can overcome the manufacturing constraint problems such as overhang, minimum feature size, anisotropy …, that may lead to a failure during the production process. Two RBTO strategies based on the Inverse Optimum Safety Factor (IOSF) are presented in this work: Objective-Based IOSF Approach and Performance-Based IOSF Approach. These approaches can be efficiently used as a topology regularization procedure. In addition, to consider the geometry uncertainty, different layouts can be obtained when increasing the reliability index values in order to provide more choices for designers. The geometry uncertainty is applied here in a manner to not affect the boundary conditions which can lead to change the structure’s functioning. The resulting RBTO layouts in this work, are too organic which leads to manufacturability problems. To use the conventional manufacturing procedures, classic CAD primitives must be used to simplify the geometry, that may lead to lose the optimality. Because of this manufacturability difficulty, AM is then obligatory. The RBTO being a good design tool for AM, paves the way to the next developments to overcome other fabrication constraints considering the reliability index values as a topology regulator.

Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification

Wafer bin maps (WBM) provides crucial information regarding process abnormalities and facilitate the diagnosis of low-yield problems in semiconductor manufacturing. Most studies of WBM classification and analysis apply a statistical-based method or machine learning method operating on raw wafer data and extracted features. With increasing WBM pattern diversity and complexity, the useful features for effective WBM recognition are highly dependent on domain knowledge. This study proposes an ensemble convolutional neural network (ECNN) framework for WBM pattern classification, in which a weighted majority function is adopted to select higher weights for the base classifiers that have higher predictive performance. An industrial WBM dataset (namely, WM-811K) from a wafer fabrication process was used to demonstrate the effectiveness of the proposed ECNN framework. The proposed ECNN has superior performance in terms of precision, recall, F1 and other conventional machine learning classifiers such as linear regression, random forest, gradient boosting machine, and artificial neural network. The experimental results show that the proposed ECNN framework is able to identify common WBM defect patterns effectively.

The role of ionic vs. non-ionic excipients in APIs-based eutectic systems

Aiming to contribute to drug pre-formulation, new eutectic mixtures were developed. Thymol, coumarin, or quaternary ammonium chlorides as excipients, were combined with the active pharmaceutical ingredients (APIs) acetylsalicylic acid, acetaminophen, ibuprofen, ketoprofen, or lidocaine. Their solid-liquid equilibrium (SLE) binary phase diagrams were measured to study eventual phase separation between the compounds, preventing manufacturing problems, and to study the molecular interactions between the APIs and ionic or non-ionic excipients. The Conductor-like Screening Model for Real Solvents (COSMO-RS) capability to predict the SLE of mixtures containing non-ionic excipients was further evaluated. COSMO-RS gives a good quantitative description of the experimental SLE being a tool with great potential in the screening of eutectic systems containing APIs and non-ionic excipients. While thymol presents strong interactions with the APIs, and consequently negative deviations to thermodynamic ideality, systems containing coumarin follow a quasi-ideal behavior. Regarding the ionic excipients, both choline chloride and the tetraalkylammonium chlorides are unable to establish relevant interactions with the APIs, and no significant negative deviations to ideality are observed. The liquefaction of the APIs here studied is favored by using non-ionic excipients, such as thymol, due to the strong interactions it can establish with the APIs.