Manufacturing Problems Research Articles

Deformation behavior and microstructure evolution of one novel superalloy with high rare earth gadolinium content: Multi-scale modeling and experimental study

It has been proved that rare earth elements have a positive effect on the performance of superalloys and steels at only trace levels, but rare earth phases are always tricky in the manufacturing process. In this work, one Ni–Mo–Cr-Gd alloy with ultrahigh rare earth Gd content (1.75 wt %) was designed to reveal the influence of hard and brittle Ni5Gd particles on hot deformation behavior and microstructure evolution. The Ni–Mo–Cr-Gd alloy exhibits higher deformation activation energy and smaller processable regions in the processing maps as compared with the Ni–Mo–Cr alloy. Combining electron back scatter diffraction and crystal plastic finite element method, it was found that strain concentrates around Ni5Gd particles, especially at grain boundaries, and rapidly reaches the critical value for recrystallization, which verifies particle stimulated nucleation. The recrystallization fraction decreases with strain rates from 0.01 to 1 s−1 but increases from 1 to 10 s−1, which is because meta-dynamic recrystallization occurs faster when the strain rate is greater than 1 s−1 and Ni5Gd particles promote the process indirectly by introducing more severely deformed matrix. Furthermore, the finite element modeling shows that plastic strain and temperature rise at the central region of samples increase with strain rate increasing, which can explain the unusual fragmentation of Ni5Gd at 1200 °C with the highest strain rate. Our results firstly reveal the evolution mechanism of Gd-containing superalloy during hot deformation, which can help to solve the manufacturing problems of similar alloys.

Pickup and Delivery Vehicle Routing Problem with Hybrid Missions and Third-Party Vehicle Rentals: A Case Study of Animal Feed Industry

The vehicle routing problem for small and medium-sized animal feed manufacturers is a complex challenge because each transport route can vary significantly between delivering products to customers and picking up raw materials to return to the factory. Currently, businesses lack well-planned management for utilizing a limited number of trucks. As a result, they often incur additional costs by relying on third-party vehicle rentals. This research aims to reduce transportation costs for small and medium-sized animal feed manufacturers by solving a formulated mixed-integer linear programming (MILP) model, using experimental data that accurately reflects the actual problems faced. The MILP model results help reduce transportation costs by up to 18.5% compared to current practices by optimizing route sequences and incorporating the use of third-party vehicle rentals. This also serves as a guideline for reducing transportation costs in other similar industries.

A Chimera method for thermal part-scale metal additive manufacturing simulation

This paper presents a Chimera approach for the thermal problems in welding and metallic Additive Manufacturing (AM). In particular, a moving mesh is attached to the moving heat source while a fixed background mesh covers the rest of the computational domain. The thermal field of the moving mesh is solved in the heat source reference frame. The chosen framework to couple the solutions on both meshes is a non-overlapping Domain Decomposition (DD) with Neumann–Dirichlet transmission conditions.Increased steadiness and accuracy within the vicinity of the Heat Affected Zone (HAZ) are the main advantages of this approach. The steadiness gain allows for the use of larger time steps, which is vital in AM applications and, in particular, Laser Powder Bed Fusion (LPBF), where the disparity of time scales represents a major hurdle. Moreover, enhanced accuracy can be observed in the resulting morphology of the melt pool. It will be shown that the method addresses classical shortcomings pointed out by Goldak without requiring the use of an asymmetrical heat source profile.

Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking

The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compaction. Each step is fraught with manufacturing problems. Undesired adhesion of powders to the surface of the compaction tooling, known as sticking, is a frequent and highly disruptive problem that occurs at the very end of the process chain when the tablet is formed. As an alternative to the mechanistic approaches to address sticking, we introduce two different machine learning strategies to predict sticking directly from the chemical formula of the drug substance, represented by molecular descriptors. An empirical database for sticking behaviour was developed and used to train the machine learning (ML) algorithms to predict sticking properties from molecular descriptors. The ML model has successfully classified sticking/non-sticking behaviour of powders with 100% separation. Predictions were made for materials in the handbook of Pharmaceutical Excipients and a subset of molecules included in the ChemBL database, demonstrating the potential use of machine learning approaches to screen for sticking propensity early at drug discovery and development stages. This is the first-time molecular descriptors and machine learning were used to predict and screen for sticking behaviour. The method has potential to transform the development of medicines by providing manufacturability information at drug screening stage and is potentially applicable to other manufacturing problems controlled by the chemistry of the drug substance.

