This study aims to investigate the effect of manufacturing process on the post-fire mechanical response of Grade 1200 ultra-high strength steel (UHSS) tubes. To this end, the post-fire mechanical ...properties of “direct-quenched” UHSS (UHSS-DQ) standard tensile coupons are compared to those made of “quenched and tempered” UHSS material (UHSS-QT) with similar original room temperature stress-strain responses. Thus, to compare the post-fire compression behaviour of UHSS-DQ tubular stub columns with those made of UHSS-QT material, a finite element (FE) model is developed in ABAQUS FE software with precise material properties extracted from the results of the post-fire tensile coupon tests. Quasi-static compression tests are then conducted on UHSS-QT tubular stub columns cooled from different fire temperatures to room temperature to validate the FE analysis. Using the results of the tensile coupon tests and the FE analysis on UHSS stub columns, it is shown that the manufacturing process substantially affects the mechanical properties of UHSS stub columns under cooling phase of a fire.
•The post-fire behaviour of Ultra-High Strength Steel (UHSS) tubes is investigated.•Effect of manufacturing process on post-fire behaviour of UHSS tubes is studied.•Direct Quenched (DQ) and Quenched and Tempered (QT) UHSS tubes are studied.•Post-fire tensile/compressive behaviour of UHSS-DQ and UHSS-QT tubes are compared.•Manufacturing process has a major effect on post-fire behaviour of UHSS tubes.
•At-line PAT is used to monitor and control of lamivudine-saccharinate salt synthesis.•MIR with MCR-ALS is applied as at-line PAT to follow the pharmaceutical salt formation.•Concentration profiles ...retrieved by MCR-ALS showed the end of the synthesis process.
This article presents a promising application of Fourier transform-mid infrared (FT-MIR) spectroscopy with multivariate curve resolution ― alternating least-squares (MCR-ALS) as an at-line process analytical technology (PAT) to enhancing the understanding and continuous control of a pharmaceutical manufacturing process. Its objective was to monitor the synthesis of pharmaceutical multicomponent crystals in solid-state, namely the mixture between lamivudine (active pharmaceutical ingredient-API) and saccharin (coformer) using liquid assisted grinding (LAG) in proportion of 1:1 in a ball mill. The continuous monitoring of synthesis procedure ensured product quality, revealing some of the events that can be detected during mechanochemical synthesis by FT-MIR spectroscopy with MCR-ALS. The concentration profiles retrieved by MCR-ALS allowed to identify the end of the salt synthesis. In fact, this is one of the advantages of real-time monitoring using FT-MIR spectroscopy and MCR-ALS, because it can be helpful not only to monitor and control a pharmaceutical manufacturing process, but also to optimize efficient use of energy, time and raw materials for lamivudine-saccharinate salt synthesis. Moreover, it allowed to understand that the antiretroviral lamivudine-saccharinate salt synthetized by LAG showed a fast reaction mechanism due to the presence of ethanol as catalyst. Differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD) techniques provided additional information needed to fully characterize pharmaceutical lamivudine-saccharinate salt synthetized by LAG technique.
Deep reinforcement learning (DRL) has been preliminarily applied to run-to-run (RtR) control. However, the existing works have mainly conducted on shift and drift disturbances in the chemical ...mechanical polishing (CMP) process and have not taken the non-stationary time-series disturbances into full consideration. Inspiring from the powerful self-learning mechanism of DRL, a new distributional reinforcement learning controller, quantile option structure deep deterministic policy gradient (QUOTA-DDPG), is designed to generate control policies without precise numerical model in this work. Specifically, the procedure for adjusting the recipe is formulated as a Markovian decision process. Meanwhile, state, action and reward are reasonably designed. Regarding QUOTA-DDPG, an option is first determined based on the option strategy, and the action is decided via intra-option policy at each time step. Moreover, target network and empirical replay mechanisms are utilized to enhance the stability and trainability. Simulations demonstrate that the presented approach outperforms the existing methods regarding the disturbance compensation and target tracking. The application of QUOTA-DDPG controller enriches the development of semiconductor smart manufacturing.
