Search

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts. [ABSTRACT FROM AUTHOR]

Quality analysis of additively manufactured (AM) surfaces is complex, yet critical for determining the functionality of parts and technology improvement. To accurately assess the quality of AM parts, it is necessary to consider the industrial application of the technology. This study investigates the variation of accumulated particles on AM top surfaces as a function of build position. It also seeks to study the surface texture variation as a function of build position, focusing on a spatial bandwidth region larger than that of traditional AM surface features, such as weld tracks, to investigate surface tension effects. Ti-6Al-4V cubes were built in a three-by-three array in a single build with fixed processing parameters. Coherence scanning interferometry was used to capture the primary data of the as-built top surfaces of cubes. The ISO 25178-2 methodology was used to extract the S-L surfaces, using the filtration methods defined in ISO 16610-21. The number of particles, coverage, and density were obtained by averaging over five repeated measurements in five different areas on the top surfaces. Particle distribution and surface texture analysis showed a trend from one location to another across the build, which is discussed according to the process variations. [ABSTRACT FROM AUTHOR]

Batch production is a modern strategy for manufacturing, especially for products unsuitable for continuous assembly lines. This study proposes a method for analysing one such production facility. Here, we characterize a batch production system as a unique sort of quorum feedback retrial queue deployed under Bernoulli working vacation and outfitted with a secondary, stand-by server. The system’s dynamics are thoroughly examined using the supplementary variable technique, providing insights into its behaviour and performance. Key metrics are determined, and the impact of specific parameters is demonstrated through numerical examples. Furthermore, this research aims to achieve the optimal system cost by employing a variety of optimization approaches. Thus, optimizing costs and identifying key metrics are crucial for enhancing efficiency and profitability in batch production systems. This process ensures that resources are used effectively, service quality is maintained, and overall operational costs are reduced, leading to a more sustainable and profitable production system which helps to make informed decisions.

Single-walled carbon nanotubes (SWNTs) have been regarded as one of the most promising candidates to supplement or even replace silicon in the post-Moore era. The requirement is to prepare the horizontally aligned SWNTs arrays (HASAs) with multiple indicators, including high density, high semiconducting purity, and wafer-scale uniformity. However, after all the fevered works being done in controlled synthesis, we still have a long way to go before realizing the application of SWNTs in highly performed electronic devices. The methods of batch production and high-throughput characterization techniques of the HASAs are the two main challenges. In this outlook, we first summarized the progresses in synthesis of HASAs with either high density or high semiconducting purity. Then the methods adopted in characterizing SWNTs and HASAs were discussed according to the different principles of characterization techniques. Afterwards, the development of carbon nanotube based electronic devices, specifically, the field effect transistors (FETs), was reviewed from three perspectives. The problems involved in electronic applications bring forward the higher request to the HASAs itself. Therefore, in the end of this outlook, we prospected the future of the synthesis and corresponding characterization of HASAs, and tried to provide our ideas about how to pave the way to the batch production of HASAs for carbon based electronic devices. [ABSTRACT FROM AUTHOR]

Monolayer MoS2 has emerged as a highly promising candidate for next-generation electronics. However, the production of monolayer MoS2 with a high yield and low cost remains a challenge that impedes its practical application. Here, a significant breakthrough in the batch production of wafer-scale monolayer MoS2 via chemical vapor deposition is reported. Notably, a single preparation process enables the growth of multiple wafers simultaneously. The homogeneity and cleanliness of the entire wafer, as well as the consistency of different wafers within a batch, are demonstrated via morphology characterizations and spectroscopic measurements. Field-effect transistors fabricated using the grown MoS2 exhibit excellent electrical performances, confirming the high quality of the films obtained via this novel batch production method. Additionally, we successfully demonstrate the batch production of wafer-scale oxygen-doped MoS2 films via in situ oxygen doping. This work establishes a pathway towards mass preparation of two-dimensional materials and accelerates their development for diverse applications. [ABSTRACT FROM AUTHOR]