A comparison of recent optimization algorithms for build orientation problems in additive manufacturing

Abstract Build orientation in additive manufacturing technology is a pre-process application that affects many parameters, such as the volume of the support structure, part quality, build time, and cost. Determining the optimum build orientation for one or more objectives for complex parts is an error-prone puzzle. This study evaluates the behavior of cuckoo search algorithm, differential evolution, firefly algorithm, genetic algorithm, gray wolf optimizer, Harris hawks optimization, jaya algorithm, moth flame optimizer, multi-verse optimizer, particle swarm optimization, A Sine cosine algorithm, salp swarm algorithm, and whale optimization algorithm to determine the optimum build orientation of the component to be manufactured additively. The efficiency of these algorithms is evaluated on the build orientation problem of two complex components considering undercut area and build height as objective functions. Thus, the feasibility of these algorithms for real-world additive manufacturing problems is revealed. According to results obtained from the extensive analysis, the cuckoo search algorithm is the best alternative for minimizing undercut area, considering its robustness. However, the required time to solve the problem is as much as almost twice that of other algorithms. The firefly algorithm and particle swarm optimization algorithm are the best alternatives for minimizing build height.

Computer Numerical Control Machining Simulations and Experimental Analysis of a Novel C-Gear

C-gear is a novel gear transmission system, with no existing special machining and cutting tools to realize its construction. In this study, we employed a 5-axis computer numerical control (CNC) machining center to construct a C-gear, while solving the prototype manufacturing problem at the experimental research stage. We deduced the C-gear mathematical model and studied the three-dimensional (3D) modeling methods based on the analysis of its generation principle. The C-gear processing technique using the 5-axis CNC machining center and the tool path designs were established using the VERICUT and MASTERCAM softwares, and the C-gears were experimentally constructed. The maximum overcut and residual of the gear positioning hole surface were both found to be 0.094 mm. Moreover, the maximum overcut on the tooth surface and root were 0.02 and 0.03 mm, respectively, and the maximum residual on both the tooth surface and root was 0.03 mm. The machining overcuts and residual amounts of the proposed method were better than those of reference [14]. The existing overcut and residual errors included the errors generated by surface fitting during the 3D modeling of the gears. Therefore, the machining errors of the physically constructed tooth surface are expected to be smaller than the theoretical value, suggesting a high feasibility of the proposed method to realize the machining of C-gears.

The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems

The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems

A Mathematical Programming Model for Minimizing Energy Consumption on a Selective Laser Melting Machine

The scheduling problem in additive manufacturing is receiving increasing attention; however, few have considered the effect of scheduling decisions on machine energy consumption. This research focuses on the nesting and scheduling problem of a single selective laser melting (SLM) machine to reduce total energy consumption. Based on an energy consumption model, a nesting and scheduling problem is formulated, and a mixed integer linear programming model is proposed. This model simultaneously determines part-to-batch assignments, part placement in the batch, and the choice of build orientation to reduce the total energy consumption of the SLM machine. The energy-saving potential of the model is validated through numerical experiments. Additionally, the effect of the number of alternative build orientations on energy consumption is explored.

Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer’s time availability in cloud manufacturing

ABSTRACT As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer’s capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

Overview and Potential Solutions of Current and Future Challenges in CMP

This talk is dedicated to the special session in honor of Prof. Babu Shrink of critical dimensions and stringent technology specifications are posing several planarization challenges in technology development (TD) and high-volume manufacturing (HVM) phases of current and advanced technology nodes. In this talk, we will focus on process challenges related to achieving (a) minimum defects (b) high planarization efficiency with less than 1nm dishing (c) tighter WIW and WID uniformities (d) ultra-smooth post-CMP surface roughness in the order of sub-nanometer. In addition, identifying appropriate endpoint control techniques combined with advanced process control strategies to provide good process margin will also be discussed. Addressing the above challenges in a cost-effective manner is vital for HVM. Root cause analysis and probable working models will be discussed for high-risk process challenges. Based on our understanding, a jist shall be provided about challenges associated with future technology nodes in logic and memory industries, with focus on how (a) technical understanding, (b) change in CMP ecosystem, and (c) AI can help solving technology development and high-volume manufacturing problems.

Multi-attribute optimization of eco-manufacturing based criteria in CNC manufacturing systems using an evolutionary algorithm

Manufacturing technology has evolved over the years with the development of CNC manufacturing systems, flexible manufacturing, rapid prototyping, smart manufacturing etc Simultaneously, the development of new and exotic materials to match specific requirements has shaped new problems in manufacturing. The materials developed thus far require special tools, lubricating agents, and extra-care in machining without compromising the quality. Further the study of carbon emissions in manufacturing sector has also gained unusual spotlight in view of their deleterious effect on the ecological balance. The manufacturing systems have to be consequently designed and developed, such that they generate minimal quantity of emissions without forfeiting the prime objectives of quality and tool morphology. The present work is principally intended to analyse the effect of cutting parameters on the emission rate of greenhouse gases, tool wear and work-piece temperature concurrently. These studies are accomplished in both dry and wet conditions on computer numerical control machining system. The machining process involved plain facing of a Ti-6Al-4V hardened material. The experimental studies are realized using both single point and multi-point cutting tools and are supplemented with the application of Multi-Objective Genetic Algorithm (MOGA). The MOGA generated set of pareto-fronts for the four machining conditions were subjected to VIKOR, TOPSIS and LINMAP decision making approaches to arrive at the optimum values of decision variables. The optimum cutting parameters obtained in single point cutting tool machining in dry conditions are speed (873.2 rpm), feed (0.199 mm rev−1) and depth of cut (0.25 mm), while the corresponding values of responses are tool wear (67.19 μm), work-piece temperature (39.36 °C) and carbon emission (0.138 Kg-CO2). The equivalent values for multi-point cutting were determined as 899.8 rpm, 0.195 mm rev−1 and 0.25 mm, while the responses for these optimal conditions are 69.92 μm, 39.48 °C and 0.137 Kg-CO2 in that order.

Solving the project scheduling problem with dependent resource constraints

ABSTRACT Most resource-constrained project scheduling problems consider resources that are independent of the job processing sequence. However, solving project scheduling problems in some industrial manufacturing is challenging because the required resources depend on the job processing sequence in existing scheduling approaches. To address this issue, this paper proposes a dependent resource-constrained project scheduling problem formulated with two types of models: a conceptual model and a binary integer programming model. The performance of four proposed algorithms – binary integer programming, reduced subproblem of the binary integer programming, job sequence assignment, and job as early as possible – is tested on 68 combinations of numerical simulation experiments. The computational results show that the job as early as possible algorithm can find a better approximate solution for solving real-world problems. This achievement aids project managers in generating activity schedules and finding the approximate minimum makespan for a project with dependent resource constraints.

An effective hybrid meta-heuristic method for the simultaneous batch production and transportation problem in additive manufacturing

One notable advancement in additive manufacturing (AM) is the mobile mini-factory, which uses a truck equipped with an AM machine to produce orders while en-route to customers' locations. This offers potential benefits such as reduced delivery times and storage expenses for companies. This study investigates a simultaneous batch production and transportation problem (denoted by SBPTP) in additive manufacturing. To solve this problem, a mixed integer linear programming (MILP) model is first formulated. Then, to solve large-scale problems, a meta-heuristic method (denoted by SA-CP) combining a simulated annealing (SA) algorithm, an ant colony optimisation algorithm (ACO) and a cutting-plane algorithm is developed, in which the assignment subproblem is dealt with the SA, the simultaneous production and transportation subproblem is dealt with the ACO, and finally the current solution is further improved by the cutting-plane algorithm. Computational experiments are conducted on both randomly generated instances and modified benchmark instances. The results demonstrate that the SA-CP is very effective since it can obtain the solutions with an average relative percentage gap 0.16% on randomly generated instances and −0.09% on modified benchmark instances within less than 180 CPU seconds, compared to those obtained by solving the MILP model directly with CPLEX within 1 h.