Bread is food that many consumed in Indonesia. It usually consumed and a substitute for rice. PT. X is an Indonesian company that produces dry bread and wet bread. The company must implement a Good ...Manufacturing Process (GMP) and Halal Guarantee System (HGS) to ensure the food safety that the company produced. The company is also obliged to pay attention to Occupational Safety and Health in the work environment by applying WISE. This study consists of five staged. The stages include identification of the condition of the company, CPPB inspection, WISE examination, HGS inspection, and proposed improvements in the production process. The results of the study show some elements that have not been fulfilled. These elements include 6 CPPB elements, 9 WISE elements, and 8 HGS elements. The results of the study also provided several studies. Some of the recipients were improvements in the Standart Operational Procedure (SOP) for the receipt and receipt of materials, and the SOP for the receipt and receipt of industrial non-processed materials.
Smart Virtual Product Development (SVPD) system provides effective use of information, knowledge, and experience in industry during the process of product development in Industry 4.0 scenario. This ...system comprises of three primary modules, each of which has been developed to cater to a need for digital knowledge capture for smart manufacturing in the areas of product design, production planning, and inspection planning. Manufacturing Capability Analysis and Process Planning (MCAPP) module is an important module of the SVPD system, and it involves the provision of manufacturing knowledge to experts working on product development at the early stages of the product lifecycle. In this research, we firstly describe the structure and working mechanism of the SVPD system's MCAPP module. This is followed by validation of the MCAPP module's Manufacturing Process Planning (MPP) sub-module against the key performance indicators (KPIs) by using our threading tap case study. Our results verify the feasibility of our approach and show how manufacturing knowledge relating to features and functions can be used to enhance the manufacturing process across similar products during the early stages of product development. An analysis of the basic concepts and methods of implementation show that this is an expert system capable of supporting smart manufacturing which can play a vital role in the establishment of Industry 4.0.
This paper deals with the experimental study of the iron losses under real operating conditions of a permanent magnet synchronous machine. The latter is a high frequency (>1 kHz) and high ...power-to-weight ratio (4 kW/kg) motor intended for an aerospace application. The measurements were carried out on different laminated stator cores based on classical commercial grades, namely, the NO20 and M270-35A for the silicon-iron alloy and the Vacodur49 (0.2 mm) for the cobalt-iron alloy. The lamination sheets stemmed from different manufacturing processes: insulation (bonding varnish and C5 varnish), cutting (laser and electrical discharge machining), and thermal treatment (fully processed only and fully processed + thermal re-treatment after cutting). We measured the iron losses at no load and over a wide range of frequency (speed) until around 1400 Hz, and then we compared them to the estimations yielded by the finite-element model under ANSYS Maxwell. Hence, this allowed us to accurately assess the iron loss add-on factor (<inline-formula> <tex-math notation="LaTeX">K_{\mathrm {add}} </tex-math></inline-formula>), which takes into account the extra magnetic loss caused by a complex magneto-thermo-mechanical coupling within the ferromagnetic material. This coupling occurs during the manufacturing and the assembly phase (cutting, welding, stacking, shrink fitting, ...) and also during the real running conditions of an electrical machine (elliptic field, local saturation, high frequency, and harmonics).
Faience beads have been excavated from the Gebusailu site in western Tibet and date back as far as the second half of the 2nd millennium BC. As the earliest faience found on the Tibetan Plateau so ...far, these beads provide important evidence for the cross-regional exchange in the pre-historic western Tibet. Randomly selected thirteen samples were analyzed by SR-μCT, EPMA and LA-ICP-MS. The CT slices suggested that the structure of the samples was formed on cylindrical core and glazed by cementation. The glaze recipes indicate the samples can be classified as soda-rich faience with pure copper ore, the scrap or oxidation product of the copper metal, as the possible colorant. Those beads were possibly imported from Egypt or somewhere impacted by the Egyptian faience production technology. This implies that the ethnic groups on the plateau or other regions already crossed the Himalayas and integrated into the treasure exchange network of Eurasia, making the decorative luxury commodities penetrate to the Tibetan Plateau in 3500 years ago.
Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). ...Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.
PDF