[Display omitted] Two-dimensional (2D) transition metal dichalcogenides (TMDs) are regarded as pivotal semiconductor candidates for next-generation devices due to their atomic-scale thickness, high carrier mobility and ultrafast charge transfer. In analog to the traditional semiconductor industry, batch production of wafer-scale TMDs is the prerequisite to proceeding with their integrated circuits evolution. However, the production capacity of TMD wafers is typically constrained to a single and small piece per batch (mainly ranging from 2 to 4 inches), due to the stringent conditions required for effective mass transport of multiple precursors during growth. Here we developed a modularized growth strategy for batch production of wafer-scale TMDs, enabling the fabrication of 2-inch wafers (15 pieces per batch) up to a record-large size 12-inch wafers (3 pieces per batch). Each module, comprising a self-sufficient local precursor supply unit for robust individual TMD wafer growth, is vertically stacked with others to form an integrated array and thus a batch growth. Comprehensive characterization techniques, including optical spectroscopy, electron microscopy, and transport measurements unambiguously illustrate the high-crystallinity and the large-area uniformity of as-prepared monolayer films. Furthermore, these modularized units demonstrate versatility by enabling the conversion of as-produced wafer-scale MoS 2 into various structures, such as Janus structures of MoSSe, alloy compounds of MoS 2(1− x) Se 2 x , and in-plane heterostructures of MoS 2 -MoSe 2. This methodology showcases high-quality and high-yield wafer output and potentially enables the seamless transition from lab-scale to industrial-scale 2D semiconductor complementary to silicon technology. [ABSTRACT FROM AUTHOR]

It is profitable to export fresh meat overseas, where it is often regarded as a premium commodity. Meeting this demand for fresh meat, however, necessitates long export times, during which uncontrolled temperature increases can affect the microbiological quality of the meat and thereby, reduce shelf life or compromise food safety. To study the impact of temperature deviations on microbial community composition and diversity, we used 16S rRNA gene sequencing for Listeria monocytogenes and Salmonella spp. detection to describe the surface microbiota of eight batches of vacuum-packed loins stored at −1.5 °C (control) for 56 days and subjected to a 2 °C or 10 °C temperature deviation for a few hours (mimicking problems regularly encountered in the industry) at day 15 or 29. The presence of pathogens was negligible. The applied temperature deviations were not associated with different microbiota. Sequencing analysis showed the presence of Yersinia, an unexpected pathogen, and relative abundance increased in the groups subjected to temperature deviations. Over time, Lactobacillales_unclassified genus became the main constituent of the microbiota of vacuum-packed pork loins. Although the microbiota of the eight batches appeared similar at the beginning of storage, differences were revealed after 56 days, suggesting unequal aging of the microbiota. [ABSTRACT FROM AUTHOR]

In powder bed fusion laser beam (PBF-LB), powder reusability remains key to keeping cost-effectivity as well as sustainability. In this study, highly sensitive Ti6Al4V ELI powder typically used for medical and aerospace applications is studied. Powder properties of new and reused powders after 10 build cycles subjected to variations such as morphology, particle size distribution (PSD), chemical composition and flowability were analysed. The flow rate using Carney flowmeter increased from 6.8 s to 12 s. Oxygen content slightly increased from 0.11% to 0.12%. The dimensional deviations are measured in six builds of eight samples spread through the build plate. The density of the cubes does not show relevant differences in density (from 99.6% to 99.9%), only the last batch exhibits slightly lower density than the previous builds. Studied properties for the powder and builds are maintained throughout the experiment, demonstrating repeatability of industrial production of metal parts. [ABSTRACT FROM AUTHOR]