Improving the formability and quality of CMT additively manufactured aluminum thin-wall parts via laser stabilization effect

In the recent past, the deposition efficiency has been one of the most concerned problems in aluminum additive manufacturing. In this research, a comparative study on the formation quality, microstructures and mechanical properties of aluminum alloy thin-wall structures between the oscillating laser-arc hybrid additive manufacturing (O-LHAM) and wire-arc additive manufacturing (WAAM) at high deposition speed was performed. The formation quality of the additively manufactured thin-wall parts under O-LHAM was significantly improved compared with WAAM. The molten pool overflow was significantly eliminated due to the higher support force on the molten pool, thus the surface roughness Sa and the maximum surface height difference Sz of O-LHAM thin walls were reduced by 13% and 22%, respectively. Compared with thin wall manufactured via WAAM, the microstructure of O-LHAM mainly consisted of coarse columnar grains and showed anisotropic characteristics due to the increased heat accumulation. Moreover, owing to the higher porosity in the interlayer combination regions of WAAM sample, the elongation of thin walls manufactured via O-LHAM was higher than WAAM in both horizontal and vertical directions, reaching 36.8% and 32.5%, respectively. This work will provide more guidance on thin-wall additive manufacturing with high deposition efficiency.

Noise Analysis and Structural Optimization of Automobile Scroll Compressor Air Valve

The air conditioning compressor is a critical component in automobile heating, ventilation and air conditioning systems. However, compressor noise has long been a problem for automobile manufacturers. In recent years, the development and application of automobile air conditioning scroll compressors has increased significantly due to their low mechanical vibration and noise. However, their limitations in terms of airflow pulse and noise cannot be ignored, especially in low speed and high load conditions where the noise generated has a negative impact on driving and passenger experience. Noise and airflow pulses are important considerations that cannot be ignored. This study innovatively modifies the end cap structure of the scroll compressor, using the principles of expansion muffler and insertion tube structure, with the aim of improving the acoustic quality of the scroll compressor. The results show that the novel valve construction can significantly reduce the sound pressure level of the scroll compressor noise to a maximum of 75.20 dBA. The results of this study provide a theoretical basis and practical technical applications for future research and development in the automobile industry.

Trends in Shortages of Lead Chelators From 2001 to 2022.

The study aims to describe drug shortages affecting lead chelators in the United States from 2001 through 2022. Drug shortage data were retrieved from the University of Utah Drug Information Service from January 1, 2001, through December 31, 2022. Shortages of first- and second-line lead chelators were analyzed. Drug class, formulation, administration route, shortage reason, shortage duration, generic status, single-source status, and presence of temporally overlapping shortages were examined. Total shortage months, percentages of study period on shortage, and median shortage durations were calculated. Thirteen lead chelator shortages were reported during the study period. Median duration was 7.4 months and the longest shortage (24.8 months) involved calcium disodium edetate. Calcium disodium edetate and dimercaprol had the greatest number of shortages, 4 each, and 61.5% of shortages involved parenteral medications. Median shortage duration was 14.2 months for parenteral agents and 2.2 months for non-parenteral agents. All shortages involved generic, single-source products. Supply/demand and manufacturing problems were the most common shortage reasons provided. Overlapping shortages occurred for 3.7% of the study period. Median shortage duration increased from 3 to 11 months in the second half of the study period, and 61.5% of shortages occurred in the second half of the study period. All chelators experienced multiple shortages, which became increasingly frequent and prolonged over time. Concurrent shortages occurred, potentially hampering substitution between different agents. Health care stakeholders must build supply chain resilience and develop guidelines regarding how to modify chelation therapy based on shortage conditions.

