Computer Numeric Control (CNC) five-axis milling plays a significant role in the machining of precision molds and dies, aerospace parts, consumer electronics, etc. This research aims to explore the potential of the machine learning (ML) technique in improving energy efficiency during the CNC five-axis milling process for sustainable manufacturing. The experiments with various machining parameters, forms of toolpath planning, and dry cutting conditions were carried out, and the data regarding energy consumption were collected simultaneously. The relationship between machine parameters and energy consumption was analyzed and built. Subsequently, a machine learning algorithm was developed to classify test methods and identify energy-efficient machining strategies. The developed algorithm was implemented and assessed using different classification methods based on the ML concept to effectively reduce energy consumption. The results show that the Decision Tree and Random Forest algorithms produced lower Root Mean Square Error (RMSE) values of 4.24 and 4.28, respectively, compared to Linear, Lasso, and Ridge Regression algorithms. Verification experiments were conducted to ascertain the real-world applicability and performance of the ML-based energy efficiency approach in an operational CNC five-axis milling machine. The findings not only underscore the potential of ML techniques in optimizing energy efficiency but also offer a compelling pathway towards enhanced sustainability in CNC machining operations. The developed algorithm was implemented within a simulation framework and the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of the machining results and integrating both conventional and advanced regression algorithms into CNC machining processes. Manufacturers stand to realize substantial energy savings and bolster sustainability initiatives, thus exemplifying the transformative power of ML-driven optimization strategies.

Pemanfaatan Pembangkit Listrik Tenaga Surya (PLTS) Off-Grid memerlukan baterai sebagai media penyimpanan energi yang dihasilkan dari modul photovoltaic (PV). Baterai perlu mendapat perhatian khusus karena kerusakannya disebabkan penggunaan yang tidak ideal dan baterai tidak dilengkapi sistem proteksi dan monitoring, yang dapat mengurangi performa dari baterai. Pada Skripsi ini dilakukan monitoring 2 jenis baterai sel sekunder yaitu Lead Acid 12 V dan Lithium 12 V untuk sistem PLTS off grid 100 Wp menggunakan metode kuantitatif, yaitu pengukuran SOC (State Of Charge) dengan metode modified Coulomb Counting. Sensor tegangan, sensor arus ACS712 digunakan untuk mengirimkan informasi mengenai kondisi baterai ke mikrokontroler Arduino uno R3 sebagai pengolah data. Pengukuran SOC dengan metode ini dilakukan dengan menjumlahkan muatan yang masuk atau muatan yang keluar dari baterai dalam kurun waktu tertentu. Perhitungan coulomb inilah yang nantinya didapatkan nilai SOC baterai. Dari pengujian yang telah dilakukan, alat mampu membaca nilai tegangan, arus, SOC dengan baik. Hasil pembacaan parameter ditampilkan pada LCD. Sistem proteksi akan aktif ketika baterai dalam kondisi tidak ideal, yaitu ketika SOC baterai bernilai 100% pada saat proses charging dan 20 persen saat proses discharging, sehingga baterai tidak mudah rusak.

Industry 4.0 brings advanced technologies and automation, enabling greater productivity and efficiency. However, it also increases energy consumption and carbon emissions. Sustainable manufacturing in the era of Industry 4.0 is vital for addressing the environmental impact by minimizing resource consumption, optimizing energy efficiency, and adopting eco-friendly practices. CNC (Computer Numeric Control) 5-axis milling plays a significant role in the machining of precision molds and dies, aerospace, consumer electronics, etc. This research aims to explore the potential of the artificial intelligence (AI) technique in improving energy efficiency on CNC 5-axis milling process for sustainable manufacturing. The experiments were carried out using a CNC 5-axis milling machine with various machining parameters, toolpath planning, and dry cutting condition. Data on energy consumption of CNC 5-axis milling machine were collected, then the relationship between the machining parameters, toolpath planning, and energy consumption was analyzed and built. Furthermore, an AI-based optimization algorithm was developed that uses machine learning and optimization techniques to identify energy-efficient machining strategies. The developed algorithm was implemented within a simulation framework, the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of machining result. Finally, verification experiments were carried out to evaluate the applicability and performance of the AI-based energy optimization approach on a CNC 5-axis milling machine. By integrating AI into the CNC machining process, manufacturers can achieve significant energy savings, leading to reduced carbon emissions and improved sustainability.

Pada jurnal yang kami buat saat ini memiliki tujuan untuk mendesain sebuah rangkaian Variable Frequency Drive (VFD) satu phasa, yang mampu menghasilkan keluaran tegangan AC dan frekuensi yang dapat diubah-ubah dengan masukan dari sumber tegangan AC 220V 50Hz. Rangkaian VFD merupakan implementasi dari sebuah rangkaian Inverter satu phasa, diode Full-bridge rectifier digunakan untuk mengubah tegangan AC menjadi tegangan DC yang selanjutnya tegangan DC akan dirubah kembali menjadi tegangan AC dengan frekuensi yang dapat diubah-ubah yaitu menggunakan rangkaian Full-bridge Inverter. Metode Unipolar Sinusoida Pulse Width Modulation (SPWM) dipakai untuk mengontrol tegangan dan frekuensi keluaran dan juga menghasilkan tegangan keluaran yang mendekati gelombang sinus. Untuk menghasilkan SPWM dipakai mikrokontroler ATMega8535. Sebagai aplikasi rangakaian VFD ini akan diaplikasikan untuk mengatur kecepatan motor AC jenis Motor kapasitor. Pembangkit SPWM menggunakan frekuensi sinyal carrier 1Khz, dengan index modulasi 0,9 . frekuensi keluaran yang dapat diatur antara rentang frekuensi 8 Hz sampai 80 Hz.