Downsized and complex micro-machining structures have to meet quality requirements concerning geometry and convince through increasing functionality. The development and use of cutting tools in the sub-millimeter range can meet these demands and contribute to the production of intelligent components in biomedical technology, optics or electronics. This article addresses the development of double-edged micro-cutters, which consist of a two-part system of cutter head and shaft. The cutting diameters are between 50 and 200 μm. The silicon carbide cutting heads are manufactured from the solid material using microsystem technology. The substrate used can be structured uniformly via photolithography, which means that 5200 homogeneous micro-milling heads can be produced simultaneously. This novel batch approach represents a contrast to conventionally manufactured micro-milling cutters. The imprint is taken by means of reactive ion etching using a mask made of electroplated nickel. Within this dry etching process, characteristic values such as the etch rate and flank angle of the structures are critical and will be compared in a parameter analysis. At optimal parameters, an anisotropy factor of 0.8 and an etching rate of 0.34 µm/min of the silicon carbide are generated. Finally, the milling heads are diced and joined. In the final machining tests, the functionality is investigated and any signs of wear are evaluated. A tool life of 1500 mm in various materials could be achieved. This and the milling quality achieved are in the range of conventional micro-milling cutters, which gives a positive outlook for further development. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Scheduling of batch production and machine disruption are the two main challenges in manufacturing organizations. Due to the complexity of production processes, many industries try to group jobs according to family criteria and use a common setup time to process every family. Also, machine breakdown is an influential factor in the planning of production systems. In this paper, the problem of scheduling a single machine with family setup times and breakdowns is studied. It is assumed that there is a breakdown with an uncertain start time and duration based on the specified probability distribution functions during the planning horizon. The objective function of this problem is the sum of the expected maximum earliness and maximum tardiness. Design/methodology/approach: For the problem under study, a new mixed integer linear programming model has been developed. Due to the NP-hardness of the problem, a new branch and bound algorithm with the dominance rules and an efficient lower bound is presented for its optimal solving, which uses a new heuristic approach to achieve the upper bound. Findings: To evaluate the performance of the introduced algorithms, 2520 instances were designed and solved with the presented algorithms. The computational results indicated that 98% of the instances were optimally solved in the specified time limitation by the branch and bound algorithm, and the average percentage of deviation from the optimal solution in the proposed heuristic approach was less than 30%. The results demonstrated the efficiency of the proposed algorithms. Research limitations/implications: Considering the newness of the problem investigated in this paper, the proposed instances and algorithms can be used as a basis for evaluating other solution methodologies in future research studies. Also, considering other modes of machine failures such as scenario-based failures or re-scheduling the jobs to minimize the deviations of the actual schedule from the planned program, situations with more than one machine such as parallel machines, flow shops, and job shops, other objective functions related to scheduling such as maximum completion time or total completion time, as well as the development of other exact, heuristic or meta-heuristic algorithms are suggested as subjects for future study. Practical implications: The problem studied in this paper can be attractive and practical for manufacturing organizations. Industries such as automotive, ship and aircraft manufacturing, steel, telecommunication power supply manufacturing, electronic, computer processors, and all industries and systems that somehow deal with the batch production process and unexpected machine breakdowns, can benefit from the results of this research. Social implications: Because in this study, the starting and finishing times of machine breakdowns were predicted, by applying the results of this research, the production of defective products will be prevented when the machine breaks down, and this leads to the reduction of waste in the environment. Also, according to the objective function defined in the problem investigated in this article, the implication of the results of this research in production environments leads to the reduction of earliness and tardiness costs, which in turn increases the work efficiency of human resources and as a result, increases the job satisfaction. Originality/value: It seems no study has been conducted on the single machine scheduling problem with batch production, random breakdown, and the objective function of minimizing the sum of the expected maximum earliness and maximum tardiness of the jobs. Particularly, innovations of this paper are threefold: i) a new mixed integer linear programming model was developed for the problem; ii) a novel heuristic approach was proposed to solve the problem, based on hill climbing (PHC); and iii) a new branch and bound algorithm with the dominance rules and an efficient lower bound was presented to solve the problem optimally, which used the PHC heuristic approach to achieve the upper bound.

As a new technology, 3D printing technology, which integrates digital software and processing equipment, can achieve customized, printable and batch production of object constructions. In recent years, the 3D printing technology, combined with modern food industry, could provide nutritional and personalized food in time-efficient, personalized and sustainable way. Therefore, this article introduces the classification, platform design, implement scheme, available printing material and assistant technology in 3D printing technology, current application of 3D printing in vegetables and fruits, desserts, powder food, traditional meat and aquatic products, cultured meat products and other special food, and explore the development prospects of future 3D printing technology. It is expected to provide more strategies for research and development of food 3D/4D/XD printing technology.

Batch production is a modern strategy for manufacturing, especially for products unsuitable for continuous assembly lines. This study proposes a method for analysing one such production facility. Here, we characterize a batch production system as a unique sort of quorum feedback retrial queue deployed under Bernoulli working vacation and outfitted with a secondary, stand-by server. The system's dynamics are thoroughly examined using the supplementary variable technique, providing insights into its behaviour and performance. Key metrics are determined, and the impact of specific parameters is demonstrated through numerical examples. Furthermore, this research aims to achieve the optimal system cost by employing a variety of optimization approaches. Thus, optimizing costs and identifying key metrics are crucial for enhancing efficiency and profitability in batch production systems. This process ensures that resources are used effectively, service quality is maintained, and overall operational costs are reduced, leading to a more sustainable and profitable production system which helps to make informed decisions. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time. [ABSTRACT FROM AUTHOR]

This brief presents a complete design flow for a compact low-loss high-selectivity excellent-isolation LTE Band3 (B3) duplexer (TX: 1710 MHz-1785 MHz, RX: 1805 MHz-1880 MHz). The proposed chip duplexer is based on the thin film bulk acoustic resonator (FBAR). In addition to the general design method, the effects of temperature drift, resonator performance distribution on wafer, the quality factor of inductance, device yield, and dissipated power density distribution on the design flow are described. These aspects are of great significance for the batch production of FBAR duplexers in the industry. Samples that pass the above industrial process are selected for testing. Measured results show reasonable consistency with the co-simulation results. 1.2/1.0-dB minimum IL in TX /RX band, 20-dB rejection in 3.5 GHz (second harmonic of TX), and more than 50 dB isolation in both TX and RX frequency are achieved with less massload simultaneously. [ABSTRACT FROM AUTHOR]