Research on the Application of Precision Detection Technology in Machining and Manufacturing

In recent years, the speed of China's social and economic development continues to accelerate, and the quality requirements of machining and manufacturing have been improved. At present, there are problems in machining and manufacturing in China, which cannot guarantee the effect of quality control, which is not conducive to the improvement of the precision of machining products, and the application of precision detection technology in machining and manufacturing can comprehensively detect the precision of products. This paper accurately analyzes quality problems and provide support for improving the quality of machining and manufacturing. Based on this, this paper analyzes the significance of the application of precision detection technology in machining and manufacturing, and puts forward the application measures to provide help for improving the quality of machining and manufacturing.

Research and Validation of CF/PEEK-Based Truss Rod Crimping and Pultruding Process for On-Orbit Isoform Forming.

A crimping and pultruding forming process for truss rods using Carbon Fiber (CF)/Polyether-Ether-Ketone (PEEK) prepreg tape as the raw material is proposed to address the problem of continuous manufacturing of space trusses on orbit. The proposed process provides material rods for continuous truss manufacturing. Through numerical simulation and experimental verification, the effects of relevant parameters on the forming process are determined, an efficient method of rod curl pultrusion, in-rail, equal material forming is proposed, and the structural configuration of the rod curl pultrusion forming mold is determined. The equivalent macroscopic mechanical properties of unidirectional CF/PEEK prepreg strips are considered, and the rod-forming process is investigated. Rod samples with different process parameters are prepared, and several tests are conducted on them. The results show that the forming load pull is negatively correlated with the temperature at the same forming speed, and forming speed is positively correlated with the forming load pull at a certain temperature. Temperature and speed affect the surface quality of the rod, the density of the material filling, and the mechanical properties of the rod. The optimal forming process parameters are determined through numerical simulation and experimental verification. The developed molding technology has the advantages of high efficiency, low energy consumption, and high integration. It reduces manufacturing costs and improves manufacturing efficiency, so it can serve as a new and effective solution for the manufacturing of high-performance truss rods in the aerospace field.

The inverse heat placement problem in metal additive manufacturing: Why a rational approach is needed for powder bed fusion

Powder bed fusion (PBF) uses an energy beam to scan a powder bed surface, heat it locally, and consolidate the material to form a part. The choice of energy beam paths is typically based on user experience. Simple beam path strategies (e.g., raster or spiral) are used and the thermal field analysed post hoc. We refer to this approach as solving the direct problem, which involves many modelling and experimental iterations until a close to satisfactory, but not optimal, thermal field is achieved. In contrast, we propose a rational approach, solving an inverse problem to control heat placement, which we illustrate using laser beam PBF. Starting from the desired thermal field to be induced in the powder bed, we show how to formulate and solve the Inverse Heat Placement Problem (IHPP) so that a set of time-varying beam parameters (path, scan speed, energy delivered) is obtained in a single iteration. Solving the IHPP takes the craftsmanship out of beam path planning and puts it on a sound scientific and mathematical basis. This will enable a step change in the quality and complexity of customised macro-, meso-, and microscale properties of PBF built parts.

Design and characteristics of contactless small-magnetic-metal detector base on tunnel magnetoresistance sensors in differential circuit

Unexpected metal pieces are significant problems for food and pharmaceutical manufacturers. Commonly, conventional coil metal detectors are used to detect these metal pieces. However, these detectors can only detect cm-size metal pieces, making it challenging to detect mm-size metal pieces. This paper presents an advanced contactless small-magnetic-metal detector based on tunnel magnetoresistance sensors (TMR). To improve the system's sensitivity, the differential circuit combined with a simple AC-bias lock-in amplifier was designed to reduce the noise of TMR sensors. The sensitivity of this designed detection system is 0.44V/mm with a good linearity of R2=0.82674. The experimental results demonstrate that the fabricated prototype can detect the 100 – μm diameter and 1-mm length metal wire. Due to high sensitivity and low-cost fabrication, this system is promising to apply on the manufacturing lines for detecting unexpected tiny metal pieces.