Monolayer MoS2 has emerged as a highly promising candidate for next-generation electronics. However, the production of monolayer MoS2 with a high yield and low cost remains a challenge that impedes its practical application. Here, a significant breakthrough in the batch production of wafer-scale monolayer MoS2 via chemical vapor deposition is reported. Notably, a single preparation process enables the growth of multiple wafers simultaneously. The homogeneity and cleanliness of the entire wafer, as well as the consistency of different wafers within a batch, are demonstrated via morphology characterizations and spectroscopic measurements. Field-effect transistors fabricated using the grown MoS2 exhibit excellent electrical performances, confirming the high quality of the films obtained via this novel batch production method. Additionally, we successfully demonstrate the batch production of wafer-scale oxygen-doped MoS2 films via in situ oxygen doping. This work establishes a pathway towards mass preparation of two-dimensional materials and accelerates their development for diverse applications.

Background: In a batch production series consisting of several process stages for discrete products, the determination of the transfer batch size greatly affects the batch waiting time. For production using a Computer Numerical Control (CNC)-machining center, several process stages can be carried out on one machine with quick setup. The use of a single machine for all stages of the process certainly does not require transfer to the next machine. Thus, there are no material handling costs. However, the use of a single machine will result in longer batch waiting times as the production batch size increases. An increase in the batch waiting time will result in an increase in work-in-process (WIP) inventory cost. This research was conducted to reduce the total variable cost consisting of WIP inventory cost and material handling cost. Methods: This research was conducted in two steps. The first step is to divide the process stages, I into m serial CNC machining centers, where in this study I= m. The second step is to determine the same size of transfer batch, Q ij for a certain number of transfers, J that produces the minimum total variable cost. Results: The results showed that J=5 resulted in a total variable cost of Rp. 146,800.00, while J=10 resulted in a total variable cost of Rp. 178,900.00. Conclusion: In conclusion, the total variable cost decreases if the size of transfer batch is reduced to a certain amount and will increase again along with the reduction in the size of transfer batch.

It is profitable to export fresh meat overseas, where it is often regarded as a premium commodity. Meeting this demand for fresh meat, however, necessitates long export times, during which uncontrolled temperature increases can affect the microbiological quality of the meat and thereby, reduce shelf life or compromise food safety. To study the impact of temperature deviations on microbial community composition and diversity, we used 16S rRNA gene sequencing for Listeria monocytogenes and Salmonella spp. detection to describe the surface microbiota of eight batches of vacuum-packed loins stored at −1.5 °C (control) for 56 days and subjected to a 2 °C or 10 °C temperature deviation for a few hours (mimicking problems regularly encountered in the industry) at day 15 or 29. The presence of pathogens was negligible. The applied temperature deviations were not associated with different microbiota. Sequencing analysis showed the presence of Yersinia, an unexpected pathogen, and relative abundance increased in the groups subjected to temperature deviations. Over time, Lactobacillales_unclassified genus became the main constituent of the microbiota of vacuum-packed pork loins. Although the microbiota of the eight batches appeared similar at the beginning of storage, differences were revealed after 56 days, suggesting unequal aging of the microbiota.

This article deals with energy saving in the hybrid flow shop scheduling problem with batch production at last stage, which has important application in energy‐intensive steelmaking‐continuous casting (SCC) process. We first establish a mixed integer programming model to reduce extra energy consumption, and then adopt genetic algorithm to solving the scheduling problem. Based on traditional genetic algorithm (TGA), the calculation of the fitness function as well as adaptive crossover and mutation are designed. Due to the complexity of the problem in this article, we then propose an efficient adaptive genetic algorithm (EAGA) to improve the search ability of TGA. The EAGA has new features including layered strategies and enhanced adaptive adjustment method. To evaluate the proposed model and algorithm, we conduct computational experiments under practical background and compare the EAGA with the several algorithms presented previously. The results illustrate that scheduling with our model can greatly reduce the extra energy consumption. Meanwhile, the proposed EAGA is very efficient in comparison. [ABSTRACT FROM AUTHOR]

In statistical process monitoring, statistical control tools are used to identify deviations from normal operating conditions. In many industrial processes, such as batch production processes, multiple process variables must be monitored as they play a key role in the quality of the final product. Several monitoring tools are found in the literature that deal with the case of multiple process variables, but a few of them deal with the case of autocorrelated data. Existing tools that are used to monitor autocorrelated process variables have two main drawbacks. First, they are run offline. That is, monitoring is performed at the end of the production cycle and hence no corrective actions can be made. Second, these tools utilize dimensionality reduction techniques to solve for computational complexity issues. A Gaussian Process–based modeling approach is proposed in this work to monitor batch production processes online. The proposed monitoring approach takes into consideration of both the correlation between process variables and the within variable autocorrelations. A simulated and two real data sets were used to evaluate the performance of the proposed modeling approach. Model performance metrics showed that the proposed modeling approach has similar performance to the optimal case of Shewhart type control charts. Process status classification accuracy of about 92–98% were achieved for the cases considered. [ABSTRACT FROM AUTHOR]

Identification of quality issues in batch production is relevant for stable and successful production. The identification of systematic quality issues and the identification of root causes is complex and challenging. Powerful multivariate statistical methods exist to perform batch analysis, yet their application is difficult due to data diversity and complexity in model training. Here, insights from data analysis projects on four batch plants are presented and real world challenges are reviewed. This leads to a semi‐automatic process for more efficient batch analysis overcoming the mentioned tedious tasks. [ABSTRACT FROM AUTHOR]

The objectives were to assess the microbiological stability and the physical enclosure integrity of the overwrapping for batch production of standard cytotoxic injectable solutions.

In the context of batch production of cytotoxic drugs in hospital pharmacies with the need of sterility testing, the objective was to validate the use of Rapid Microbiological Method (RMM), and to develop adequate neutralization method in case of inhibition of bacterial growth. The potential microbiological growth inhibitory effect of three anticancer drugs (5 fluorouracil, irinotecan and oxaliplatin) selected for batch production was assessed on BacT/ALERT® system. Among cytotoxic drugs, only 5FU exhibited inhibitory effect on microbiological growth using rapid microbiological method. To counteract this effect our purpose was to use neutralizing agents complexing the drug i. e. activated carbon or ion exchange resins. The microbiological bactericidal concentration of 5FU was very low (1.10–4 mg/mL) indicating the absolute need to neutralize the whole drug before sterility test. The complexation was validated by High Performance Liquid Chromatography control of the residual 5FU concentration in solution after the use of neutralizing agents. Only activated carbon was able to fully capture 5FU when previously diluted at 5 mg/mL. Conversely, the resins, in the condition of the study, were not able to fully capture 5FU whatever the dilution. The microbiological growth on BacT/ALERT® system after active carbon treatment was successfully confirmed with Staphylococcus aureus. Based on this validation results a method was then developed to routinely be able to perform sterility test of the batches produced and was confirmed on five microbiological species (i. e. S. aureus, Pseudomonas aeruginosa, Bacillus subtilis, Candida albicans, Aspergillus brasiliensis). Our work gives a new insight for considering sterility testing by rapid microbiological method even for drugs exhibiting inhibitory effect on microbiological growth.

Rapid vacuum casting has been proven to be a successful method in producing high-quality parts in small series. However, a challenge lies in the selection of proper Additive Manufacturing (AM) technologies for the development of a master pattern for the vacuum casting process. Each AM technologies differ from one another in terms of dimensional accuracy, surface finish, cost and lead times. The aim of this study is to investigate the performance of casting mating parts based on different additive manufacturing patterns for small batch. Three types of AM-based patterns: Fused Deposition Modeling (FDM), Stereolithography (SLA) and Multi-Jet Fusion (MJF) were compared. The Taguchi method, Signal to Noise ratio (S/N), Analysis of Variance (ANOVA) and T-test were conducted in determining the optimized parameters. From the findings, curing time is shown to be a significant parameter for dimensional accuracy and surface finish. Dimensional deviation varies in different directions of parts. For surface finish, there was only a slight change from the master pattern whereas the surface roughness of casting parts decreased within the range 0.23% to 2.85%. Tolerance grades for the selected dimensions of the parts were in the permissible range, based on ISO286-1:2010. When using distinct kinds of AM patterns to create replacement components, design tolerance is needed. It was suggested to select AM technology similar to that had been used for the original components. Battery cover was selected as a case study to represent the mating application parts.

An approach is proposed for modeling and scheduling batch production systems based on Petri nets. First, a group of jobs to be carried out by a batch production system is modeled by a transition-timed Petri net according to logical relations between operations and time requirements on operations. Second, conflict relations between operations are obtained by operation-resource diagrams that describe how operations compete for resources, and are formally expressed by linear constraints. Third, monitor places are designed to enforce linear constraints on the transition-timed Petri net, and a plant net is consequently built and made free of conflicts between operations, which can well emulate a real batch production system. Fourth, an optimal schedule problem, where the optimization performance index is to minimize a makespan, is formalized based on a plant net. Finally, a new variant of filtered beam search method is proposed to solve the problem based on an ad hoc function and a dynamical timed extended reachability graph of a plant net. A typical chemical production plant illustrates the theoretic results.

Downsized and complex micro-machining structures have to meet quality requirements concerning geometry and convince through increasing functionality. The development and use of cutting tools in the sub-millimeter range can meet these demands and contribute to the production of intelligent components in biomedical technology, optics or electronics. This article addresses the development of double-edged micro-cutters, which consist of a two-part system of cutter head and shaft. The cutting diameters are between 50 and 200 μm. The silicon carbide cutting heads are manufactured from the solid material using microsystem technology. The substrate used can be structured uniformly via photolithography, which means that 5200 homogeneous micro-milling heads can be produced simultaneously. This novel batch approach represents a contrast to conventionally manufactured micro-milling cutters. The imprint is taken by means of reactive ion etching using a mask made of electroplated nickel. Within this dry etching process, characteristic values such as the etch rate and flank angle of the structures are critical and will be compared in a parameter analysis. At optimal parameters, an anisotropy factor of 0.8 and an etching rate of 0.34 µm/min of the silicon carbide are generated. Finally, the milling heads are diced and joined. In the final machining tests, the functionality is investigated and any signs of wear are evaluated. A tool life of 1500 mm in various materials could be achieved. This and the milling quality achieved are in the range of conventional micro-milling cutters, which gives a positive outlook for further development.

2D materials with atomically thin nature are promising to develop scaled transistors and enable the extreme miniaturization of electronic components. However, batch manufacturing of top-gate 2D transistors remains a challenge since gate dielectrics or gate electrodes transferred from 2D material easily peel away as gate pitch decreases to the nanometer scale during lift-off processes. In this study, an oxidation-assisted etching technique is developed for batch manufacturing of nanopatterned high-κ/metal gate (HKMG) stacks on 2D materials. This strategy produces nano-pitch self-oxidized Al 2 O 3 /Al patterns with a resolution of 150 nm on 2D channel material, including graphene, MoS 2 , and WS 2 without introducing any additional damage. Through a gate-first technology in which the Al 2 O 3 /Al gate stacks are used as a mask for the formation of source and drain, a short-channel HKMG MoS 2 transistor with a nearly ideal subthreshold swing (SS) of 61 mV dec -1 , and HKMG graphene transistor with a cut-off frequency of 150 GHz are achieved. Moreover, both graphene and MoS 2 HKMG transistor arrays exhibit high uniformity. The study may bring the potential for the massive production of large-scale integrated circuits using 2D materials., (© 2024 Wiley‐VCH GmbH.)

Two-photon polymerization (2PP) is becoming increasingly established as additive manufacturing technology for microfabrication due to its high-resolution and the feasibility of generating complex parts. Until now, the high resolution of 2PP is also its bottleneck, as it limited throughput and therefore restricted the application to the production of microparts. Thus, mechanical properties of 2PP materials can only be characterized using nonstandardized specialized microtesting methods. Due to recent advances in 2PP technology, it is now possible to produce parts in the size of several millimeters to even centimeters, finally permitting the fabrication of macrosized testing specimens. Besides suitable hardware systems, 2PP materials exhibiting favorable mechanical properties that allow printing of up-scaled parts are strongly demanded. In this work, the up-scalability of three different photopolymers is investigated using a high-throughput 2PP system and low numerical aperture optics. Testing specimens in the cm-range are produced and tested with common or even standardized material testing methods available in conventionally equipped polymer testing labs. Examples of the characterization of mechanical, thermo-mechanical, and fracture properties of 2PP processed materials are shown. Additionally, aspects such as postprocessing and aging are investigated. This lays a foundation for future expansion of the 2PP technology to broader industrial application., (© 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH.)

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time.

With the development of the product-service paradigm, many original equipment manufacturers (OEMs) have begun to provide remanufacturing services besides maintenance services as the lessors. An integrated contract including remanufacturing and maintenance service brings extra leasing profits and complex scheduling challenges as well. The use of machine remanufacturing in conjunction with system-level opportunistic maintenance (OM) has not been previously studied. This paper tries to integrate remanufacturing planning into maintenance optimizations for improved system performance in terms of saved leasing profits. An integrated remanufacturing and opportunistic maintenance (R&OM) policy is developed and applied to a leased manufacturing line in batch production. The system-level saved profit achieved via the R&OM policy is estimated and maximized through a sequential decision-making programming. Performing this integrated policy, it is possible to utilize the sequential R&OM opportunities raised from the random batch orders. In addition, preventive maintenance (PM) and preventive remanufacturing (PR) can be dynamically optimized to form profit-effective schemes. Numerical examples based on the collected information from a leased batch production line of engine crankshaft demonstrate that this proposed R&OM policy could efficiently achieve saved leasing profit maximization, reduce joint decision complexity, and expand OM theory. [ABSTRACT FROM AUTHOR]

Although the Theory of Constraints (TOC) is a widely known management concept, it warrants further research, particularly in terms of its application. The study investigates TOC implementation in a medium-scale manufacturing system with diverse products. The instrumental case study methodology is employed in this study, and the conceptual studies assess the case. The study helps to elucidate the methodology of TOC implementation in a manufacturing system. Instead of the five-step sequential approach that exploits constraints one after another, the cycles of system productivity target points are proposed, where each improvement cycle simultaneously considers several constraints. The proposed methodology is visualised, several implications and suggestions for constraint identification are provided, and a new method of bottleneck identification is elaborated in this study.

Aiming at the problem of order selections and multi-component equipment maintenance decisions under limited capacity when facing multiple orders with different requirements, the paper proposed a joint optimization model based on the customer satisfaction. Firstly, the method used the proportional risk model to describe the specific deterioration trend of each component and the virtual age to represent the health status of each component. Next, the paper determined the different maintenance methods based on the real-time monitoring results and maintenance threshold. Then, with profit maximization as the optimization goal, combined with limited capacity batch problem model and customer satisfaction model, the paper established the maintenance strategy based on the situation and the multi-order batch production joint optimization model. Finally, the paper used the simulated annealing algorithm and particle swarm optimization algorithm to solve the model, an example verifies the validity of the model in order to let the model have more practical significance, and carried out the sensitivity analysis of the corresponding parameters of the model. [ABSTRACT FROM AUTHOR]

This paper presents modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined (proposed) genetic algorithm; then the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: (1) The suggested algorithm is valid and able to achieve optimal and near-optimal solutions. This conclusion was made after proving the validity of the proposed method by solving a case study by employing the classical method (employing Lingo 11). And when the results were compared with the ones obtained by the proposed algorithm, they were found to be the same in both cases. (2) The combined genetic algorithm is more effective in obtaining optimal boundaries and the solutions close to them in all cases compared to the classical (simple) genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature. [ABSTRACT FROM AUTHOR]

We consider a batch production process that can be either stable or unstable, in which inspection is performed offline after production of the batch is completed. The quality of a batch can be estimated with a desired level of certainty by inspecting only a sample of its units. In order to minimise the expected total cost per batch, which includes the costs of inspection, of false acceptance and of false rejection, we propose an economic inspection plan in which only a fraction of the batches, rather than each batch, is inspected. We prove that the expected total cost is a strictly quasiconvex function of the inspection interval. We establish necessary and sufficient conditions for the optimal inspection interval to be finite and propose an efficient algorithm to obtain its value. We demonstrate for the case of a single-sampling plan where the proposed economic approach outperforms the common procedure of inspecting every batch, and the proposed algorithm is very efficient. [ABSTRACT FROM PUBLISHER]

This paper discusses an integrated model of batch production and maintenance scheduling on flow shop with two deteriorating machines producing single item to be delivered at a due date. The model describes the trade-off between production and maintenance costs as the production run length increases on two machines. The objective function of the model is to minimize the total cost consisting of in process and completed part inventory costs, setup costs, preventive & corrective maintenance costs and rework costs on two machines. The step-wise optimization algorithm is developed to solve a mixed integer quadratic programming. Comparison with the practice and the model sensitivity analysis are demonstrated to clarify how the algorithm works.

On the consideration of the development trend of industrial robots and domestic market situation& research status of RV reducer,the difficult problems need to be solved in the domestic batch manufacturing of RV reducer are put forward,such as national standards for design,manufacture and inspection,machining equipment for special part,wear and lubrication and intelligent inspection and assembly. The relevant solutions are discussed and the strategic thinking and suggestion are provided for RV reducer technology and industry of robot field in China.

Surface-enhanced Raman scattering (SERS) have numerous applications in areas such as analytical chemistry, biochemistry, and environmental science. However to manufacture SERS active substrates with good reproducibility and low cost is not easy, which hinder the SERS technology from being widespread in various applications. In this study, we developed a batch producible hot embossing 3D nanostructured SERS substrate technology for SERS applications. This study utilized the anodic aluminum oxide (AAO) self-assembled uniform nano-hemispherical array barrier layer as a template to create a durable nanostructured nickel mold. With the hot embossing technique and the durable nanostructured nickel mold, we were able to batch produce the 3D Nanostructured SERS chip with consistent quality and low cost. In addition, according to the SERS experiments, the 3D nano-hemispherical cavity array combined with sub-10-nm-gaps Au NPs showed distinct SERS signals in both Rhodamine 6G and Chlorpyrifos measurements. Therefore, the developed method is good to be used extensively in rapid chemical and biomolecular detection applications.

Most of textile companies in the world are operating in a fluctuating and uncertain environment. Despite laying the first brick of TPS (Toyota Production System), textile industry still lacks necessary leanness in its internal and external processes to buffer against the intensifying competition. In order to dust off this traditional and neglected industry, innovative smart techniques and methodologies from academia and practitioners are needed. In this research paper, first textile industry and its compatibility with Lean Manufacturing (LM) and System of Systems (SoS) approach has been explored. Then the detailed current value stream mapping (VSM) was drawn to map and highlight the weaknesses of processes during the case study accomplished at a spinning mill located in Pakistan. In order to address the unveiled issues, a Smart Lean Spinning Framework was envisaged integrating SoS ideas with the prudently screened and selected LM tools, apt to textile industry. Subsequently, the proposed framework was simulated to evaluate the impacts of diverse parameters. The impressive results obtained by modeling and simulation encouraged us to put our prospective model to test. The outcome after implementation has been depicted in Smart Lean VSM and later transfigured into appropriate diagrams that show significant improvements. In this way, the research work also implicitly adapts and adopts lean tools of discrete manufacturing to process industry. [ABSTRACT FROM AUTHOR]

Problem definition: We analyze a catalyst-activated batch-production process with uncertainty in production times, learning about catalyst-productivity characteristics and decay of catalyst performance across batches. The goal is to determine the quality level of batches and to decide when to replenish a catalyst so as to minimize average costs, consisting of inventory-holding, backlogging, and catalyst-switching costs. Academic/practical relevance: This is an important problem in a variety of process-industry sectors, such as food processing, pharmaceuticals, and specialty chemicals, but has not been adequately studied in the academic literature. This paper also contributes to the stochastic economic lot-sizing literature. Methodology: We formulate this problem as a semi-Markov decision process (SMDP) and develop a two-level heuristic to solve this problem. The heuristic consists of a lower-level problem that plans the duration of batches within the current campaign to maximize the efficiency of the catalyst while ensuring that the target attribute level for each batch is set to meet a quality specification represented by an average attribute level across all the batches in a campaign. The higher-level problem determines when to replace the costly catalyst as its productivity decays. To evaluate our heuristic, we present a lower bound on the optimal value of the SMDP. This bound accounts for all costs, as well as the randomness and discreteness in the process. We then extend our methods to multiple-product settings, which results in an advanced stochastic economic lot-sizing problem. Results: We test our proposed solution methodology with data from a leading food-processing company and show that our methods outperform current practice with an average improvement of around 22% in costs. In addition, compared with the stochastic lower bounds, our results show that the two-level heuristic attains near-optimal performance for the intractable multidimensional SMDP. Managerial implications: Our results generate three important managerial insights. First, our simulation-based lower bound provides a close approximation to the optimal cost of the SMDP, and it is nearly attainable by using a relatively simple two-level heuristic. Second, the reoptimization policy used in the lower-level problem adequately captures the value of information and Bayesian learning. Third, in the higher-level problem of choosing when to replace a catalyst, the intractable multidimensional state of the system is efficiently summarized by a single statistic: the probability of inventory falling below a specific threshold. [ABSTRACT FROM AUTHOR]

To survive the fierce market competition, many manufacturing enterprises have applied integration measures at all levels of the production process. Against this backdrop, this paper mainly establishes a job-shop scheduling problem (JSP) that aims to minimize the makespan of products through integrated scheduling of machining and assembly under batch production environment. Then, the established problem was modelled considering the influence of different batch number on the makespan. In view of the complexity and discreteness of the established problem, a genetic algorithm (GA) was designed to obtain the optimal scheduling sequence in different batch production situations. The effectiveness of our problem model and algorithm was verified through the analysis on an example with different batch conditions. The research findings help to design a realistic and feasible scheduling plan for manufacturing enterprises. (Received, processed and accepted by the Chinese Representative Office.) [ABSTRACT FROM AUTHOR]

Although the Theory of Constraints (TOC) is a widely known management concept, it warrants further research, particularly in terms of its application. The study investigates TOC implementation in a medium-scale manufacturing system with diverse products. The instrumental case study methodology is employed in this study, and the conceptual studies assess the case. The study helps to elucidate the methodology of TOC implementation in a manufacturing system. Instead of the five-step sequential approach that exploits constraints one after another, the cycles of system productivity target points are proposed, where each improvement cycle simultaneously considers several constraints. The proposed methodology is visualised, several implications and suggestions for constraint identification are provided, and a new method of bottleneck identification is elaborated in this study. [ABSTRACT FROM AUTHOR]

The lot-sizing problem in batch manufacturing systems with capacity constraints is studied using queueing relationships and optimisation techniques. In this research, the effect of lot sizes when there are parallel machines and multiple part types is considered. Furthermore, the issue of whether or not to use pooled queues, based on part types, to feed the machines is examined. Different scenarios are evaluated using GI/G/n queueing approximations to predict performance and optimise lot sizes. Optimisation is based on minimising the mean flowtimes, which include queue and lot service times. The results show that if part types are very different, there are situations in which pooling is not advantageous. [ABSTRACT FROM AUTHOR]